IPL Analytics Platform — complete with 2025/2026 data

.gitattributes

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+data/processed/deliveries_clean.csv filter=lfs diff=lfs merge=lfs -text
+data/processed/ipl.db filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Raw data — too large for GitHub
+data/raw/
+
+# Virtual environment
+venv/
+.venv/
+
+# Python cache
+__pycache__/
+*.pyc
+*.pyo
+.ipynb_checkpoints/
+
+# VS Code
+.vscode/
+
+# Jupyter
+.ipynb_checkpoints/
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Keep processed data and models — needed for deployment
+!data/processed/
+!data/processed/models/

dashboard/app.py

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+# dashboard/app.py
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).parent.parent
+DASHBOARD = Path(__file__).parent
+sys.path.insert(0, str(ROOT))
+sys.path.insert(0, str(DASHBOARD))
+
+import dash
+from dash import Input, Output, html, dcc
+import dash_bootstrap_components as dbc
+
+from layout    import (make_navbar,
+                       layout_team_stats,
+                       layout_player_explorer,
+                       layout_win_predictor,
+                       layout_auction_simulator)
+from callbacks import register_callbacks
+
+# ── Initialise app ────────────────────────────────────────────────────
+app = dash.Dash(
+    __name__,
+    external_stylesheets=[dbc.themes.BOOTSTRAP],
+    suppress_callback_exceptions=True,
+    meta_tags=[{"name": "viewport",
+                "content": "width=device-width, initial-scale=1"}],
+)
+app.title  = "IPL Analytics Platform"
+server     = app.server
+
+# ── Root layout ───────────────────────────────────────────────────────
+# dcc.Location MUST be first child — it triggers the routing callback
+app.layout = html.Div([
+    dcc.Location(id="url", refresh=False),
+    make_navbar(),
+    html.Div(id="page-content"),
+])
+
+# ── Page routing callback ─────────────────────────────────────────────
+@app.callback(
+    Output("page-content", "children"),
+    Input("url", "pathname"),
+)
+def render_page(pathname):
+    print(f"render_page called with pathname: {pathname}")
+    if pathname == "/players":
+        return layout_player_explorer()
+    if pathname == "/predict":
+        return layout_win_predictor()
+    if pathname == "/auction":
+        return layout_auction_simulator()
+    return layout_team_stats()
+
+register_callbacks(app)
+
+if __name__ == "__main__":
+    app.run(debug=True, port=8050)

dashboard/assets/style.css

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+body {
+    font-family: "Segoe UI", Arial, sans-serif;
+    background-color: #f8f8f8;
+}
+
+.metric-card {
+    background: white;
+    border-radius: 10px;
+    padding: 18px 20px;
+    border: 0.5px solid #e0e0e0;
+    text-align: center;
+}
+
+.metric-value {
+    font-size: 28px;
+    font-weight: 600;
+    color: #1a1a1a;
+    margin: 0;
+}
+
+.metric-label {
+    font-size: 12px;
+    color: #888;
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+    margin: 4px 0 0 0;
+}
+
+.section-title {
+    font-size: 16px;
+    font-weight: 600;
+    color: #222;
+    margin: 24px 0 12px 0;
+    padding-bottom: 6px;
+    border-bottom: 2px solid #7F77DD;
+    display: inline-block;
+}
+
+.insight-box {
+    background: #E1F5EE;
+    border-left: 3px solid #1D9E75;
+    border-radius: 0 8px 8px 0;
+    padding: 10px 16px;
+    font-size: 13px;
+    color: #085041;
+    margin: 12px 0 20px 0;
+    line-height: 1.6;
+}
+
+.page-header {
+    background: white;
+    border-bottom: 0.5px solid #e8e8e8;
+    padding: 16px 28px;
+    margin-bottom: 24px;
+}
+
+.page-title {
+    font-size: 22px;
+    font-weight: 600;
+    color: #1a1a1a;
+    margin: 0;
+}
+
+.page-subtitle {
+    font-size: 13px;
+    color: #888;
+    margin: 3px 0 0 0;
+}
+
+.prediction-result {
+    background: white;
+    border-radius: 12px;
+    padding: 24px;
+    border: 0.5px solid #e0e0e0;
+    text-align: center;
+    margin-top: 16px;
+}
+
+.win-team {
+    font-size: 26px;
+    font-weight: 700;
+    color: #085041;
+}
+
+.win-prob {
+    font-size: 44px;
+    font-weight: 700;
+    color: #1D9E75;
+}

dashboard/callbacks.py

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+from dash import Input, Output, State, callback, no_update, dash_table
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from dash import html
+import dash_bootstrap_components as dbc
+from pathlib import Path
+import sys
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from src.models import predict_winner, predict_auction_value
+
+PROCESSED = Path("E:/ipl-analytics/data/processed")
+MODELS    = Path("E:/ipl-analytics/data/processed/models")
+
+matches    = pd.read_csv(PROCESSED / "matches_clean.csv",  parse_dates=["date"])
+batting    = pd.read_csv(PROCESSED / "batting_features.csv")
+bowling    = pd.read_csv(PROCESSED / "bowling_features.csv")
+deliveries = pd.read_csv(PROCESSED / "deliveries_clean.csv", low_memory=False)
+valuations = pd.read_csv(MODELS    / "player_valuations.csv")
+summary = pd.read_csv(PROCESSED / "match_summary.csv", parse_dates=["date"])
+print("batting_first_won in summary:", "batting_first_won" in summary.columns)
+print("summary shape:", summary.shape)
+
+TEAM_COLORS = {
+    "Mumbai Indians"             : "#004BA0",
+    "Chennai Super Kings"        : "#F9CD05",
+    "Royal Challengers Bangalore": "#EC1C24",
+    "Kolkata Knight Riders"      : "#3A225D",
+    "Sunrisers Hyderabad"        : "#F7A721",
+    "Delhi Capitals"             : "#0078BC",
+    "Punjab Kings"               : "#ED1B24",
+    "Rajasthan Royals"           : "#254AA5",
+}
+DEFAULT_C = "#7F77DD"
+
+def tc(team): return TEAM_COLORS.get(team, DEFAULT_C)
+
+CHART_LAYOUT = dict(
+    plot_bgcolor="white",
+    paper_bgcolor="white",
+    font_family="Segoe UI, Arial",
+    margin=dict(t=40, b=40, l=40, r=20),
+)
+
+
+
+def register_callbacks(app):
+
+    @app.callback(
+        Output("team-metric-cards",   "children"),
+        Output("wins-per-season-chart","figure"),
+        Output("win-pct-chart",        "figure"),
+        Output("toss-chart",           "figure"),
+        Output("runs-per-over-chart",  "figure"),
+        Input("team-filter",   "value"),
+        Input("season-range",  "value"),
+    )
+    def update_team_stats(selected_teams, season_range):
+        
+        print(f"update_team_stats called")
+        print(f"  selected_teams : {selected_teams}")
+        print(f"  season_range   : {season_range}")
+        print(f"  matches shape  : {matches.shape}")
+        
+        if not selected_teams:
+            selected_teams = ["Mumbai Indians", "Chennai Super Kings"]
+
+        s_min, s_max = season_range
+        df = matches[
+            (matches["season"] >= s_min) &
+            (matches["season"] <= s_max)
+        ].copy()
+
+        # ── Metric cards ──────────────────────────────────────────
+        total_matches = len(df)
+        total_seasons = df["season"].nunique()
+        total_sixes   = int(deliveries[
+            deliveries["match_id"].isin(df["match_id"])
+        ]["is_six"].sum())
+        avg_score = deliveries[
+            deliveries["match_id"].isin(df["match_id"])
+        ].groupby("match_id")["total_runs"].sum().mean()
+
+        def metric_card(value, label):
+            return dbc.Col(html.Div([
+                html.P(str(value), className="metric-value"),
+                html.P(label,      className="metric-label"),
+            ], className="metric-card"), xs=6, md=3)
+
+        cards = [
+            metric_card(total_matches,        "total matches"),
+            metric_card(total_seasons,         "seasons"),
+            metric_card(f"{total_sixes:,}",    "total sixes"),
+            metric_card(f"{avg_score:.0f}",    "avg match score"),
+        ]
+
+        # ── Wins per season ───────────────────────────────────────
+        wins = (
+            df[df["winner"].isin(selected_teams)]
+            .groupby(["season", "winner"])
+            .size()
+            .reset_index(name="wins")
+            .rename(columns={"winner": "team"})
+        )
+        fig_wins = px.bar(
+            wins, x="season", y="wins", color="team",
+            barmode="group",
+            color_discrete_map={t: tc(t) for t in wins["team"].unique()},
+            labels={"wins": "wins", "season": "season"},
+        )
+        fig_wins.update_layout(**CHART_LAYOUT)
+        fig_wins.update_layout(
+            xaxis=dict(tickmode="linear", dtick=1, tickangle=-45),
+            legend=dict(orientation="h", y=-0.25, font_size=11),
+        )
+        fig_wins.update_traces(marker_line_width=0)
+
+        # ── Win percentage ────────────────────────────────────────
+        records = []
+        for team in selected_teams:
+            played = df[(df["team1"] == team) | (df["team2"] == team)]
+            won    = df[df["winner"] == team]
+            if len(played) > 0:
+                records.append({
+                    "team"   : team,
+                    "win_pct": round(len(won) / len(played) * 100, 1),
+                    "played" : len(played),
+                })
+        wp_df  = pd.DataFrame(records).sort_values("win_pct", ascending=True)
+        fig_wp = px.bar(
+            wp_df, x="win_pct", y="team", orientation="h",
+            color="team",
+            color_discrete_map={t: tc(t) for t in wp_df["team"].unique()},
+            labels={"win_pct": "win %", "team": ""},
+            text="win_pct",
+        )
+        fig_wp.update_traces(texttemplate="%{text:.1f}%", textposition="outside",
+                              marker_line_width=0)
+        fig_wp.update_layout(**CHART_LAYOUT)
+        fig_wp.update_layout(showlegend=False, xaxis=dict(range=[0, 100]))
+
+        # ── Toss decisions ────────────────────────────────────────
+        toss_df = (
+            df[df["toss_winner"].isin(selected_teams)]
+            .groupby(["toss_winner", "toss_decision"])
+            .size()
+            .reset_index(name="count")
+            .rename(columns={"toss_winner": "team"})
+        )
+        fig_toss = px.bar(
+            toss_df, x="team", y="count", color="toss_decision",
+            barmode="group",
+            color_discrete_map={"bat": "#7F77DD", "field": "#1D9E75"},
+            labels={"count": "times chosen", "toss_decision": "decision"},
+        )
+        fig_toss.update_layout(**CHART_LAYOUT)
+        fig_toss.update_layout(
+            xaxis_tickangle=-30,
+            legend=dict(orientation="h", y=-0.3, font_size=11),
+        )
+        fig_toss.update_traces(marker_line_width=0)
+
+        # ── Runs per over ─────────────────────────────────────────
+        over_avg = (
+            deliveries[deliveries["match_id"].isin(df["match_id"])]
+            .groupby("over")["total_runs"]
+            .mean()
+            .round(2)
+            .reset_index()
+        )
+        PHASE_BG = [
+            dict(type="rect", xref="x", yref="paper",
+                 x0=0.5, x1=6.5, y0=0, y1=1,
+                 fillcolor="#EEEDFE", opacity=0.3, layer="below", line_width=0),
+            dict(type="rect", xref="x", yref="paper",
+                 x0=6.5, x1=15.5, y0=0, y1=1,
+                 fillcolor="#E1F5EE", opacity=0.3, layer="below", line_width=0),
+            dict(type="rect", xref="x", yref="paper",
+                 x0=15.5, x1=20.5, y0=0, y1=1,
+                 fillcolor="#FAECE7", opacity=0.3, layer="below", line_width=0),
+        ]
+        fig_rpo = go.Figure()
+        fig_rpo.add_trace(go.Bar(
+            x=over_avg["over"], y=over_avg["total_runs"],
+            marker_color="#B5D4F4", name="avg runs",
+        ))
+        for label, xpos in [("Powerplay", 3.5), ("Middle", 11), ("Death", 18)]:
+            fig_rpo.add_annotation(
+                x=xpos, y=over_avg["total_runs"].max() * 1.08,
+                text=label, showarrow=False,
+                font=dict(size=10, color="#444"),
+            )
+        fig_rpo.update_layout(
+            **CHART_LAYOUT,
+            shapes=PHASE_BG,
+            xaxis=dict(title="over", tickmode="linear", dtick=1),
+            yaxis_title="avg runs/over",
+            showlegend=False,
+        )
+
+        return cards, fig_wins, fig_wp, fig_toss, fig_rpo
+
+
+    # ════════════════════════════════════════════════════════════════
+    # PAGE 2 CALLBACKS — PLAYER EXPLORER
+    # ════════════════════════════════════════════════════════════════
+
+    @app.callback(
+        Output("batting-metric-cards", "children"),
+        Output("batting-scatter-chart","figure"),
+        Output("phase-sr-chart",        "figure"),
+        Input("batsman-search",  "value"),
+        Input("batsman-compare", "value"),
+    )
+    def update_batting(player1, player2):
+        players = [p for p in [player1, player2] if p]
+
+        # ── Metric cards for primary player ───────────────────────
+        p1 = batting[batting["batter"] == player1].iloc[0]
+
+        def bat_card(value, label):
+            return dbc.Col(html.Div([
+                html.P(str(value), className="metric-value"),
+                html.P(label,      className="metric-label"),
+            ], className="metric-card"), xs=6, md=2)
+
+        cards = [
+            bat_card(f"{p1['total_runs']:,.0f}",    "career runs"),
+            bat_card(f"{p1['batting_average']:.1f}","average"),
+            bat_card(f"{p1['strike_rate']:.1f}",    "strike rate"),
+            bat_card(int(p1["hundreds"]),             "100s"),
+            bat_card(int(p1["fifties"]),              "50s"),
+            bat_card(f"{p1['boundary_rate']:.1f}%",  "boundary %"),
+        ]
+
+        # ── Batting scatter: all players, highlight selected ──────
+        plot_df = batting[batting["balls_faced"] >= 200].copy()
+        plot_df["highlight"] = plot_df["batter"].apply(
+            lambda x: x if x in players else "others"
+        )
+        plot_df = plot_df.sort_values(
+            "highlight",
+            key=lambda s: s.map({"others": 0}).fillna(1)
+        )
+        color_map = {"others": "#D3D1C7"}
+        colors    = ["#7F77DD", "#D85A30"]
+        for i, p in enumerate(players):
+            color_map[p] = colors[i % len(colors)]
+
+        fig_scatter = px.scatter(
+            plot_df,
+            x="batting_average", y="strike_rate",
+            color="highlight",
+            color_discrete_map=color_map,
+            size="total_runs", size_max=22,
+            hover_name="batter",
+            hover_data={"total_runs": True, "highlight": False},
+            labels={
+                "batting_average": "batting average",
+                "strike_rate"    : "strike rate",
+                "highlight"      : "player",
+            },
+        )
+        fig_scatter.update_layout(**CHART_LAYOUT)
+        fig_scatter.update_layout(
+            legend=dict(orientation="h", y=-0.2, font_size=11)
+        )
+        fig_scatter.update_traces(marker_line_width=0.5,
+                                   marker_line_color="white")
+
+        # ── Phase SR comparison ────────────────────────────────────
+        phase_data = []
+        for p in players:
+            row = batting[batting["batter"] == p]
+            if row.empty:
+                continue
+            row = row.iloc[0]
+            for phase in ["powerplay", "middle", "death"]:
+                col = f"sr_{phase}"
+                if col in row and pd.notna(row[col]):
+                    phase_data.append({
+                        "player": p,
+                        "phase" : phase.capitalize(),
+                        "SR"    : round(row[col], 1),
+                    })
+
+        if phase_data:
+            ph_df   = pd.DataFrame(phase_data)
+            fig_phase = px.bar(
+                ph_df, x="phase", y="SR", color="player",
+                barmode="group",
+                color_discrete_map={players[0]: "#7F77DD",
+                                    players[1] if len(players) > 1 else "": "#D85A30"},
+                labels={"SR": "strike rate", "phase": "match phase"},
+                text="SR",
+            )
+            fig_phase.update_traces(texttemplate="%{text:.1f}", textposition="outside",
+                                     marker_line_width=0)
+            fig_phase.update_layout(**CHART_LAYOUT)
+            fig_phase.update_layout(
+                legend=dict(orientation="h", y=-0.2, font_size=11)
+            )
+        else:
+            fig_phase = go.Figure()
+            fig_phase.update_layout(**CHART_LAYOUT)
+
+        return cards, fig_scatter, fig_phase
+
+
+    @app.callback(
+        Output("bowling-metric-cards","children"),
+        Output("bowling-phase-chart", "figure"),
+        Input("bowler-search", "value"),
+    )
+    def update_bowling(player):
+        row = bowling[bowling["bowler"] == player]
+        if row.empty:
+            return [], go.Figure()
+        row = row.iloc[0]
+
+        def bowl_card(value, label):
+            return dbc.Col(html.Div([
+                html.P(str(value), className="metric-value"),
+                html.P(label,      className="metric-label"),
+            ], className="metric-card"), xs=6, md=2)
+
+        cards = [
+            bowl_card(int(row["wickets"]),                "career wickets"),
+            bowl_card(f"{row['economy_rate']:.2f}",       "economy rate"),
+            bowl_card(f"{row['bowling_average']:.1f}",    "bowling avg"),
+            bowl_card(f"{row['bowling_sr']:.1f}",         "bowling SR"),
+            bowl_card(f"{row['dot_ball_pct']:.1f}%",      "dot ball %"),
+            bowl_card(int(row["three_wkt_hauls"]) if "three_wkt_hauls" in row else "—",
+                      "3-wkt hauls"),
+        ]
+
+        phases = ["powerplay", "middle", "death"]
+        econ_vals = [row.get(f"economy_{p}", np.nan) for p in phases]
+
+        fig = go.Figure(go.Bar(
+            x=[p.capitalize() for p in phases],
+            y=[round(e, 2) if pd.notna(e) else 0 for e in econ_vals],
+            marker_color=["#7F77DD", "#1D9E75", "#D85A30"],
+            text=[f"{e:.2f}" if pd.notna(e) else "N/A" for e in econ_vals],
+            textposition="outside",
+        ))
+        fig.add_hline(y=8.0, line_dash="dot", line_color="#888",
+                      annotation_text="8.0 benchmark")
+        fig.update_layout(
+            **CHART_LAYOUT,
+            title=f"{player} — economy by phase",
+            yaxis_title="economy rate",
+            xaxis_title="match phase",
+            showlegend=False,
+        )
+
+        return cards, fig
+
+
+    @app.callback(
+        Output("player-table", "data"),
+        Output("player-table", "columns"),
+        Input("leaderboard-type", "value"),
+    )
+    def update_player_table(table_type):
+        if table_type == "batting":
+            cols = ["batter", "total_runs", "batting_average",
+                    "strike_rate", "hundreds", "fifties",
+                    "boundary_rate", "matches_batted"]
+            df = batting[cols].copy().rename(columns={"batter": "player"})
+            df = df.sort_values("total_runs", ascending=False).round(2)
+        else:
+            cols = ["bowler", "wickets", "economy_rate",
+                    "bowling_average", "bowling_sr",
+                    "dot_ball_pct", "matches_bowled"]
+            df = bowling[cols].copy().rename(columns={"bowler": "player"})
+            df = df.sort_values("wickets", ascending=False).round(2)
+
+        columns = [{"name": c.replace("_", " ").title(),
+                    "id": c, "type": "numeric",
+                    "format": {"specifier": ",.2f"}}
+                   for c in df.columns]
+        return df.to_dict("records"), columns
+
+
+    # ════════════════════════════════════════════════════════════════
+    # PAGE 3 CALLBACKS — WIN PREDICTOR
+    # ════════════════════════════════════════════════════════════════
+
+    @app.callback(
+        Output("prediction-output", "children"),
+        Output("win-prob-gauge",    "figure"),
+        Output("historical-h2h",    "children"),
+        Input("predict-btn",        "n_clicks"),
+        State("pred-team1",         "value"),
+        State("pred-team2",         "value"),
+        State("pred-venue",         "value"),
+        State("pred-toss-winner",   "value"),
+        State("pred-toss-decision", "value"),
+        prevent_initial_call=True,
+    )
+    def run_prediction(n, team1, team2, venue, toss_winner, toss_decision):
+        result = predict_winner(
+            team1=team1, team2=team2, venue=venue,
+            toss_winner=toss_winner, toss_decision=toss_decision,
+        )
+
+        team_A  = result["toss_winner"]       
+        team_B  = result["other_team"]       
+        p_toss  = result["toss_winner_prob"] 
+        p_other = result["other_team_prob"]   
+        winner  = result["predicted_winner"]
+
+        # ── Result card ───────────────────────────────────────────
+        result_card = html.Div([
+            html.P("Predicted winner", style={"color": "#888", "fontSize": "13px",
+                                               "marginBottom": "4px"}),
+            html.P(winner, className="win-team"),
+            html.Div([
+                html.Span(f"{team_A}: ", style={"fontWeight": 600}),
+                html.Span(f"{p_toss}%",
+                           style={"color": tc(team1), "fontWeight": 700,
+                                  "fontSize": "18px"}),
+                html.Span("   vs   ", style={"color": "#ccc"}),
+                html.Span(f"{team_B}: ", style={"fontWeight": 600}),
+                html.Span(f"{p_other}%",
+                           style={"color": tc(team_B), "fontWeight": 700,
+                                  "fontSize": "18px"}),
+            ], style={"marginTop": "8px"}),
+        ], className="prediction-result")
+
+        # ── Gauge chart ───────────────────────────────────────────
+        fig_gauge = go.Figure(go.Indicator(
+            mode="gauge+number",
+            value=p_toss,
+            number={"suffix": "%", "font": {"size": 36, "color": tc(team_A)}},
+            title={"text": f"{team_A} win probability", "font": {"size": 13}},
+            gauge={
+                "axis"      : {"range": [0, 100]},
+                "bar"       : {"color": tc(team_A)},
+                "steps"     : [
+                    {"range": [0,  40], "color": "#FCEBEB"},
+                    {"range": [40, 60], "color": "#F1EFE8"},
+                    {"range": [60, 100],"color": "#E1F5EE"},
+                ],
+                "threshold" : {
+                    "line" : {"color": "#1D9E75", "width": 3},
+                    "thickness": 0.75,
+                    "value": 50,
+                },
+            },
+        ))
+        fig_gauge.update_layout(
+            paper_bgcolor="white",
+            font_family="Segoe UI, Arial",
+            margin=dict(t=60, b=20, l=30, r=30),
+        )
+
+        # ── Historical H2H ────────────────────────────────────────
+        h2h = matches[
+            ((matches["team1"] == team_A) & (matches["team2"] == team_B)) |
+            ((matches["team1"] == team_B) & (matches["team2"] == team_A))
+        ]
+        h2h = h2h[h2h["winner"].isin([team_A, team_B])]
+        tA_wins = int((h2h["winner"] == team_A).sum())
+        tB_wins = int((h2h["winner"] == team_B).sum())
+
+        h2h_card = dbc.Card(dbc.CardBody([
+            html.H6("Historical head-to-head",
+                    style={"fontWeight": 600, "marginBottom": "12px"}),
+            html.Div([
+                html.Div([
+                    html.P(str(tA_wins), style={"fontSize": "32px",
+                                                 "fontWeight": 700,
+                                                 "color": tc(team_A),
+                                                 "margin": 0}),
+                    html.P(team_A, style={"fontSize": "12px", "color": "#888"}),
+                ], style={"textAlign": "center", "flex": 1}),
+                html.Div([
+                    html.P(f"{len(h2h)} played",
+                           style={"fontSize": "13px", "color": "#aaa",
+                                  "margin": 0}),
+                ], style={"textAlign": "center", "flex": 1,
+                           "display": "flex", "alignItems": "center",
+                           "justifyContent": "center"}),
+                html.Div([
+                    html.P(str(tB_wins), style={"fontSize": "32px",
+                                                 "fontWeight": 700,
+                                                 "color": tc(team_B),
+                                                 "margin": 0}),
+                    html.P(team_B, style={"fontSize": "12px", "color": "#888"}),
+                ], style={"textAlign": "center", "flex": 1}),
+            ], style={"display": "flex", "marginTop": "8px"}),
+        ]), style={"border": "0.5px solid #e0e0e0", "marginTop": "16px"})
+        
+        venue_h2h = matches[
+            (
+            ((matches["team1"] == team_A) & (matches["team2"] == team_B)) |
+            ((matches["team1"] == team_B) & (matches["team2"] == team_A))
+            ) & 
+            (matches["venue"] == venue)
+        ]
+        venue_h2h = venue_h2h[venue_h2h["winner"].isin([team_A, team_B])]
+        vA_wins = int((venue_h2h["winner"] == team_A).sum())
+        vB_wins = int((venue_h2h["winner"] == team_B).sum())
+        total_venue = len(venue_h2h)
+        
+        venue_card = dbc.Card(dbc.CardBody([
+            html.H6(f"Head-to-Head at {venue.split(',')[0]}",
+                    style={"fontWeight": 600, "marginBottom": "12px"}),
+            html.Div([
+                html.Div([
+                    html.P(str(vA_wins),
+                           style={"fontSize": "28px", "fontWeight": 700,
+                                  "color": tc(team_A), "margin": 0}),
+                    html.P(team_A, style={"fontSize": "11px", "color": "#888" }),
+                ], style={"textAlign": "center", "flex": 1}),
+                html.Div([
+                    html.P(f"{total_venue} played" if total_venue > 0
+                           else "No matches\nat this value",
+                           style={"fontSize": "12px", "color": "#aaa",
+                                  "margin": 0, "textAlign": "center"}),
+                ], style={"flex": 1, "display": "flex",
+                          "alignItems": "center", "justifyContent": "center"}),
+                html.Div([
+                    html.P(str(vB_wins),
+                           style={"fontSize": "28px", "fontWeight": 700,
+                                  "color": tc(team_B), "margin": 0}),
+                    html.P(team_B, style={"fontSize": "11px", "color": "#888"}),
+                ], style={"textAlign": "center", "flex": 1}),
+            ], style={"display": "flex"}),
+            html.P(
+                    "No historical data at this venue - result based on overall form only."
+                    if total_venue == 0 else
+                    f"Sample size: {total_venue} matches. "
+                    + ("Reliable Signal." if total_venue >= 10 else "Small sample - treat with caution"),
+                    style={"fontSize": "11px", "color": "#aaa",
+                           "marginTop": "8px", "marginBottom": 0}
+            ),
+        ]), style={"border": "0.5px solid #e0e0e0", "marginTop": "12px"})
+
+        return result_card, fig_gauge, html.Div([h2h_card, venue_card])
+
+
+    # ════════════════════════════════════════════════════════════════
+    # PAGE 4 CALLBACKS — AUCTION SIMULATOR
+    # ════════════════════════════════════════════════════════════════
+
+    # Slider output labels
+    SLIDER_LABEL_MAP = {
+        "sl-runs"    : "sl-runs-out",     "sl-avg"     : "sl-avg-out",
+        "sl-sr"      : "sl-sr-out",       "sl-100s"    : "sl-100s-out",
+        "sl-50s"     : "sl-50s-out",      "sl-br"      : "sl-br-out",
+        "sl-pp-sr"   : "sl-pp-sr-out",    "sl-death-sr": "sl-death-sr-out",
+        "sl-wkts"    : "sl-wkts-out",     "sl-econ"    : "sl-econ-out",
+        "sl-bowl-avg": "sl-bowl-avg-out", "sl-dot"     : "sl-dot-out",
+        "sl-d-econ"  : "sl-d-econ-out",   "sl-pp-econ" : "sl-pp-econ-out",
+        "sl-mbowled" : "sl-mbowled-out",
+    }
+
+    @app.callback(
+        [Output(v, "children") for v in SLIDER_LABEL_MAP.values()] +
+        [
+            Output("auction-price-display",  "children"),
+            Output("auction-gauge",          "figure"),
+            Output("similar-players-table",  "children"),
+            Output("valuation-chart",        "figure"),
+        ],
+        [Input(k, "value") for k in SLIDER_LABEL_MAP.keys()],
+    )
+    def update_auction(runs, avg, sr, h100, h50, br, pp_sr, death_sr,
+                       wkts, econ, bowl_avg, dot, d_econ, pp_econ, mbowled):
+
+        labels = [runs, avg, sr, h100, h50, br, pp_sr, death_sr,
+                  wkts, econ, bowl_avg, dot, d_econ, pp_econ, mbowled]
+
+        stats = {
+            "total_runs"       : runs,      "batting_average"  : avg,
+            "strike_rate"      : sr,        "hundreds"         : h100,
+            "fifties"          : h50,        "boundary_rate"    : br,
+            "sr_powerplay"     : pp_sr,      "sr_death"         : death_sr,
+            "wickets"          : wkts,       "economy_rate"     : econ,
+            "bowling_average"  : bowl_avg,   "dot_ball_pct"     : dot,
+            "economy_death"    : d_econ,     "economy_powerplay": pp_econ,
+            "matches_bowled"   : mbowled,    "matches_batted"   : max(runs // 30, 1),
+            "bowling_sr"       : 30.0,
+        }
+        result = predict_auction_value(stats)
+        price  = result["predicted_price_cr"]
+        tier   = result["tier"]
+
+        tier_color = {
+            "Icon (12 cr+)"    : "#085041",
+            "Premium (7–12 cr)": "#1D9E75",
+            "Standard (3–7 cr)": "#7F77DD",
+            "Emerging (< 3 cr)": "#888780",
+        }.get(tier, "#888780")
+
+        price_display = html.Div([
+            html.P(f"₹ {price:.2f} Cr",
+                   style={"fontSize": "42px", "fontWeight": 700,
+                           "color": tier_color, "margin": 0}),
+            html.Span(tier, style={
+                "fontSize": "13px", "padding": "4px 14px",
+                "background": tier_color, "color": "white",
+                "borderRadius": "999px", "fontWeight": 500,
+            }),
+        ], style={"textAlign": "center", "padding": "16px 0"})
+
+        # ── Gauge ──────────────────────────────────────────────────
+        fig_g = go.Figure(go.Indicator(
+            mode="gauge+number",
+            value=min(price, 20),
+            number={"prefix": "₹ ", "suffix": " Cr",
+                    "font": {"size": 28, "color": tier_color}},
+            gauge={
+                "axis" : {"range": [0, 20],
+                           "tickvals": [0, 3, 7, 12, 20],
+                           "ticktext": ["0", "3cr", "7cr", "12cr", "20cr+"]},
+                "bar"  : {"color": tier_color},
+                "steps": [
+                    {"range": [0,  3],  "color": "#F1EFE8"},
+                    {"range": [3,  7],  "color": "#EEEDFE"},
+                    {"range": [7,  12], "color": "#E1F5EE"},
+                    {"range": [12, 20], "color": "#9FE1CB"},
+                ],
+            },
+        ))
+        fig_g.update_layout(
+            paper_bgcolor="white",
+            font_family="Segoe UI, Arial",
+            margin=dict(t=20, b=10, l=30, r=30),
+            height=220,
+        )
+
+        # ── Similar players ────────────────────────────────────────
+        sim = valuations.copy()
+        sim = sim[
+            (sim["predicted_price_cr"] >= price * 0.75) &
+            (sim["predicted_price_cr"] <= price * 1.25)
+        ].nlargest(5, "predicted_price_cr")[
+            ["player", "total_runs", "wickets", "predicted_price_cr"]
+        ].round(2)
+
+        sim_table = dash_table.DataTable(
+            data=sim.to_dict("records"),
+            columns=[
+                {"name": "Player",          "id": "player"},
+                {"name": "Runs",            "id": "total_runs"},
+                {"name": "Wickets",         "id": "wickets"},
+                {"name": "Est. Price (Cr)", "id": "predicted_price_cr"},
+            ],
+            style_header={"backgroundColor": "#1a1a2e", "color": "white",
+                           "fontSize": "11px", "fontWeight": 600},
+            style_cell={"fontSize": "12px", "padding": "6px 10px",
+                         "border": "0.5px solid #f0f0f0"},
+            style_data_conditional=[
+                {"if": {"row_index": "odd"}, "backgroundColor": "#fafafa"}
+            ],
+        )
+
+        # ── Valuation leaderboard chart ────────────────────────────
+        top_v = valuations.nlargest(25, "predicted_price_cr")[
+            ["player", "predicted_price_cr", "total_runs", "wickets"]
+        ].sort_values("predicted_price_cr")
+
+        fig_val = px.bar(
+            top_v, x="predicted_price_cr", y="player",
+            orientation="h",
+            color="predicted_price_cr",
+            color_continuous_scale=["#EEEDFE", "#7F77DD", "#3C3489"],
+            labels={"predicted_price_cr": "est. price (₹ Cr)", "player": ""},
+            text="predicted_price_cr",
+        )
+        fig_val.update_traces(texttemplate="₹%{text:.1f}Cr",
+                               textposition="outside")
+        fig_val.update_layout(
+            **CHART_LAYOUT,
+            coloraxis_showscale=False,
+            title="Top 25 most valuable IPL players (model estimate)",
+        )
+
+        return labels + [price_display, fig_g, sim_table, fig_val]

dashboard/layout.py

@@ -0,0 +1,416 @@
+from dash import dcc, html, dash_table
+import dash_bootstrap_components as dbc
+import pandas as pd
+import json
+from pathlib import Path
+import sys
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+
+from src.models import get_available_teams, get_available_venues
+
+
+MODELS    = Path("E:/ipl-analytics/data/processed/models")
+PROCESSED = Path("E:/ipl-analytics/data/processed")
+
+# ── Load static data for dropdowns ───────────────────────────────────
+team_season = pd.read_csv(PROCESSED / "team_season_stats.csv")
+matches     = pd.read_csv(PROCESSED / "matches_clean.csv")
+batting     = pd.read_csv(PROCESSED / "batting_features.csv")
+bowling     = pd.read_csv(PROCESSED / "bowling_features.csv")
+valuations  = pd.read_csv(MODELS    / "player_valuations.csv")
+
+ALL_VENUES  = get_available_venues()
+ALL_TEAMS   = get_available_teams()
+ALL_SEASONS = sorted(matches["season"].unique().tolist())
+
+
+TOP_TEAMS = [
+    "Mumbai Indians", "Chennai Super Kings",
+    "Royal Challengers Bangalore", "Kolkata Knight Riders",
+    "Sunrisers Hyderabad", "Delhi Capitals",
+    "Punjab Kings", "Rajasthan Royals",
+]
+
+# ════════════════════════════════════════════════════════════════════
+# NAVBAR
+# ════════════════════════════════════════════════════════════════════
+
+def make_navbar():
+    return dbc.Navbar(
+        dbc.Container([
+            dbc.NavbarBrand(
+                [html.Span("IPL", style={"color": "#F9CD05", "fontWeight": 700}),
+                 html.Span(" Analytics Platform", style={"color": "white"})],
+                style={"fontSize": "18px"},
+            ),
+            dbc.Nav([
+                dbc.NavItem(dbc.NavLink("Team Stats",       href="/",           active="exact")),
+                dbc.NavItem(dbc.NavLink("Player Explorer",  href="/players",    active="exact")),
+                dbc.NavItem(dbc.NavLink("Win Predictor",    href="/predict",    active="exact")),
+                dbc.NavItem(dbc.NavLink("Auction Simulator",href="/auction",    active="exact")),
+            ], navbar=True, className="ms-auto"),
+        ], fluid=True),
+        color="#1a1a2e",
+        dark=True,
+        sticky="top",
+        style={"marginBottom": "0px"},
+    )
+
+
+# ════════════════════════════════════════════════════════════════════
+# PAGE 1 — TEAM STATS
+# ════════════════════════════════════════════════════════════════════
+
+def layout_team_stats():
+    return html.Div([
+        html.Div([
+            html.H1("Team Statistics", className="page-title"),
+            html.P("Season-by-season performance, head-to-head records, and venue analysis.",
+                   className="page-subtitle"),
+        ], className="page-header"),
+
+        dbc.Container([
+            # ── Filters ───────────────────────────────────────────
+            dbc.Row([
+                dbc.Col([
+                    html.Label("Select teams", style={"fontWeight": 500, "fontSize": "13px"}),
+                    dcc.Dropdown(
+                        id="team-filter",
+                        options=[{"label": t, "value": t} for t in ALL_TEAMS],
+                        value=TOP_TEAMS[:6],
+                        multi=True,
+                        placeholder="Select teams...",
+                    ),
+                ], md=8),
+                dbc.Col([
+                    html.Label("Season range", style={"fontWeight": 500, "fontSize": "13px"}),
+                    dcc.RangeSlider(
+                        id="season-range",
+                        min=min(ALL_SEASONS), max=max(ALL_SEASONS),
+                        value=[min(ALL_SEASONS), max(ALL_SEASONS)],
+                        marks={s: str(s) for s in ALL_SEASONS[::2]},
+                        step=1,
+                        tooltip={"placement": "bottom", "always_visible": False},
+                    ),
+                ], md=4),
+            ], className="mb-4"),
+
+            # ── Metric cards ──────────────────────────────────────
+            dbc.Row(id="team-metric-cards", className="mb-4 g-3"),
+
+            # ── Charts ────────────────────────────────────────────
+            dbc.Row([
+                dbc.Col([
+                    html.P("Wins per season", className="section-title"),
+                    dcc.Graph(id="wins-per-season-chart", style={"height": "380px"}),
+                ], md=7),
+                dbc.Col([
+                    html.P("Win percentage by team", className="section-title"),
+                    dcc.Graph(id="win-pct-chart", style={"height": "380px"}),
+                ], md=5),
+            ], className="mb-4"),
+
+            dbc.Row([
+                dbc.Col([
+                    html.P("Toss decision trends", className="section-title"),
+                    dcc.Graph(id="toss-chart", style={"height": "340px"}),
+                ], md=6),
+                dbc.Col([
+                    html.P("Runs per over (all seasons)", className="section-title"),
+                    dcc.Graph(id="runs-per-over-chart", style={"height": "340px"}),
+                ], md=6),
+            ], className="mb-4"),
+
+        ], fluid=True),
+    ])
+
+
+
+
+def layout_player_explorer():
+    return html.Div([
+        html.Div([
+            html.H1("Player Explorer", className="page-title"),
+            html.P("Search and compare individual player career statistics.",
+                   className="page-subtitle"),
+        ], className="page-header"),
+
+        dbc.Container([
+            dbc.Row([
+                dbc.Col([
+                    html.Label("Search batsman", style={"fontWeight": 500, "fontSize": "13px"}),
+                    dcc.Dropdown(
+                        id="batsman-search",
+                        options=[{"label": p, "value": p}
+                                 for p in sorted(batting["batter"].unique())],
+                        value=batting.nlargest(1, "total_runs")["batter"].iloc[0],
+                        placeholder="Select a batsman...",
+                        clearable=False,
+                    ),
+                ], md=4),
+                dbc.Col([
+                    html.Label("Compare with (optional)", style={"fontWeight": 500, "fontSize": "13px"}),
+                    dcc.Dropdown(
+                        id="batsman-compare",
+                        options=[{"label": p, "value": p}
+                                 for p in sorted(batting["batter"].unique())],
+                        placeholder="Add a second player...",
+                    ),
+                ], md=4),
+                dbc.Col([
+                    html.Label("Search bowler", style={"fontWeight": 500, "fontSize": "13px"}),
+                    dcc.Dropdown(
+                        id="bowler-search",
+                        options=[{"label": p, "value": p}
+                                 for p in sorted(bowling["bowler"].unique())],
+                        value=bowling.nlargest(1, "wickets")["bowler"].iloc[0],
+                        placeholder="Select a bowler...",
+                        clearable=False,
+                    ),
+                ], md=4),
+            ], className="mb-4"),
+
+            # ── Batting stats ──────────────────────────────────────
+            html.P("Batting profile", className="section-title"),
+            dbc.Row(id="batting-metric-cards", className="mb-3 g-3"),
+            dbc.Row([
+                dbc.Col([
+                    dcc.Graph(id="batting-scatter-chart", style={"height": "400px"}),
+                ], md=7),
+                dbc.Col([
+                    dcc.Graph(id="phase-sr-chart", style={"height": "400px"}),
+                ], md=5),
+            ], className="mb-4"),
+
+            # ── Bowling stats ──────────────────────────────────────
+            html.P("Bowling profile", className="section-title"),
+            dbc.Row(id="bowling-metric-cards", className="mb-3 g-3"),
+            dcc.Graph(id="bowling-phase-chart", style={"height": "360px"}),
+
+            # ── Player table ───────────────────────────────────────
+            html.P("Full leaderboard", className="section-title"),
+            dbc.Row([
+                dbc.Col([
+                    dcc.RadioItems(
+                        id="leaderboard-type",
+                        options=[
+                            {"label": " Batting", "value": "batting"},
+                            {"label": " Bowling", "value": "bowling"},
+                        ],
+                        value="batting",
+                        inline=True,
+                        style={"fontSize": "13px", "marginBottom": "10px"},
+                    ),
+                ]),
+            ]),
+            dash_table.DataTable(
+                id="player-table",
+                page_size=15,
+                sort_action="native",
+                filter_action="native",
+                style_table={"overflowX": "auto"},
+                style_header={
+                    "backgroundColor": "#1a1a2e",
+                    "color": "white",
+                    "fontWeight": 600,
+                    "fontSize": "12px",
+                    "border": "none",
+                },
+                style_cell={
+                    "fontSize": "12px",
+                    "padding": "8px 12px",
+                    "border": "0.5px solid #f0f0f0",
+                    "fontFamily": "Segoe UI, Arial",
+                },
+                style_data_conditional=[
+                    {"if": {"row_index": "odd"},
+                     "backgroundColor": "#fafafa"},
+                ],
+            ),
+        ], fluid=True),
+    ])
+
+
+# ════════════════════════════════════════════════════════════════════
+# PAGE 3 — WIN PREDICTOR
+# ════════════════════════════════════════════════════════════════════
+
+def layout_win_predictor():
+    return html.Div([
+        html.Div([
+            html.H1("Win Probability Predictor", className="page-title"),
+            html.P("Pre-match win probability powered by XGBoost — trained on 15 seasons of IPL data.",
+                   className="page-subtitle"),
+        ], className="page-header"),
+
+        dbc.Container([
+            dbc.Row([
+                # ── Input form ────────────────────────────────────
+                dbc.Col([
+                    dbc.Card([
+                        dbc.CardBody([
+                            html.H5("Match setup", style={"fontWeight": 600, "marginBottom": "20px"}),
+
+                            html.Label("Team 1 (batting side)", style={"fontSize": "13px", "fontWeight": 500}),
+                            dcc.Dropdown(
+                                id="pred-team1",
+                                options=[{"label": t, "value": t} for t in sorted(ALL_TEAMS)],
+                                value="Mumbai Indians",
+                                clearable=False,
+                                style={"marginBottom": "14px"},
+                            ),
+
+                            html.Label("Team 2", style={"fontSize": "13px", "fontWeight": 500}),
+                            dcc.Dropdown(
+                                id="pred-team2",
+                                options=[{"label": t, "value": t} for t in sorted(ALL_TEAMS)],
+                                value="Chennai Super Kings",
+                                clearable=False,
+                                style={"marginBottom": "14px"},
+                            ),
+
+                            html.Label("Venue", style={"fontSize": "13px", "fontWeight": 500}),
+                            dcc.Dropdown(
+                                id="pred-venue",
+                                options=[{"label": v, "value": v} for v in ALL_VENUES],
+                                value=ALL_VENUES[0] if ALL_VENUES else None,
+                                clearable=False,
+                                style={"marginBottom": "14px"},
+                            ),
+
+                            html.Label("Toss winner", style={"fontSize": "13px", "fontWeight": 500}),
+                            dcc.Dropdown(
+                                id="pred-toss-winner",
+                                options=[{"label": t, "value": t} for t in sorted(ALL_TEAMS)],
+                                value="Mumbai Indians",
+                                clearable=False,
+                                style={"marginBottom": "14px"},
+                            ),
+
+                            html.Label("Toss decision", style={"fontSize": "13px", "fontWeight": 500}),
+                            dcc.RadioItems(
+                                id="pred-toss-decision",
+                                options=[
+                                    {"label": "  Bat first", "value": "bat"},
+                                    {"label": "  Field first", "value": "field"},
+                                ],
+                                value="field",
+                                inline=True,
+                                style={"fontSize": "13px", "marginBottom": "20px"},
+                            ),
+
+                            dbc.Button(
+                                "Predict outcome",
+                                id="predict-btn",
+                                color="primary",
+                                style={"width": "100%",
+                                       "background": "#7F77DD",
+                                       "border": "none",
+                                       "fontWeight": 600},
+                            ),
+                        ])
+                    ], style={"border": "0.5px solid #e0e0e0"}),
+                ], md=4),
+
+                # ── Result panel ──────────────────────────────────
+                dbc.Col([
+                    html.Div(id="prediction-output"),
+                    dcc.Graph(id="win-prob-gauge", style={"height": "320px"}),
+                    html.Div(id="historical-h2h"),
+                ], md=8),
+            ]),
+        ], fluid=True),
+    ])
+
+
+# ════════════════════════════════════════════════════════════════════
+# PAGE 4 — AUCTION SIMULATOR
+# ════════════════════════════════════════════════════════════════════
+
+def layout_auction_simulator():
+    def stat_slider(label, id_, min_, max_, value, step=1):
+        return html.Div([
+            html.Div([
+                html.Span(label,  style={"fontSize": "13px", "fontWeight": 500}),
+                html.Span(id=f"{id_}-out", style={"fontSize": "13px",
+                                                    "color": "#7F77DD",
+                                                    "fontWeight": 600,
+                                                    "float": "right"}),
+            ], style={"display": "flex", "justifyContent": "space-between"}),
+            dcc.Slider(
+                id=id_, min=min_, max=max_, value=value, step=step,
+                marks=None,
+                tooltip={"placement": "bottom", "always_visible": False},
+            ),
+        ], style={"marginBottom": "18px"})
+
+    return html.Div([
+        html.Div([
+            html.H1("Auction Value Simulator", className="page-title"),
+            html.P("Adjust player stats with the sliders to see predicted IPL auction price in real-time.",
+                   className="page-subtitle"),
+        ], className="page-header"),
+
+        dbc.Container([
+            dbc.Row([
+                # ── Sliders ───────────────────────────────────────
+                dbc.Col([
+                    dbc.Card([
+                        dbc.CardBody([
+                            html.H5("Batting stats",
+                                    style={"fontWeight": 600, "marginBottom": "16px"}),
+                            stat_slider("Career runs",    "sl-runs",    0, 8000, 2000, 50),
+                            stat_slider("Batting average","sl-avg",     0, 80,   28,   0.5),
+                            stat_slider("Strike rate",    "sl-sr",      60, 220, 130,  0.5),
+                            stat_slider("Centuries",      "sl-100s",    0, 10,   1,    1),
+                            stat_slider("Half-centuries", "sl-50s",     0, 60,   10,   1),
+                            stat_slider("Boundary rate (%)", "sl-br",   0, 30,   12,   0.5),
+                            stat_slider("Powerplay SR",   "sl-pp-sr",   0, 220, 130,  0.5),
+                            stat_slider("Death SR",       "sl-death-sr",0, 300, 160,  0.5),
+                        ])
+                    ], style={"border": "0.5px solid #e0e0e0", "marginBottom": "16px"}),
+
+                    dbc.Card([
+                        dbc.CardBody([
+                            html.H5("Bowling stats",
+                                    style={"fontWeight": 600, "marginBottom": "16px"}),
+                            stat_slider("Career wickets",    "sl-wkts",     0, 200, 30,   1),
+                            stat_slider("Economy rate",      "sl-econ",     5, 14,  8.5,  0.1),
+                            stat_slider("Bowling average",   "sl-bowl-avg", 10, 60, 35,   0.5),
+                            stat_slider("Dot ball %",        "sl-dot",      0, 60,  20,   0.5),
+                            stat_slider("Death economy",     "sl-d-econ",   5, 16,  9.5,  0.1),
+                            stat_slider("Powerplay economy", "sl-pp-econ",  5, 14,  8.0,  0.1),
+                            stat_slider("Matches bowled",    "sl-mbowled",  0, 150, 40,   1),
+                        ])
+                    ], style={"border": "0.5px solid #e0e0e0"}),
+                ], md=5),
+
+                # ── Live result ───────────────────────────────────
+                dbc.Col([
+                    dbc.Card([
+                        dbc.CardBody([
+                            html.H5("Predicted auction value",
+                                    style={"fontWeight": 600,
+                                           "textAlign": "center",
+                                           "marginBottom": "20px"}),
+                            html.Div(id="auction-price-display",
+                                     style={"textAlign": "center"}),
+                            dcc.Graph(id="auction-gauge",
+                                      style={"height": "260px"},
+                                      config={"displayModeBar": False}),
+                            html.Hr(),
+                            html.P("Similar players at this price",
+                                   className="section-title"),
+                            html.Div(id="similar-players-table"),
+                        ])
+                    ], style={"border": "0.5px solid #e0e0e0", "position": "sticky", "top": "70px"}),
+                ], md=7),
+            ]),
+
+            html.Br(),
+            html.P("All-time player valuations leaderboard", className="section-title"),
+            dcc.Graph(id="valuation-chart", style={"height": "420px"}),
+
+        ], fluid=True),
+    ])

data/processed/batting_features.csv

@@ -0,0 +1,461 @@
+batter,total_runs,balls_faced,matches_batted,innings,fours,sixes,dot_balls,dismissals,strike_rate,batting_average,boundary_rate,dot_ball_pct,runs_per_match,sr_powerplay,sr_middle,sr_death,fifties,hundreds
+V Kohli,9022,6743,267,6761,811,306,2226,237,133.8,38.07,16.57,33.01,33.79,123.74,130.9,202.63,66,8
+RG Sharma,7185,5417,270,5435,653,311,2056,249,132.64,28.86,17.8,37.95,26.61,123.0,125.79,198.0,48,2
+S Dhawan,6769,5304,221,5326,768,153,1970,193,127.62,35.07,17.36,37.14,30.63,121.69,131.26,165.78,51,2
+DA Warner,6567,4682,184,4702,663,236,1715,164,140.26,40.04,19.2,36.63,35.69,136.32,141.07,179.65,62,4
+KL Rahul,5593,4048,143,4059,489,227,1347,123,138.17,45.47,17.69,33.28,39.11,126.16,141.54,186.58,42,6
+SK Raina,5536,4025,200,4046,506,204,1334,167,137.54,33.15,17.64,33.14,27.68,129.17,136.82,173.37,39,1
+MS Dhoni,5439,3944,241,3966,375,264,1336,158,137.91,34.42,16.2,33.87,22.57,76.6,106.66,187.23,24,0
+AM Rahane,5194,4158,191,4162,523,133,1519,174,124.92,29.85,15.78,36.53,27.19,120.32,126.02,164.96,34,2
+AB de Villiers,5181,3400,170,3411,414,253,1039,125,152.38,41.45,19.62,30.56,30.48,118.41,140.96,235.26,40,3
+SV Samson,5008,3556,179,3569,412,234,1223,157,140.83,31.9,18.17,34.39,27.98,127.86,141.65,195.83,26,5
+CH Gayle,4997,3331,141,3346,408,359,1462,127,150.02,39.35,23.03,43.89,35.44,135.29,165.09,209.33,31,6
+RV Uthappa,4954,3784,197,3801,481,182,1464,182,130.92,27.22,17.52,38.69,25.15,123.26,133.21,177.35,27,0
+KD Karthik,4843,3570,233,3580,466,161,1219,187,135.66,25.9,17.56,34.15,20.79,100.69,124.7,188.55,22,0
+F du Plessis,4773,3515,147,3522,440,174,1203,135,135.79,35.36,17.47,34.22,32.47,133.23,129.92,199.04,39,0
+SA Yadav,4468,3017,158,3029,471,172,1030,130,148.09,34.37,21.31,34.14,28.28,135.1,145.68,195.47,30,2
+JC Buttler,4391,2937,127,2948,439,196,1075,112,149.51,39.21,21.62,36.6,34.57,138.78,151.08,193.84,26,7
+AT Rayudu,4348,3391,185,3409,359,173,1185,152,128.22,28.61,15.69,34.95,23.5,104.46,127.87,172.64,22,1
+G Gambhir,4217,3394,151,3404,492,59,1235,134,124.25,31.47,16.23,36.39,27.93,121.43,127.63,139.84,36,0
+Shubman Gill,4196,3025,121,3035,402,133,959,104,138.71,40.35,17.69,31.7,34.68,130.55,145.71,157.21,29,4
+SS Iyer,4044,2993,138,2999,333,174,1019,119,135.12,33.98,16.94,34.05,29.3,110.18,139.33,193.13,31,0
+MK Pandey,3951,3241,160,3250,340,116,1163,134,121.91,29.49,14.07,35.88,24.69,111.87,121.32,159.89,22,1
+SR Watson,3880,2801,141,2813,377,190,1164,125,138.52,31.04,20.24,41.56,27.52,115.16,159.95,187.9,21,4
+RR Pant,3756,2567,131,2576,339,176,884,113,146.32,33.24,20.06,34.44,28.67,119.41,142.62,203.87,20,2
+Q de Kock,3444,2557,118,2563,334,143,992,110,134.69,31.31,18.65,38.8,29.19,129.2,138.81,206.1,24,3
+KA Pollard,3437,2315,168,2329,221,224,842,129,148.47,26.64,19.22,36.37,20.46,92.13,126.89,184.05,16,0
+RA Jadeja,3392,2608,200,2621,251,118,830,125,130.06,27.14,14.15,31.83,16.96,104.79,112.09,159.37,5,0
+Ishan Kishan,3310,2347,120,2354,323,150,885,108,141.03,30.65,20.15,37.71,27.58,131.39,144.78,193.15,20,1
+YK Pathan,3222,2233,153,2245,263,161,781,110,144.29,29.29,18.99,34.98,21.06,146.44,135.17,167.91,13,1
+DA Miller,3200,2309,140,2316,232,144,729,92,138.59,34.78,16.28,31.57,22.86,90.66,129.08,172.81,13,1
+N Rana,3022,2205,118,2211,282,149,881,110,137.05,27.47,19.55,39.95,25.61,113.26,146.37,188.76,22,0
+WP Saha,2934,2296,143,2300,296,87,865,118,127.79,24.86,16.68,37.67,20.52,128.72,117.18,156.94,13,1
+BB McCullum,2882,2176,109,2190,293,130,938,106,132.44,27.19,19.44,43.11,26.44,128.29,131.81,216.44,13,2
+HH Pandya,2855,1949,146,1958,219,152,656,103,146.49,27.72,19.04,33.66,19.55,106.47,130.72,181.14,10,0
+PA Patel,2848,2350,136,2358,365,49,993,127,121.19,22.43,17.62,42.26,20.94,121.27,119.57,140.0,13,0
+GJ Maxwell,2820,1815,134,1823,238,161,653,117,155.37,24.1,21.98,35.98,21.04,134.12,157.43,182.52,18,0
+MA Agarwal,2764,2055,125,2067,275,100,769,118,134.5,23.42,18.25,37.42,22.11,122.57,145.73,214.44,13,1
+Yuvraj Singh,2754,2115,126,2122,218,149,890,109,130.21,25.27,17.35,42.08,21.86,97.41,124.92,181.02,13,0
+V Sehwag,2728,1740,104,1755,334,106,650,98,156.78,27.84,25.29,37.36,26.23,146.67,178.78,235.71,16,2
+RD Gaikwad,2680,1970,78,1979,248,101,698,68,136.04,39.41,17.72,35.43,34.36,126.1,144.54,183.76,21,2
+AD Russell,2655,1518,114,1534,187,223,608,92,174.9,28.86,27.01,40.05,23.29,215.38,155.17,204.71,12,0
+M Vijay,2619,2148,105,2150,247,91,927,96,121.93,27.28,15.74,43.16,24.94,113.01,141.36,168.18,13,2
+SPD Smith,2495,1931,93,1945,226,60,602,78,129.21,31.99,14.81,31.18,26.83,120.71,120.26,173.67,11,1
+SE Marsh,2489,1866,69,1871,269,78,693,65,133.39,38.29,18.6,37.14,36.07,112.66,150.74,163.46,20,1
+YBK Jaiswal,2472,1627,75,1632,295,105,647,69,151.94,35.83,24.59,39.77,32.96,156.53,138.05,225.0,18,2
+JH Kallis,2427,2213,95,2222,255,44,909,89,109.67,27.27,13.51,41.08,25.55,103.58,102.87,194.23,17,0
+DR Smith,2385,1755,88,1764,245,117,787,81,135.9,29.44,20.63,44.84,27.1,125.0,153.65,155.41,17,0
+N Pooran,2375,1488,94,1490,163,171,545,76,159.61,31.25,22.45,36.63,25.27,138.65,157.79,173.04,14,0
+SR Tendulkar,2334,1941,78,1948,296,29,773,71,120.25,32.87,16.74,39.82,29.92,114.85,123.22,169.23,13,1
+RA Tripathi,2291,1655,98,1662,229,85,603,86,138.43,26.64,18.97,36.44,23.38,137.7,135.18,168.0,12,0
+Abhishek Sharma,2196,1325,82,1329,210,129,488,75,165.74,29.28,25.58,36.83,26.78,166.29,165.88,160.78,12,2
+R Dravid,2174,1878,82,1882,269,28,780,77,115.76,28.23,15.81,41.53,26.51,106.81,119.63,179.78,11,0
+MP Stoinis,2136,1452,104,1458,168,114,498,75,147.11,28.48,19.42,34.3,20.54,118.18,133.0,190.83,10,1
+KS Williamson,2132,1692,76,1697,187,64,583,59,126.0,36.14,14.83,34.46,28.05,98.48,134.63,168.66,18,0
+B Sai Sudharsan,2115,1447,48,1455,215,70,443,43,146.16,49.19,19.7,30.62,44.06,134.7,148.56,210.99,14,3
+AJ Finch,2092,1626,90,1635,214,78,683,84,128.66,24.9,17.96,42.0,23.24,116.13,140.13,162.4,15,0
+AC Gilchrist,2069,1483,80,1495,239,92,673,76,139.51,27.22,22.32,45.38,25.86,133.79,152.54,208.0,11,2
+D Padikkal,2048,1564,81,1568,214,72,612,77,130.95,26.6,18.29,39.13,25.28,129.85,130.86,144.29,14,1
+JP Duminy,2029,1631,75,1636,126,79,516,49,124.4,41.41,12.57,31.64,27.05,98.35,108.75,182.1,14,0
+S Dube,2009,1428,83,1430,122,128,512,66,140.69,30.44,17.51,35.85,24.2,100.0,137.41,159.26,10,0
+MEK Hussey,1977,1610,58,1612,198,52,606,52,122.8,38.02,15.53,37.64,34.09,105.78,136.47,188.51,15,1
+AR Patel,1947,1462,129,1466,133,94,485,94,133.17,20.71,15.53,33.17,15.09,102.59,121.59,157.98,3,0
+PP Shaw,1892,1280,79,1283,238,61,483,77,147.81,24.57,23.36,37.73,23.95,143.43,163.24,163.64,14,0
+H Klaasen,1829,1124,53,1130,123,103,289,43,162.72,42.53,20.11,25.71,34.51,128.92,154.62,191.48,10,2
+SP Narine,1820,1118,122,1119,191,120,519,98,162.79,18.57,27.82,46.42,14.92,164.78,184.56,92.38,7,1
+DPMD Jayawardene,1808,1462,78,1464,200,40,560,64,123.67,28.25,16.42,38.3,23.18,115.99,115.97,180.92,10,1
+KH Pandya,1792,1358,117,1359,156,67,503,82,131.96,21.85,16.42,37.04,15.32,99.13,125.06,157.14,2,0
+Mandeep Singh,1706,1385,97,1388,176,38,523,84,123.18,20.31,15.45,37.76,17.59,115.21,118.44,160.59,6,0
+P Simran Singh,1701,1095,59,1096,178,88,425,58,155.34,29.33,24.29,38.81,28.83,151.78,161.3,172.5,12,1
+KK Nair,1699,1296,77,1298,186,49,501,73,131.1,23.27,18.13,38.66,22.06,124.69,126.38,190.09,11,0
+MK Tiwary,1695,1444,83,1449,156,40,530,60,117.38,28.25,13.57,36.7,20.42,95.72,110.53,155.11,7,0
+KC Sangakkara,1687,1385,68,1392,195,27,515,69,121.81,24.45,16.03,37.18,24.81,112.03,124.8,157.78,10,0
+R Parag,1687,1209,81,1212,120,94,429,68,139.54,24.81,17.7,35.48,20.83,119.73,135.41,161.73,7,0
+Tilak Varma,1680,1157,58,1160,134,81,369,44,145.2,38.18,18.58,31.89,28.97,123.4,138.0,196.32,8,1
+JM Bairstow,1674,1145,52,1149,173,74,445,50,146.2,33.48,21.57,38.86,32.19,145.82,146.21,155.17,9,2
+AK Markram,1633,1208,63,1211,130,67,380,55,135.18,29.69,16.31,31.46,25.92,125.81,131.9,186.07,10,0
+DJ Bravo,1560,1196,110,1204,120,66,452,69,130.43,22.61,15.55,37.79,14.18,98.68,97.92,190.02,5,0
+SO Hetmyer,1560,1056,84,1061,98,96,363,58,147.73,26.9,18.37,34.38,18.57,105.63,125.0,178.65,5,0
+NV Ojha,1554,1308,94,1313,121,79,599,74,118.81,21.0,15.29,45.8,16.53,99.63,127.74,144.24,6,0
+MR Marsh,1504,1087,57,1087,122,86,430,54,138.36,27.85,19.14,39.56,26.39,144.17,128.69,155.45,10,1
+VR Iyer,1497,1089,57,1090,137,67,418,50,137.47,29.94,18.73,38.38,26.26,130.65,137.37,182.5,12,1
+DJ Hooda,1497,1170,101,1175,98,62,389,81,127.95,18.48,13.68,33.25,14.82,94.47,130.83,150.21,8,0
+SS Tiwary,1494,1235,73,1244,111,50,428,49,120.97,30.49,13.04,34.66,20.47,109.63,114.12,151.59,8,0
+S Badrinath,1441,1208,65,1212,154,28,458,48,119.29,30.02,15.07,37.91,22.17,82.17,123.23,163.68,11,0
+EJG Morgan,1406,1147,74,1148,112,64,478,61,122.58,23.05,15.34,41.67,19.0,93.36,117.2,168.33,5,0
+BJ Hodge,1400,1112,63,1118,122,43,399,46,125.9,30.43,14.84,35.88,22.22,92.57,116.16,169.76,6,0
+SC Ganguly,1349,1257,56,1263,137,42,594,54,107.32,24.98,14.24,47.26,24.09,97.7,109.78,175.38,7,0
+RM Patidar,1349,851,45,852,88,91,270,42,158.52,32.12,21.03,31.73,29.98,119.55,173.71,162.07,11,1
+TM Head,1332,825,45,828,148,63,303,41,161.45,32.49,25.58,36.73,29.6,169.57,137.5,195.83,8,1
+CA Lynn,1329,941,42,945,132,66,387,39,141.23,34.08,21.04,41.13,31.64,144.39,135.46,136.36,10,0
+DJ Hussey,1322,1070,61,1075,90,60,397,48,123.55,27.54,14.02,37.1,21.67,103.73,116.6,177.56,5,0
+RK Singh,1318,920,58,926,108,64,311,41,143.26,32.15,18.7,33.8,22.72,127.27,114.26,191.96,6,0
+PD Salt,1258,729,40,733,138,67,267,38,172.57,33.11,28.12,36.63,31.45,168.92,179.5,218.18,12,0
+V Shankar,1233,952,63,954,88,48,308,46,129.52,26.8,14.29,32.35,19.57,91.72,121.2,172.84,7,0
+KM Jadhav,1208,969,81,981,102,40,341,54,124.66,22.37,14.65,35.19,14.91,88.43,121.41,143.38,4,0
+MM Ali,1167,834,59,837,95,67,328,50,139.93,23.34,19.42,39.33,19.78,122.08,140.08,176.52,6,0
+R Tewatia,1161,857,82,861,94,55,310,51,135.47,22.76,17.39,36.17,14.16,92.86,99.76,171.0,1,0
+TM Dilshan,1153,1001,50,1007,140,24,451,41,115.18,28.12,16.38,45.05,23.06,108.15,108.5,188.37,9,0
+IK Pathan,1150,947,82,950,88,38,336,52,121.44,22.12,13.31,35.48,14.02,113.79,96.83,158.15,1,0
+A Badoni,1135,832,54,833,91,44,275,44,136.42,25.8,16.23,33.05,21.02,96.88,122.94,170.73,7,0
+ML Hayden,1107,799,32,806,121,44,332,27,138.55,41.0,20.65,41.55,34.59,133.2,141.43,200.0,8,0
+M Vohra,1083,828,51,829,104,43,358,48,130.8,22.56,17.75,43.24,21.24,124.75,142.31,200.0,3,0
+DP Conway,1080,776,28,779,117,34,271,25,139.18,43.2,19.46,34.92,38.57,124.15,156.29,154.17,11,0
+LMP Simmons,1079,848,29,852,109,44,372,27,127.24,39.96,18.04,43.87,37.21,118.47,141.4,128.0,11,1
+LS Livingstone,1066,687,49,694,70,76,249,41,155.17,26.0,21.25,36.24,21.76,130.32,145.74,219.01,7,0
+JM Sharma,1053,690,53,692,82,65,256,45,152.61,23.4,21.3,37.1,19.87,84.62,145.07,181.95,1,0
+TH David,1029,590,51,592,66,75,184,28,174.41,36.75,23.9,31.19,20.18,,134.29,202.9,2,0
+LRPL Taylor,1017,818,54,822,66,46,307,41,124.33,24.8,13.69,37.53,18.83,126.45,113.07,165.93,3,0
+KP Pietersen,1001,742,36,743,91,40,275,28,134.91,35.75,17.65,37.06,27.81,130.19,129.14,200.0,4,1
+MC Henriques,1000,785,54,788,87,28,269,38,127.39,26.32,14.65,34.27,18.52,123.76,110.05,175.76,5,0
+SM Curran,997,731,53,735,85,41,256,41,136.39,24.32,17.24,35.02,18.81,110.06,121.07,186.8,6,0
+Y Venugopal Rao,985,832,52,836,77,37,324,42,118.39,23.45,13.7,38.94,18.94,82.64,111.67,157.87,3,0
+JA Morkel,975,683,67,687,61,55,226,40,142.75,24.38,16.98,33.09,14.55,121.74,120.13,164.55,3,0
+A Symonds,974,745,36,750,74,41,273,26,130.74,37.46,15.44,36.64,27.06,104.58,126.69,168.31,5,1
+CL White,971,757,45,760,76,38,260,35,128.27,27.74,15.06,34.35,21.58,89.17,121.24,191.22,6,0
+BA Stokes,935,695,43,698,81,32,231,41,134.53,22.8,16.26,33.24,21.74,129.13,133.91,150.59,2,2
+Dhruv Jurel,928,619,44,619,76,48,199,30,149.92,30.93,20.03,32.15,21.09,140.74,131.0,178.39,7,0
+T Stubbs,912,585,37,586,66,44,144,20,155.9,45.6,18.8,24.62,24.65,121.88,118.67,216.74,5,0
+C Green,903,593,36,596,78,42,181,26,152.28,34.73,20.24,30.52,25.08,146.31,149.65,170.75,3,1
+HH Gibbs,886,805,36,807,83,31,378,32,110.06,27.69,14.16,46.96,24.61,103.93,112.54,174.29,6,0
+STR Binny,880,682,66,683,66,35,251,44,129.03,20.0,14.81,36.8,13.33,69.39,120.57,160.0,0,0
+Shashank Singh,843,539,38,539,62,47,159,20,156.4,42.15,20.22,29.5,22.18,137.14,123.13,197.03,5,0
+Harbhajan Singh,833,599,88,604,79,42,251,56,139.07,14.88,20.2,41.9,9.47,114.06,106.55,158.31,1,0
+R Ashwin,833,704,93,705,64,29,257,62,118.32,13.44,13.21,36.51,8.96,102.38,106.19,134.5,1,0
+Priyansh Arya,828,430,25,430,81,57,155,25,192.56,33.12,32.09,36.05,33.12,189.62,200.89,,5,1
+Abdul Samad,815,558,56,561,52,51,196,46,146.06,17.72,18.46,35.13,14.55,120.0,120.96,174.43,0,0
+MS Bisla,798,700,39,702,93,23,339,37,114.0,21.57,16.57,48.43,20.46,106.33,128.11,160.0,4,0
+Shakib Al Hasan,795,635,52,639,73,21,212,39,125.2,20.38,14.8,33.39,15.29,88.46,118.45,150.55,2,0
+N Wadhera,784,566,36,566,64,43,217,31,138.52,25.29,18.9,38.34,21.78,115.25,139.35,152.0,4,0
+ST Jayasuriya,768,529,30,532,84,39,251,29,145.18,26.48,23.25,47.45,25.6,139.85,160.74,,4,1
+M Shahrukh Khan,767,521,52,521,48,52,187,32,147.22,23.97,19.19,35.89,14.75,112.12,133.33,171.84,2,0
+SN Khan,746,544,43,548,86,20,194,36,137.13,20.72,19.49,35.66,17.35,137.78,110.61,184.83,2,0
+GC Smith,739,664,29,668,94,9,295,30,111.3,24.63,15.51,44.43,25.48,95.59,137.91,163.64,4,0
+AD Mathews,724,573,41,575,44,29,183,31,126.35,23.35,12.74,31.94,17.66,68.75,114.46,154.07,1,0
+Abishek Porel,691,479,30,480,68,27,162,29,144.26,23.83,19.83,33.82,23.03,143.15,136.84,206.9,3,0
+TL Suman,676,573,39,575,55,26,241,30,117.98,22.53,14.14,42.06,17.33,102.5,125.0,131.51,2,0
+AM Nayar,672,573,50,577,55,20,223,39,117.28,17.23,13.09,38.92,13.44,84.04,108.53,161.15,0,0
+A Raghuvanshi,672,477,26,477,70,23,160,23,140.88,29.22,19.5,33.54,25.85,140.88,140.08,144.44,4,0
+Washington Sundar,668,521,51,522,60,20,178,37,128.21,18.05,15.36,34.17,13.1,117.65,111.81,153.27,1,0
+GJ Bailey,663,541,36,544,59,19,198,29,122.55,22.86,14.42,36.6,18.42,99.38,95.13,187.58,2,0
+Nithish Kumar Reddy,657,478,29,478,40,39,167,24,137.45,27.38,16.53,34.94,22.66,89.0,141.16,182.14,3,0
+E Lewis,654,477,26,477,62,36,212,24,137.11,27.25,20.55,44.44,25.15,140.26,128.57,160.0,4,0
+V Suryavanshi,652,303,16,303,52,61,116,16,215.18,40.75,37.29,38.28,40.75,218.4,207.69,,3,2
+PP Chawla,624,561,86,563,56,20,245,57,111.23,10.95,13.55,43.67,7.26,114.29,91.52,124.77,0,0
+CH Morris,618,397,49,398,41,35,119,29,155.67,21.31,19.14,29.97,12.61,211.11,113.6,173.76,2,0
+JJ Roy,614,441,21,443,75,21,178,18,139.23,34.11,21.77,40.36,29.24,131.17,155.75,170.0,4,0
+Rashid Khan,613,389,69,395,44,41,155,45,157.58,13.62,21.85,39.85,8.88,,130.0,165.89,1,0
+PJ Cummins,612,400,48,402,39,41,154,33,153.0,18.55,20.0,38.5,12.75,20.0,145.88,161.33,3,0
+JD Ryder,604,455,29,458,69,19,191,29,132.75,20.83,19.34,41.98,20.83,132.28,131.46,147.37,4,0
+HM Amla,577,406,16,407,60,21,139,13,142.12,44.38,19.95,34.24,36.06,124.89,150.35,213.16,3,2
+Shahbaz Ahmed,560,462,39,464,30,25,160,30,121.21,18.67,11.9,34.63,14.36,80.56,107.86,156.85,1,0
+Naman Dhir,546,324,26,325,52,29,101,20,168.52,27.3,25.0,31.17,21.0,151.14,152.88,192.42,2,0
+CJ Anderson,538,421,29,423,40,31,185,22,127.79,24.45,16.86,43.94,18.55,79.27,123.97,197.22,3,0
+RS Bopara,531,453,22,453,39,16,160,19,117.22,27.95,12.14,35.32,24.14,94.44,118.31,161.54,3,0
+JP Faulkner,527,385,45,390,36,23,127,28,136.88,18.82,15.32,32.99,11.71,70.0,93.94,166.95,0,0
+MK Lomror,527,372,35,373,33,30,120,29,141.67,18.17,16.94,32.26,15.06,80.85,156.48,138.53,1,0
+RD Rickelton,525,355,19,355,56,29,157,18,147.89,29.17,23.94,44.23,27.63,149.61,145.45,50.0,4,0
+D Brevis,519,359,20,359,35,37,146,20,144.57,25.95,20.06,40.67,25.95,108.89,148.33,224.14,2,0
+M Manhas,514,470,38,470,43,10,181,24,109.36,21.42,11.28,38.51,13.53,86.11,99.6,136.91,0,0
+Gurkeerat Singh,511,422,32,422,55,11,156,27,121.09,18.93,15.64,36.97,15.97,90.57,107.38,160.8,2,0
+OA Shah,506,388,22,389,34,23,133,13,130.41,38.92,14.69,34.28,23.0,107.14,120.7,151.88,4,0
+PC Valthaty,505,414,23,418,61,20,215,22,121.98,22.95,19.57,51.93,21.96,109.76,146.15,207.69,2,1
+SW Billings,503,385,27,388,40,20,139,26,130.65,19.35,15.58,36.1,18.63,125.64,126.13,165.22,3,0
+SE Rutherford,500,368,26,368,29,37,156,22,135.87,22.73,17.93,42.39,19.23,26.32,136.02,148.47,1,0
+R Powell,486,347,28,347,31,35,147,24,140.06,20.25,19.02,42.36,17.36,115.38,109.5,188.06,1,0
+RR Rossouw,473,306,22,308,45,25,115,21,154.58,22.52,22.88,37.58,21.5,130.43,161.82,215.38,2,0
+WG Jacks,463,303,19,304,38,29,111,16,152.81,28.94,22.11,36.63,24.37,144.44,160.76,100.0,2,1
+DT Christian,460,394,41,398,23,19,134,28,116.75,16.43,10.66,34.01,11.22,35.71,100.52,139.57,0,0
+A Mhatre,441,250,13,250,51,23,89,12,176.4,36.75,29.6,35.6,33.92,179.74,180.46,90.0,3,0
+Ashutosh Sharma,426,270,20,273,27,30,95,17,157.78,25.06,21.11,35.19,21.3,,139.29,170.89,2,0
+NLTC Perera,424,306,30,309,23,26,112,24,138.56,17.67,16.01,36.6,14.13,137.5,116.15,166.67,0,0
+SA Asnodkar,423,337,19,339,56,10,149,19,125.52,22.26,19.58,44.21,22.26,128.21,119.42,,2,0
+JR Hopes,417,305,19,306,49,11,111,17,136.72,24.53,19.67,36.39,21.95,126.0,148.31,143.24,4,0
+R Ravindra,413,289,18,290,40,16,99,16,142.91,25.81,19.38,34.26,22.94,136.19,147.14,266.67,2,0
+J Botha,409,358,28,359,39,5,133,22,114.25,18.59,12.29,37.15,14.61,80.28,114.81,137.76,1,0
+LR Shukla,405,350,33,350,33,16,144,29,115.71,13.97,14.0,41.14,12.27,58.33,100.97,144.27,0,0
+AL Menaria,401,354,23,356,24,18,140,22,113.28,18.23,11.86,39.55,17.43,88.0,111.52,131.67,0,0
+MV Boucher,394,307,23,309,32,13,107,15,128.34,26.27,14.66,34.85,17.13,93.1,110.59,165.74,1,0
+CA Pujara,390,390,22,391,50,4,187,18,100.0,21.67,13.85,47.95,17.73,93.78,103.33,137.93,1,0
+Azhar Mahmood,388,303,21,303,39,13,116,19,128.05,20.42,17.16,38.28,18.48,90.91,126.57,152.38,2,0
+J Fraser-McGurk,385,192,15,194,39,30,83,14,200.52,27.5,35.94,43.23,25.67,213.16,152.5,,4,0
+Sameer Rizvi,381,273,16,273,30,22,110,13,139.56,29.31,19.05,40.29,23.81,56.25,141.05,186.27,3,0
+A Mishra,381,417,55,419,31,5,177,30,91.37,12.7,8.63,42.45,6.93,83.33,68.37,113.4,0,0
+KR Mayers,379,258,13,263,38,22,124,13,146.9,29.15,23.26,48.06,29.15,141.98,169.57,,4,0
+DB Ravi Teja,375,315,25,317,35,9,116,20,119.05,18.75,13.97,36.83,15.0,94.2,118.24,140.23,1,0
+S Sohal,368,289,20,292,34,18,136,18,127.34,20.44,17.99,47.06,18.4,128.45,137.5,44.44,2,0
+P Negi,365,288,35,289,27,16,112,26,126.74,14.04,14.93,38.89,10.43,62.5,108.94,143.95,0,0
+Rahmanullah Gurbaz,363,270,18,271,34,22,134,17,134.44,21.35,20.74,49.63,20.17,127.88,156.45,,2,0
+KV Sharma,352,295,40,295,20,17,113,25,119.32,14.08,12.54,38.31,8.8,71.43,95.0,141.61,0,0
+DJ Mitchell,351,267,15,267,28,10,75,13,131.46,27.0,14.23,28.09,23.4,137.76,127.14,131.03,2,0
+R Bhatia,342,284,47,284,24,13,104,29,120.42,11.79,13.03,36.62,7.28,100.0,82.35,155.78,0,0
+P Kumar,340,312,57,314,22,17,148,34,108.97,10.0,12.5,47.44,5.96,75.0,100.0,112.45,0,0
+AP Tare,339,272,27,273,40,11,129,24,124.63,14.12,18.75,47.43,12.56,114.62,158.97,161.9,1,0
+SN Thakur,339,256,42,257,31,13,96,28,132.42,12.11,17.19,37.5,8.07,0.0,128.7,139.42,1,0
+B Kumar,332,361,73,362,32,3,166,37,91.97,8.97,9.7,45.98,4.55,,64.56,99.65,0,0
+JEC Franklin,327,300,16,301,25,9,115,11,109.0,29.73,11.33,38.33,20.44,84.31,110.88,152.94,1,0
+RN ten Doeschate,326,235,21,235,26,15,77,17,138.72,19.18,17.45,32.77,15.52,85.71,110.0,173.87,1,0
+Anuj Rawat,318,267,21,267,26,14,114,14,119.1,22.71,14.98,42.7,15.14,97.92,93.27,189.55,1,0
+R Vinay Kumar,310,274,42,274,21,9,103,26,113.14,11.92,10.95,37.59,7.38,,91.03,121.94,0,0
+Aniket Verma,309,197,19,197,17,25,76,14,156.85,22.07,21.32,38.58,16.26,100.0,147.52,181.01,1,0
+Lalit Yadav,305,285,21,290,27,7,116,15,107.02,20.33,11.93,40.7,14.52,70.45,109.6,125.0,0,0
+DB Das,304,260,22,261,23,16,117,12,116.92,25.33,15.0,45.0,13.82,115.79,108.2,130.86,0,0
+C de Grandhomme,303,224,21,225,18,18,82,15,135.27,20.2,16.07,36.61,14.43,11.11,141.88,138.78,0,0
+LA Pomersbach,302,243,16,246,25,13,102,11,124.28,27.45,15.64,41.98,18.88,133.33,104.1,173.53,1,0
+UBT Chand,300,299,20,300,32,9,155,20,100.33,15.0,13.71,51.84,15.0,94.8,109.32,87.5,1,0
+Ramandeep Singh,299,206,26,208,17,19,71,16,145.15,18.69,17.48,34.47,11.5,80.0,115.93,186.36,0,0
+DJG Sammy,295,241,20,241,15,18,98,15,122.41,19.67,13.69,40.66,14.75,155.56,105.62,155.56,1,0
+SP Goswami,293,293,21,295,32,3,131,20,100.0,14.65,11.95,44.71,13.95,97.3,108.51,78.57,1,0
+A Manohar,292,236,20,236,23,14,92,19,123.73,15.37,15.68,38.98,14.6,66.67,110.61,166.23,0,0
+Y Nagar,285,259,20,259,20,9,101,15,110.04,19.0,11.2,39.0,14.25,45.45,90.34,144.66,0,0
+GH Vihari,284,319,23,321,23,1,136,20,89.03,14.2,7.52,42.63,12.35,71.03,95.62,105.77,0,0
+VVS Laxman,282,266,20,267,33,5,119,16,106.02,17.62,14.29,44.74,14.1,100.0,130.0,50.0,1,0
+JO Holder,282,219,28,221,14,20,92,21,128.77,13.43,15.53,42.01,10.07,61.9,126.67,141.46,0,0
+A Ashish Reddy,280,193,23,193,16,15,58,15,145.08,18.67,16.06,30.05,12.17,83.33,138.33,151.18,0,0
+B Chipli,280,250,21,251,28,7,104,16,112.0,17.5,14.0,41.6,13.33,77.65,112.4,177.27,1,0
+JP Inglis,278,177,11,177,26,16,69,10,157.06,27.8,23.73,38.98,25.27,162.03,154.43,147.37,1,0
+MJ Lumb,278,191,12,194,45,6,89,10,145.55,27.8,26.7,46.6,23.17,147.62,130.43,,1,0
+PBB Rajapaksa,277,190,13,191,22,15,70,12,145.79,23.08,19.47,36.84,21.31,158.11,141.07,50.0,1,0
+JC Archer,275,191,34,195,15,18,71,20,143.98,13.75,17.28,37.17,8.09,0.0,119.15,156.43,0,0
+HV Patel,274,234,43,234,17,15,99,28,117.09,9.79,13.68,42.31,6.37,,95.6,130.77,0,0
+PK Garg,273,238,19,241,16,9,86,19,114.71,14.37,10.5,36.13,14.37,98.31,107.38,183.33,1,0
+MJ Guptill,271,196,13,197,24,15,88,12,138.27,22.58,19.9,44.9,20.85,138.46,137.04,,1,0
+C Connolly,270,165,7,165,24,18,61,6,163.64,45.0,25.45,36.97,38.57,115.0,189.36,209.09,2,0
+R Sathish,270,230,24,231,22,6,87,16,117.39,16.88,12.17,37.83,11.25,0.0,102.42,144.44,0,0
+Atharva Taide,260,179,10,179,30,8,65,9,145.25,28.89,21.23,36.31,26.0,153.39,129.51,,2,0
+M Kaif,259,249,22,250,22,6,111,16,104.02,16.19,11.24,44.58,11.77,69.39,111.83,116.13,0,0
+K Rabada,250,232,32,233,19,11,106,19,107.76,13.16,12.93,45.69,7.81,60.0,75.27,132.09,0,0
+K Gowtham,247,148,27,148,15,17,51,18,166.89,13.72,21.62,34.46,9.15,114.29,142.62,190.0,0,0
+Harpreet Brar,244,203,26,203,19,10,82,13,120.2,18.77,14.29,40.39,9.38,,87.5,138.17,0,0
+SM Katich,241,185,11,186,26,8,80,10,130.27,24.1,18.38,43.24,21.91,129.75,131.37,130.77,2,0
+BCJ Cutting,238,141,17,141,15,19,53,11,168.79,21.64,24.11,37.59,14.0,50.0,96.67,190.83,0,0
+MD Mishra,237,206,17,208,24,8,97,15,115.05,15.8,15.53,47.09,13.94,108.27,120.69,153.33,0,0
+R Shepherd,224,112,16,113,15,19,35,10,200.0,22.4,30.36,31.25,14.0,,135.71,238.57,1,0
+Mohammad Nabi,221,151,19,152,18,12,55,17,146.36,13.0,19.87,36.42,11.63,164.71,140.0,147.3,0,0
+K Goel,218,231,16,231,17,9,122,13,94.37,16.77,11.26,52.81,13.62,97.62,73.17,109.09,0,0
+AA Jhunjhunwala,217,209,15,210,19,5,89,14,103.83,15.5,11.48,42.58,14.47,59.62,111.76,139.47,1,0
+R Dhawan,210,187,22,187,18,7,84,10,112.3,21.0,13.37,44.92,9.55,,81.4,138.61,0,0
+Kuldeep Yadav,210,255,41,255,18,3,128,15,82.35,14.0,8.24,50.2,5.12,,60.61,89.95,0,0
+UT Yadav,208,201,48,201,16,9,96,20,103.48,10.4,12.44,47.76,4.33,,60.0,114.29,0,0
+CA Ingram,205,179,15,180,22,5,74,12,114.53,17.08,15.08,41.34,13.67,70.37,113.68,151.43,0,0
+PD Collingwood,203,156,7,156,9,13,57,4,130.13,50.75,14.1,36.54,29.0,116.13,114.77,178.38,3,0
+JD Unadkat,201,169,28,169,16,7,67,14,118.93,14.36,13.61,39.64,7.18,,96.08,128.81,0,0
+KS Bharat,199,161,8,163,12,8,56,7,123.6,28.43,12.42,34.78,24.88,110.26,124.29,200.0,1,0
+SK Warne,198,213,27,214,14,6,100,19,92.96,10.42,9.39,46.95,7.33,,82.0,102.65,0,0
+SP Fleming,196,162,10,165,27,3,78,9,120.99,21.78,18.52,48.15,19.6,116.0,137.84,,0,0
+AS Raut,194,167,16,167,13,7,62,11,116.17,17.64,11.98,37.13,12.12,83.33,97.47,140.79,0,0
+Salman Butt,193,160,7,161,30,2,75,7,120.62,27.57,20.0,46.88,27.57,110.28,139.22,200.0,1,0
+BJ Rohrer,193,141,8,141,21,5,52,7,136.88,27.57,18.44,36.88,24.12,50.0,109.17,243.33,1,0
+YV Takawale,192,177,10,178,26,3,95,8,108.47,24.0,16.38,53.67,19.2,92.14,136.0,241.67,0,0
+D Ferreira,191,132,9,132,19,10,55,8,144.7,23.88,21.97,41.67,21.22,116.67,148.61,145.83,2,0
+HC Brook,190,153,11,154,23,4,65,9,124.18,21.11,17.65,42.48,17.27,109.78,146.34,145.0,0,1
+SB Dubey,189,109,13,109,15,12,31,7,173.39,27.0,24.77,28.44,14.54,233.33,114.29,198.53,0,0
+Bipul Sharma,187,123,17,123,11,9,33,6,152.03,31.17,16.26,26.83,11.0,,134.62,156.7,0,0
+MS Wade,183,177,14,177,23,2,85,13,103.39,14.08,14.12,48.02,13.07,110.81,90.91,,0,0
+SD Hope,183,122,9,122,12,12,45,8,150.0,22.88,19.67,36.89,20.33,138.57,175.56,100.0,0,0
+FY Fazal,183,172,11,173,22,1,67,9,106.4,20.33,13.37,38.95,16.64,98.86,111.11,120.0,0,0
+Sikandar Raza,182,136,9,136,12,8,52,7,133.82,26.0,14.71,38.24,20.22,129.41,136.96,125.93,1,0
+CR Brathwaite,181,110,14,111,10,16,44,14,164.55,12.93,23.64,40.0,12.93,,100.0,201.43,0,0
+AC Voges,181,143,7,143,15,3,44,6,126.57,30.17,12.59,30.77,25.86,84.62,120.93,150.0,0,0
+S Gopal,180,169,22,169,19,2,67,13,106.51,13.85,12.43,39.64,8.18,42.86,78.38,135.23,0,0
+AB Agarkar,179,151,18,154,13,5,53,8,118.54,22.38,11.92,35.1,9.94,153.85,84.75,137.97,0,0
+MF Maharoof,177,123,14,123,12,9,43,10,143.9,17.7,17.07,34.96,12.64,50.0,104.26,171.62,0,0
+C Munro,177,141,11,141,19,8,65,9,125.53,19.67,19.15,46.1,16.09,132.38,109.68,80.0,0,0
+DH Yagnik,170,133,17,137,23,2,55,9,127.82,18.89,18.8,41.35,10.0,0.0,120.0,139.39,0,0
+CM Gautam,169,149,13,150,17,6,75,9,113.42,18.78,15.44,50.34,13.0,111.94,108.89,121.62,0,0
+MG Johnson,167,163,28,164,10,7,76,14,102.45,11.93,10.43,46.63,5.96,,69.23,108.76,0,0
+MN van Wyk,167,131,5,132,19,1,41,3,127.48,55.67,15.27,31.3,33.4,110.34,120.9,154.29,1,0
+DW Steyn,167,160,33,160,14,3,69,22,104.38,7.59,10.62,43.12,5.06,,39.13,115.33,0,0
+PA Reddy,164,160,10,160,15,2,65,10,102.5,16.4,10.62,40.62,16.4,96.49,109.09,300.0,0,0
+N Jagadeesan,162,147,10,147,21,2,65,9,110.2,18.0,15.65,44.22,16.2,104.0,122.95,81.82,0,0
+MN Samuels,161,172,14,172,9,7,84,12,93.6,13.42,9.3,48.84,11.5,37.84,104.07,158.33,0,0
+RE van der Merwe,159,141,15,141,11,8,63,11,112.77,14.45,13.48,44.68,10.6,102.56,109.09,130.56,0,0
+R McLaren,159,171,13,171,14,1,69,8,92.98,19.88,8.77,40.35,12.23,73.91,86.14,117.02,1,0
+MD Choudhary,156,109,8,109,9,12,44,5,143.12,31.2,19.27,40.37,19.5,,128.95,150.7,1,0
+V Nigam,154,89,9,89,18,8,32,6,173.03,25.67,29.21,35.96,17.11,250.0,155.88,172.34,0,0
+MW Short,153,131,8,131,18,4,60,8,116.79,19.12,16.79,45.8,19.12,123.16,102.86,0.0,0,0
+J Overton,151,98,8,98,14,7,33,5,154.08,30.2,21.43,33.67,18.88,,151.61,155.22,0,0
+M Jansen,151,144,22,144,8,7,60,13,104.86,11.62,10.42,41.67,6.86,100.0,107.5,103.88,0,0
+AD Hales,148,117,6,118,13,6,47,6,126.5,24.67,16.24,40.17,24.67,135.23,100.0,,0,0
+D Wiese,148,100,11,101,12,7,30,6,148.0,24.67,19.0,30.0,13.45,,125.81,157.97,0,0
+P Nissanka,147,101,7,101,19,5,39,7,145.54,21.0,23.76,38.61,21.0,156.98,80.0,,0,0
+SM Pollock,147,111,8,111,12,8,42,8,132.43,18.38,18.02,37.84,18.38,50.0,106.06,205.71,0,0
+S Vidyut,145,109,8,109,21,3,49,8,133.03,18.12,22.02,44.95,18.12,131.71,137.04,,1,0
+Sachin Baby,144,115,11,118,11,5,44,10,125.22,14.4,13.91,38.26,13.09,0.0,89.39,184.78,0,0
+N Saini,140,140,10,141,16,0,65,11,100.0,12.73,11.43,46.43,14.0,94.68,112.5,100.0,1,0
+Anmolpreet Singh,139,115,9,115,19,3,53,9,120.87,15.44,19.13,46.09,15.44,125.25,93.75,,0,0
+S Anirudha,136,112,12,113,9,7,45,8,121.43,17.0,14.29,40.18,11.33,106.45,95.0,158.54,1,0
+MJ Santner,136,125,21,126,9,6,53,8,108.8,17.0,12.0,42.4,6.48,83.33,91.43,127.27,0,0
+GD Phillips,132,111,13,112,7,8,48,11,118.92,12.0,13.51,43.24,10.15,57.89,97.83,165.22,0,0
+SB Styris,131,132,10,133,10,3,51,7,99.24,18.71,9.85,38.64,13.1,73.91,102.2,116.67,0,0
+W Jaffer,130,121,8,121,14,3,56,7,107.44,18.57,14.05,46.28,16.25,86.46,188.0,,1,0
+RD Chahar,129,124,22,124,13,5,67,16,104.03,8.06,14.52,54.03,5.86,,80.65,111.83,0,0
+Kamran Akmal,128,77,6,78,13,8,33,4,166.23,32.0,27.27,42.86,21.33,124.39,237.5,166.67,1,0
+P Dogra,127,138,12,138,4,5,60,12,92.03,10.58,6.52,43.48,10.58,,80.21,119.05,0,0
+TK Curran,127,107,10,107,10,3,36,5,118.69,25.4,12.15,33.64,12.7,,109.52,131.82,1,0
+UT Khawaja,127,99,6,100,14,3,37,5,128.28,25.4,17.17,37.37,21.17,137.5,89.47,,0,0
+M Morkel,126,89,20,90,11,5,32,11,141.57,11.45,17.98,35.96,6.3,,71.43,154.67,0,0
+SS Prabhudessai,126,105,10,106,11,4,48,9,120.0,14.0,14.29,45.71,12.6,87.5,132.26,121.05,0,0
+MC Juneja,125,128,7,128,11,1,48,7,97.66,17.86,9.38,37.5,17.86,96.3,102.17,55.56,0,0
+MM Sharma,125,136,30,136,9,4,67,17,91.91,7.35,9.56,49.26,4.17,,85.0,97.37,0,0
+BB Samantray,125,113,8,113,13,2,45,5,110.62,25.0,13.27,39.82,15.62,84.62,114.29,129.17,1,0
+B Lee,124,96,19,97,8,8,40,10,129.17,12.4,16.67,41.67,6.53,,90.91,140.54,0,0
+AB McDonald,123,100,9,100,9,4,35,5,123.0,24.6,13.0,35.0,13.67,157.89,78.95,125.81,0,0
+Harpreet Singh,123,118,8,119,10,3,49,6,104.24,20.5,11.02,41.53,15.38,97.06,115.79,88.89,0,0
+DL Chahar,123,89,17,89,6,8,33,8,138.2,15.38,15.73,37.08,7.24,0.0,172.5,114.89,0,0
+DL Vettori,121,112,16,113,11,2,40,7,108.04,17.29,11.61,35.71,7.56,,96.77,122.0,0,0
+NK Patel,121,119,6,119,14,1,56,5,101.68,24.2,12.61,47.06,20.17,78.79,108.57,172.22,1,0
+AC Blizzard,120,90,7,90,21,2,47,7,133.33,17.14,25.56,52.22,17.14,129.41,200.0,,1,0
+TG Southee,120,106,19,107,8,4,45,11,113.21,10.91,11.32,42.45,6.32,,55.17,135.06,0,0
+Arshad Khan,119,88,10,88,5,9,37,6,135.23,19.83,15.91,42.05,11.9,,120.59,144.44,1,0
+RJ Harris,117,111,21,112,6,3,46,12,105.41,9.75,8.11,41.44,5.57,,81.58,117.81,0,0
+Misbah-ul-Haq,117,81,8,81,10,6,30,9,144.44,13.0,19.75,37.04,14.62,131.82,109.09,266.67,0,0
+Z Khan,117,140,27,141,11,2,74,15,83.57,7.8,9.29,52.86,4.33,0.0,40.0,104.12,0,0
+KK Cooper,116,67,12,68,9,8,27,9,173.13,12.89,25.37,40.3,9.67,122.22,152.0,203.03,0,0
+Aman Hakim Khan,115,104,10,104,8,6,50,9,110.58,12.78,13.46,48.08,11.5,80.0,101.43,137.93,1,0
+DJM Short,115,99,7,99,11,5,45,7,116.16,16.43,16.16,45.45,16.43,83.82,187.1,,0,0
+Mohammed Shami,115,112,33,112,9,5,55,18,102.68,6.39,12.5,49.11,3.48,,94.44,104.26,0,0
+M Kartik,113,107,14,108,7,1,31,7,105.61,16.14,7.48,28.97,8.07,,110.26,102.94,0,0
+Mohammed Siraj,112,126,22,128,10,4,70,11,88.89,10.18,11.11,55.56,5.09,,69.23,91.15,0,0
+MA Starc,111,120,21,120,11,0,51,10,92.5,11.1,9.17,42.5,5.29,,100.0,88.31,0,0
+DJ Harris,111,101,4,101,11,5,50,3,109.9,37.0,15.84,49.5,27.75,89.04,164.29,,0,0
+LJ Wright,106,59,5,60,16,3,19,4,179.66,26.5,32.2,32.2,21.2,100.0,125.0,240.0,0,0
+JDP Oram,106,107,11,108,6,5,47,8,99.07,13.25,10.28,43.93,9.64,,77.78,142.86,0,0
+DS Kulkarni,104,108,20,108,7,2,45,9,96.3,11.56,8.33,41.67,5.2,,41.18,106.59,0,0
+RJ Quiney,103,102,7,102,12,3,54,7,100.98,14.71,14.71,52.94,14.71,89.47,134.62,,1,0
+JG Bethell,101,60,4,60,13,5,24,4,168.33,25.25,30.0,40.0,25.25,176.0,130.0,,1,0
+SD Chitnis,99,88,8,89,10,2,35,7,112.5,14.14,13.64,39.77,12.38,58.33,117.39,126.67,0,0
+Azmatullah Omarzai,99,76,9,76,8,5,29,8,130.26,12.38,17.11,38.16,11.0,,126.67,143.75,0,0
+MS Gony,99,70,15,71,6,8,33,9,141.43,11.0,20.0,47.14,6.6,,90.91,164.58,0,0
+CJ Ferguson,98,117,8,117,9,0,56,6,83.76,16.33,7.69,47.86,12.25,68.75,85.51,106.25,0,0
+MJ Clarke,98,94,6,94,12,0,41,5,104.26,19.6,12.77,43.62,16.33,107.89,88.89,,0,0
+PN Mankad,97,72,5,73,13,2,29,3,134.72,32.33,20.83,40.28,19.4,117.5,163.64,140.0,1,0
+PHKD Mendis,92,72,5,72,7,2,25,4,127.78,23.0,12.5,34.72,18.4,55.56,164.86,100.0,0,0
+PR Shah,92,90,9,91,9,2,44,8,102.22,11.5,12.22,48.89,10.22,97.3,100.0,118.75,0,0
+JDS Neesham,92,93,10,93,6,2,40,8,98.92,11.5,8.6,43.01,9.2,,105.26,88.89,0,0
+DJ Jacobs,92,98,7,98,10,4,59,7,93.88,13.14,14.29,60.2,13.14,90.32,160.0,,0,0
+AJ Tye,91,75,13,76,6,5,35,8,121.33,11.38,14.67,46.67,7.0,,142.31,110.2,0,0
+RT Ponting,91,128,9,128,5,2,68,8,71.09,11.38,5.47,53.12,10.11,67.05,80.0,,0,0
+S Rana,91,79,8,81,9,1,32,5,115.19,18.2,12.66,40.51,11.38,87.5,64.29,148.78,0,0
+AD Nath,90,98,10,98,7,2,44,10,91.84,9.0,9.18,44.9,9.0,76.47,96.83,88.89,0,0
+Iqbal Abdulla,88,82,13,84,9,1,33,1,107.32,88.0,12.2,40.24,6.77,,90.62,118.0,0,0
+SL Malinga,88,99,20,99,6,5,55,12,88.89,7.33,11.11,55.56,4.4,,80.0,89.89,0,0
+AP Majumdar,87,76,4,76,7,2,27,4,114.47,21.75,11.84,35.53,21.75,126.32,110.53,,0,0
+Ankit Sharma,87,67,10,67,7,4,27,7,129.85,12.43,16.42,40.3,8.7,116.67,186.67,110.71,0,0
+TA Boult,86,84,24,84,5,3,37,9,102.38,9.56,9.52,44.05,3.58,,100.0,102.99,0,0
+MJ McClenaghan,85,69,19,70,5,7,36,11,123.19,7.73,17.39,52.17,4.47,0.0,136.36,119.57,0,0
+RE Levi,83,73,6,73,10,4,40,6,113.7,13.83,19.18,54.79,13.83,115.62,100.0,,1,0
+NM Coulter-Nile,82,72,16,72,7,4,36,10,113.89,8.2,15.28,50.0,5.12,,73.68,128.3,0,0
+FH Allen,81,44,5,44,13,4,22,5,184.09,16.2,38.64,50.0,16.2,184.09,,,0,0
+Shahid Afridi,81,46,9,46,7,6,20,8,176.09,10.12,28.26,43.48,9.0,226.32,94.12,220.0,0,0
+CJ Jordan,81,77,12,77,3,3,34,9,105.19,9.0,7.79,44.16,6.75,,60.0,121.05,0,0
+PWH de Silva,81,88,19,88,7,1,41,15,92.05,5.4,9.09,46.59,4.26,,80.85,104.88,0,0
+LA Carseldine,81,66,5,68,11,0,27,3,122.73,27.0,16.67,40.91,16.2,110.26,117.65,180.0,0,0
+WPUJC Vaas,81,72,11,73,2,3,24,7,112.5,11.57,6.94,33.33,7.36,,108.33,114.58,0,0
+RV Patel,80,73,7,73,6,2,26,5,109.59,16.0,10.96,35.62,11.43,,86.11,132.43,0,0
+AD Mascarenhas,79,78,11,78,5,1,28,10,101.28,7.9,7.69,35.9,7.18,200.0,91.49,110.34,0,0
+CR Woakes,78,77,12,77,7,2,33,5,101.3,15.6,11.69,42.86,6.5,,86.67,110.64,0,0
+JR Philippe,78,77,5,77,9,1,35,4,101.3,19.5,12.99,45.45,15.6,100.0,114.29,50.0,0,0
+KA Jamieson,77,74,10,74,6,4,40,4,104.05,19.25,13.51,54.05,7.7,,76.19,140.62,0,0
+IR Jaggi,76,97,7,97,6,0,48,5,78.35,15.2,6.19,49.48,10.86,61.7,94.59,92.31,0,0
+B Akhil,76,55,11,55,5,5,24,7,138.18,10.86,18.18,43.64,6.91,,108.0,163.33,0,0
+TR Birt,75,57,5,58,9,2,25,5,131.58,15.0,19.3,43.86,15.0,20.0,95.83,182.14,0,0
+JJ Bumrah,75,86,29,87,6,1,39,8,87.21,9.38,8.14,45.35,2.59,,137.5,82.05,0,0
+RR Sarwan,73,75,4,75,6,1,31,3,97.33,24.33,9.33,41.33,18.25,106.38,82.14,,0,0
+M Klinger,73,75,4,77,9,0,34,4,97.33,18.25,12.0,45.33,18.25,96.61,100.0,,0,0
+LH Ferguson,72,46,8,47,7,2,12,4,156.52,18.0,19.57,26.09,9.0,,60.0,168.29,0,0
+Urvil Patel,72,37,4,37,6,6,15,4,194.59,18.0,32.43,40.54,18.0,176.92,236.36,,0,0
+SK Rasheed,71,67,5,67,9,2,38,5,105.97,14.2,16.42,56.72,14.2,107.81,66.67,,0,0
+J Suchith,70,61,9,61,6,3,29,4,114.75,17.5,14.75,47.54,7.78,,68.57,176.92,0,0
+AA Bilakhia,69,84,7,85,5,0,39,5,82.14,13.8,5.95,46.43,9.86,71.05,42.11,125.93,0,0
+Vivrant Sharma,69,47,1,47,9,2,16,1,146.81,69.0,23.4,34.04,69.0,111.54,190.48,,1,0
+Avesh Khan,68,46,16,46,7,4,22,3,147.83,22.67,23.91,47.83,4.25,,300.0,133.33,0,0
+RR Powar,67,64,9,64,6,1,27,3,104.69,22.33,10.94,42.19,7.44,,27.27,120.75,0,0
+HR Shokeen,66,65,5,65,9,0,31,3,101.54,22.0,13.85,47.69,13.2,12.5,111.9,120.0,0,0
+R Sharma,66,75,19,75,5,3,46,11,88.0,6.0,10.67,61.33,3.47,,150.0,80.6,0,0
+WD Parnell,65,80,13,80,4,1,37,10,81.25,6.5,6.25,46.25,5.0,78.57,76.47,83.67,0,0
+AS Roy,65,53,12,53,5,2,20,7,122.64,9.29,13.21,37.74,5.42,80.0,72.22,160.0,0,0
+Mohammad Hafeez,64,83,8,83,7,2,50,8,77.11,8.0,10.84,60.24,8.0,75.68,73.81,125.0,0,0
+S Arora,63,45,6,45,4,5,19,5,140.0,12.6,20.0,42.22,10.5,,136.36,141.18,0,0
+Anirudh Singh,63,66,4,66,6,1,31,4,95.45,15.75,10.61,46.97,15.75,90.0,97.3,100.0,0,0
+A Flintoff,62,53,3,53,5,2,20,2,116.98,31.0,13.21,37.74,20.67,107.14,96.15,169.23,0,0
+SP Jackson,61,57,8,57,5,1,20,6,107.02,10.17,10.53,35.09,7.62,111.11,114.29,95.0,0,0
+Sandeep Sharma,60,75,26,76,4,0,31,5,80.0,12.0,5.33,41.33,2.31,,80.0,80.0,0,0
+S Aravind,59,57,10,57,7,0,24,3,103.51,19.67,12.28,42.11,5.9,,81.25,112.2,0,0
+Harshit Rana,59,57,10,57,5,3,33,7,103.51,8.43,14.04,57.89,5.9,,131.82,85.71,0,0
+C Bosch,58,36,3,36,5,3,12,2,161.11,29.0,22.22,33.33,19.33,,40.0,180.65,0,0
+Kartik Sharma,58,51,5,51,3,4,26,5,113.73,11.6,13.73,50.98,11.6,225.0,100.0,115.38,0,0
+MG Bracewell,58,47,4,47,6,1,15,2,123.4,29.0,14.89,31.91,14.5,66.67,143.33,100.0,0,0
+I Sharma,57,69,21,69,4,2,37,7,82.61,8.14,8.7,53.62,2.71,,81.25,83.02,0,0
+Vishnu Vinod,56,56,6,57,3,3,26,6,100.0,9.33,10.71,46.43,9.33,47.62,127.59,150.0,0,0
+WA Mota,56,75,8,75,2,0,29,5,74.67,11.2,2.67,38.67,7.0,,70.49,92.86,0,0
+A Kamboj,55,47,9,47,4,2,17,2,117.02,27.5,12.77,36.17,6.11,,100.0,117.78,0,0
+M Rawat,55,69,11,69,4,1,33,5,79.71,11.0,7.25,47.83,5.0,0.0,66.67,94.59,0,0
+DJ Willey,53,62,5,62,7,0,34,3,85.48,17.67,11.29,54.84,10.6,64.29,74.19,123.53,0,0
+A Chopra,53,71,6,71,7,0,41,5,74.65,10.6,9.86,57.75,8.83,67.44,85.71,,0,0
+PVD Chameera,53,45,11,45,3,3,19,3,117.78,17.67,13.33,42.22,4.82,,100.0,120.0,0,0
+TU Deshpande,53,34,9,34,2,4,12,3,155.88,17.67,17.65,35.29,5.89,,,155.88,0,0
+RP Singh,52,76,28,76,2,1,39,15,68.42,3.47,3.95,51.32,1.86,,20.0,75.76,0,0
+Shoaib Malik,52,46,5,47,5,0,14,4,113.04,13.0,10.87,30.43,10.4,116.67,120.83,50.0,0,0
+Swapnil Singh,51,45,9,45,3,3,20,5,113.33,10.2,13.33,44.44,5.67,,96.15,136.84,0,0
+Shivam Mavi,51,56,11,56,4,2,30,9,91.07,5.67,10.71,53.57,4.64,,33.33,102.13,0,0
+KB Arun Karthik,51,51,8,51,4,1,22,5,100.0,10.2,9.8,43.14,6.38,,118.18,86.21,0,0
+OF Smith,51,44,6,44,1,5,23,3,115.91,17.0,13.64,52.27,8.5,,41.67,143.75,0,0
+R Rampaul,51,49,7,50,3,2,24,5,104.08,10.2,10.2,48.98,7.29,,104.76,103.57,0,0
+RV Gomez,50,51,9,51,5,1,25,7,98.04,7.14,11.76,49.02,5.56,0.0,40.0,125.71,0,0
+VR Aaron,50,71,12,72,2,2,42,4,70.42,12.5,5.63,59.15,4.17,,20.0,74.24,0,0
+SB Bangar,49,58,7,58,1,3,28,6,84.48,8.17,6.9,48.28,7.0,,48.78,170.59,0,0
+A Nortje,49,50,14,50,6,0,24,6,98.0,8.17,12.0,48.0,3.5,,50.0,104.55,0,0
+PR Veer,49,36,2,36,7,1,13,1,136.11,49.0,22.22,36.11,24.5,,153.57,75.0,0,0
+AG Paunikar,49,55,5,58,9,0,36,5,89.09,9.8,16.36,65.45,9.8,89.09,,,0,0
+AS Yadav,49,39,6,39,5,2,17,7,125.64,7.0,17.95,43.59,8.17,,100.0,134.48,0,0
+T Kohler-Cadmore,48,54,3,54,7,1,33,3,88.89,16.0,14.81,61.11,16.0,90.57,0.0,,0,0
+M Markande,48,42,10,42,5,1,18,3,114.29,16.0,14.29,42.86,4.8,,42.86,128.57,0,0
+Yashpal Singh,47,65,4,66,5,0,37,4,72.31,11.75,7.69,56.92,11.75,48.65,100.0,110.0,0,0
+Ravi Bishnoi,45,69,18,69,2,2,44,11,65.22,4.09,5.8,63.77,2.5,,62.5,66.67,0,0
+TL Seifert,45,41,6,41,6,1,22,6,109.76,7.5,17.07,53.66,7.5,116.22,,50.0,0,0
+DR Sams,44,44,13,44,1,3,22,10,100.0,4.4,9.09,50.0,3.38,16.67,95.0,133.33,0,0
+Sunny Singh,43,31,5,31,6,1,12,4,138.71,10.75,22.58,38.71,8.6,160.0,166.67,77.78,0,0
+AB Barath,42,42,3,42,5,1,21,2,100.0,21.0,14.29,50.0,14.0,111.11,105.26,40.0,0,0
+SK Trivedi,42,58,14,59,3,1,34,8,72.41,5.25,6.9,58.62,3.0,,40.0,75.47,0,0
+A Nehra,41,60,17,62,3,1,36,8,68.33,5.12,6.67,60.0,2.41,,166.67,63.16,0,0
+J Yadav,40,36,4,36,2,1,12,2,111.11,20.0,8.33,33.33,10.0,,125.0,104.17,0,0
+Rasikh Salam,40,40,6,40,5,0,19,5,100.0,8.0,12.5,47.5,6.67,,61.9,142.11,0,0
+BR Dunk,40,35,3,35,7,0,19,3,114.29,13.33,20.0,54.29,13.33,114.29,,,0,0
+DJ Thornely,39,53,4,53,2,2,30,3,73.58,13.0,7.55,56.6,9.75,28.57,124.0,,0,0
+MM Patel,39,41,12,41,5,0,18,5,95.12,7.8,12.2,43.9,3.25,,0.0,105.41,0,0
+S Nadeem,39,87,20,87,2,0,57,16,44.83,2.44,2.3,65.52,1.95,,45.71,44.23,0,0
+YS Chahal,37,86,16,86,0,0,54,6,43.02,6.17,0.0,62.79,2.31,,20.0,52.46,0,0
+Anureet Singh,36,47,8,47,2,1,22,4,76.6,9.0,6.38,46.81,4.5,,58.33,82.86,0,0
+Joginder Sharma,36,30,5,30,1,2,10,4,120.0,9.0,10.0,33.33,7.2,,100.0,126.09,0,0
+L Balaji,36,49,13,49,2,1,28,7,73.47,5.14,6.12,57.14,2.77,,44.44,80.0,0,0
+KW Richardson,36,39,4,39,2,1,17,3,92.31,12.0,7.69,43.59,9.0,,104.35,75.0,0,0
+M Ashwin,35,50,12,50,2,1,27,8,70.0,4.38,6.0,54.0,2.92,,38.89,87.5,0,0
+B Sumanth,35,37,4,37,3,0,14,1,94.59,35.0,8.11,37.84,8.75,,75.0,109.52,0,0
+A Kumble,35,47,15,47,3,0,22,2,74.47,17.5,6.38,46.81,2.33,,75.0,74.36,0,0
+L Ronchi,34,34,5,34,6,1,23,3,100.0,11.33,20.59,67.65,6.8,100.0,,,0,0
+S Sreesanth,34,55,12,55,6,0,41,3,61.82,11.33,10.91,74.55,2.83,,0.0,62.96,0,0
+Imran Tahir,33,37,8,37,5,0,21,4,89.19,8.25,13.51,56.76,4.12,,40.0,107.41,0,0
+Navdeep Saini,33,37,7,37,3,0,16,4,89.19,8.25,8.11,43.24,4.71,0.0,50.0,112.5,0,0
+Basil Thampi,32,35,7,35,1,1,14,2,91.43,16.0,5.71,40.0,4.57,,50.0,100.0,0,0
+RJ Peterson,32,30,5,30,3,1,13,3,106.67,10.67,13.33,43.33,6.4,,93.33,120.0,0,0
+S Sriram,31,36,2,36,3,0,16,2,86.11,15.5,8.33,44.44,15.5,116.67,70.83,,0,0
+Arshdeep Singh,31,46,13,46,4,0,31,6,67.39,5.17,8.7,67.39,2.38,,66.67,67.5,0,0
+NS Naik,31,50,4,50,2,0,28,4,62.0,7.75,4.0,56.0,7.75,43.75,72.22,68.75,0,0
+G Coetzee,31,33,7,33,2,2,18,6,93.94,5.17,12.12,54.55,4.43,,83.33,100.0,0,0
+Noor Ahmad,30,54,14,54,2,1,37,10,55.56,3.0,5.56,68.52,2.14,,18.75,71.05,0,0
+AS Joseph,27,32,5,32,3,0,16,1,84.38,27.0,9.38,50.0,5.4,,110.0,72.73,0,0
+AS Rajpoot,26,41,7,41,2,1,27,4,63.41,6.5,7.32,65.85,3.71,,16.67,71.43,0,0
+PJ Sangwan,26,44,14,44,1,0,24,8,59.09,3.25,2.27,54.55,1.86,,44.44,62.86,0,0
+AB Dinda,26,48,16,48,2,0,29,9,54.17,2.89,4.17,60.42,1.62,,72.73,48.65,0,0
+CV Varun,26,48,12,48,2,0,29,3,54.17,8.67,4.17,60.42,2.17,,11.11,64.1,0,0
+S Chanderpaul,25,31,3,31,4,0,18,3,80.65,8.33,12.9,58.06,8.33,80.65,,,0,0
+Tanush Kotian,24,31,1,31,3,0,17,1,77.42,24.0,9.68,54.84,24.0,76.0,83.33,,0,0
+P Dubey,23,33,2,33,2,0,17,1,69.7,23.0,6.06,51.52,11.5,,84.21,50.0,0,0
+KL Nagarkoti,22,33,7,33,1,0,16,4,66.67,5.5,3.03,48.48,3.14,,46.67,83.33,0,0
+B Indrajith,21,30,3,30,2,0,17,3,70.0,7.0,6.67,56.67,7.0,72.41,0.0,,0,0
+S Kaul,20,36,9,36,1,0,23,3,55.56,6.67,2.78,63.89,2.22,,40.0,58.06,0,0
+C Sakariya,20,30,7,31,3,0,20,5,66.67,4.0,10.0,66.67,2.86,,20.0,76.0,0,0
+PV Tambe,18,38,6,39,1,0,23,1,47.37,18.0,2.63,60.53,3.0,,60.0,42.86,0,0
+M Theekshana,17,34,6,34,0,1,22,3,50.0,5.67,2.94,64.71,2.83,,16.67,68.18,0,0
+PP Ojha,16,44,19,45,0,0,29,10,36.36,1.6,0.0,65.91,0.84,,0.0,39.02,0,0

data/processed/bowling_features.csv

@@ -0,0 +1,403 @@
+bowler,balls_bowled,runs_conceded,wickets,dot_balls,wides,no_balls,matches_bowled,fours_conceded,sixes_conceded,economy_rate,bowling_average,bowling_sr,dot_ball_pct,wickets_per_match,economy_powerplay,economy_middle,economy_death,three_wicket_haul,five_wicket_haul
+YS Chahal,3961,5355,237,1370,98,9,179,308,264,8.11,22.59,16.71,34.59,1.32,7.52,8.02,9.91,25,1
+B Kumar,4422,5780,228,1937,138,12,198,579,177,7.84,25.35,19.39,43.8,1.15,6.7,8.42,9.66,21,2
+SP Narine,4521,5213,221,1794,61,4,194,331,196,6.92,23.59,20.46,39.68,1.14,6.71,6.69,7.96,18,3
+DJ Bravo,3120,4436,207,1054,167,9,158,349,155,8.53,21.43,15.07,33.78,1.31,7.53,7.82,9.91,21,0
+JJ Bumrah,3531,4374,206,1501,77,33,152,384,130,7.43,21.23,17.14,42.51,1.36,6.89,6.82,8.47,31,2
+R Ashwin,4713,5721,205,1666,151,4,217,334,221,7.28,27.91,22.99,35.35,0.94,6.93,7.26,9.06,10,1
+PP Chawla,3850,5179,201,1358,43,2,191,374,222,8.07,25.77,19.15,35.27,1.05,7.68,7.99,10.02,15,0
+SL Malinga,2828,3486,188,1218,128,18,122,314,86,7.4,18.54,15.04,43.07,1.54,6.51,7.74,8.19,24,2
+RA Jadeja,4177,5385,185,1381,61,5,232,320,229,7.74,29.11,22.58,33.06,0.8,7.32,7.66,9.2,18,1
+A Mishra,3371,4193,183,1220,52,21,162,239,184,7.46,22.91,18.42,36.19,1.13,6.88,7.31,9.85,20,1
+Rashid Khan,3387,4126,174,1273,29,0,144,261,175,7.31,23.71,19.47,37.58,1.21,7.15,7.14,8.59,18,1
+Sandeep Sharma,3174,4316,169,1268,91,12,141,408,153,8.16,25.54,18.78,39.95,1.2,7.11,8.29,10.71,17,1
+HV Patel,2495,3747,168,874,106,16,119,317,148,9.01,22.3,14.85,35.03,1.41,8.87,8.32,10.32,20,2
+UT Yadav,3057,4442,163,1262,110,23,147,473,151,8.72,27.25,18.75,41.28,1.11,7.95,8.4,11.11,21,0
+Harbhajan Singh,3416,4101,161,1312,76,4,160,296,145,7.2,25.47,21.22,38.41,1.01,6.81,7.27,8.75,15,1
+Mohammed Shami,2823,4090,157,1208,79,7,127,423,154,8.69,26.05,17.98,42.79,1.24,7.78,8.78,11.05,15,0
+TA Boult,2758,3916,152,1176,85,2,122,435,134,8.52,25.76,18.14,42.64,1.25,7.43,9.26,10.72,13,0
+MM Sharma,2416,3537,149,847,48,7,119,342,131,8.78,23.74,16.21,35.06,1.25,8.03,8.17,10.38,19,2
+AR Patel,3471,4342,144,1178,47,0,168,274,179,7.51,30.15,24.1,33.94,0.86,7.38,7.45,8.58,4,0
+K Rabada,2111,3112,141,873,67,11,92,284,136,8.85,22.07,14.97,41.35,1.53,8.56,8.38,9.82,17,0
+AD Russell,1811,2909,133,648,64,10,121,274,130,9.64,21.87,13.62,35.78,1.1,8.48,9.41,11.14,14,2
+R Vinay Kumar,2127,3041,127,801,48,11,104,296,100,8.58,23.94,16.75,37.66,1.22,7.3,8.45,10.7,15,1
+JD Unadkat,2355,3541,125,788,62,1,115,295,158,9.02,28.33,18.84,33.46,1.09,7.93,8.48,11.57,14,2
+Mohammed Siraj,2505,3670,121,1121,115,8,116,363,157,8.79,30.33,20.7,44.75,1.04,8.21,8.37,10.33,11,0
+SN Thakur,2190,3474,121,782,103,10,107,331,138,9.52,28.71,18.1,35.71,1.13,9.28,8.65,11.02,10,0
+A Nehra,1908,2537,121,828,57,9,88,279,76,7.98,20.97,15.77,43.4,1.38,7.57,7.97,9.06,18,1
+Z Khan,2200,2860,119,929,74,2,99,312,64,7.8,24.03,18.49,42.23,1.2,6.88,9.26,9.08,12,0
+Arshdeep Singh,1931,2954,114,735,84,5,89,318,92,9.18,25.91,16.94,38.06,1.28,8.79,9.22,9.79,13,1
+Kuldeep Yadav,2211,3043,113,690,38,2,103,155,164,8.26,26.93,19.57,31.21,1.1,7.67,8.29,8.54,13,0
+CV Varun,2028,2619,109,776,28,6,89,180,119,7.75,24.03,18.61,38.26,1.22,7.88,7.43,9.41,10,1
+SR Watson,2029,2742,107,850,95,13,105,293,74,8.11,25.63,18.96,41.89,1.02,7.19,7.6,10.35,12,0
+CH Morris,1726,2377,107,708,70,5,81,222,79,8.26,22.21,16.13,41.02,1.32,7.81,8.26,8.83,12,1
+KH Pandya,2571,3265,106,845,58,3,140,219,117,7.62,30.8,24.25,32.87,0.76,7.57,7.55,8.83,11,0
+DW Steyn,2182,2583,105,1064,93,7,95,284,55,7.1,24.6,20.78,48.76,1.11,6.43,6.72,8.93,11,0
+P Kumar,2524,3342,102,1147,112,1,119,338,104,7.94,32.76,24.75,45.44,0.86,6.88,9.33,10.44,6,0
+RP Singh,1775,2417,100,790,86,13,82,254,70,8.17,24.17,17.75,44.51,1.22,7.38,8.74,9.55,12,1
+I Sharma,2426,3465,100,1077,97,23,117,391,102,8.57,34.65,24.26,44.39,0.85,7.58,9.63,11.46,6,1
+IK Pathan,2043,2711,99,885,69,1,101,293,82,7.96,27.38,20.64,43.32,0.98,7.21,8.14,9.56,8,0
+Avesh Khan,1733,2672,99,638,51,4,80,266,109,9.25,26.99,17.51,36.81,1.24,9.35,8.22,10.07,12,0
+PP Ojha,1899,2399,99,708,44,2,90,146,97,7.58,24.23,19.18,37.28,1.1,7.26,7.44,9.42,8,0
+KK Ahmed,1672,2521,96,735,64,5,76,255,113,9.05,26.26,17.42,43.96,1.26,8.33,8.58,11.23,12,0
+JA Morkel,1723,2409,96,683,71,13,87,264,56,8.39,25.09,17.95,39.64,1.1,8.62,7.15,9.64,10,0
+M Prasidh Krishna,1678,2516,94,709,63,16,73,265,94,9.0,26.77,17.85,42.25,1.29,8.3,8.14,11.04,10,1
+DL Chahar,1974,2708,92,866,72,5,98,311,81,8.23,29.43,21.46,43.87,0.94,8.0,8.47,10.08,7,1
+DS Kulkarni,1787,2513,91,765,82,7,92,272,81,8.44,27.62,19.64,42.81,0.99,7.64,8.92,10.94,9,0
+KV Sharma,1586,2263,89,540,25,0,87,125,132,8.56,25.43,17.82,34.05,1.02,7.0,8.79,10.88,9,1
+Ravi Bishnoi,1796,2494,89,634,44,5,84,181,114,8.33,28.02,20.18,35.3,1.06,7.73,8.18,11.13,6,1
+M Morkel,1629,2136,88,738,57,13,70,230,61,7.87,24.27,18.51,45.3,1.26,7.4,7.56,9.48,8,0
+HH Pandya,1747,2732,87,630,62,5,113,243,123,9.38,31.4,20.08,36.06,0.77,9.5,8.58,12.49,9,1
+Imran Tahir,1316,1729,86,451,23,1,59,97,83,7.88,20.1,15.3,34.27,1.46,8.04,7.7,8.61,13,0
+L Balaji,1512,2083,85,582,58,4,73,188,69,8.27,24.51,17.79,38.49,1.16,7.05,7.68,10.52,8,1
+PJ Cummins,1655,2463,83,640,50,1,73,228,101,8.93,29.67,19.94,38.67,1.14,8.74,7.9,10.79,8,0
+R Bhatia,1636,2059,82,467,23,2,91,120,68,7.55,25.11,19.95,28.55,0.9,7.19,7.43,8.83,8,0
+MM Patel,1355,1733,82,612,16,11,63,188,53,7.67,21.13,16.52,45.17,1.3,6.91,6.97,11.04,9,1
+T Natarajan,1525,2317,82,493,63,1,71,206,90,9.12,28.26,18.6,32.33,1.15,8.34,8.53,10.36,9,0
+AB Dinda,1516,2103,82,648,59,14,75,232,58,8.32,25.65,18.49,42.74,1.09,7.4,6.91,11.3,9,0
+KA Pollard,1488,2200,81,422,90,8,107,169,78,8.87,27.16,18.37,28.36,0.76,7.65,8.49,11.43,3,0
+MA Starc,1076,1588,78,460,45,3,50,174,45,8.86,20.36,13.79,42.75,1.56,8.96,7.65,9.28,12,1
+JC Archer,1443,1934,77,677,34,6,61,177,83,8.04,25.12,18.74,46.92,1.26,6.78,7.52,10.23,8,0
+JP Faulkner,1238,1849,76,440,44,5,60,160,67,8.96,24.33,16.29,35.54,1.27,7.93,8.76,10.33,9,2
+RD Chahar,1695,2220,76,597,32,1,79,143,101,7.86,29.21,22.3,35.22,0.96,7.18,7.9,9.09,6,0
+MJ McClenaghan,1274,1839,75,534,63,9,56,181,72,8.66,24.52,16.99,41.92,1.34,8.04,8.57,10.18,9,0
+JH Kallis,1742,2348,74,640,48,9,89,236,58,8.09,31.73,23.54,36.74,0.83,7.53,7.8,10.02,4,0
+Mustafizur Rahman,1365,1879,74,512,47,7,60,171,61,8.26,25.39,18.45,37.51,1.23,7.22,7.61,9.67,10,0
+SK Trivedi,1506,1944,73,528,42,9,75,154,51,7.75,26.63,20.63,35.06,0.97,7.68,7.35,9.76,7,0
+Shakib Al Hasan,1484,1864,71,523,31,0,70,120,78,7.54,26.25,20.9,35.24,1.01,7.08,7.44,10.22,3,0
+M Muralitharan,1528,1765,67,643,51,2,66,105,67,6.93,26.34,22.81,42.08,1.02,6.23,6.86,8.75,5,0
+MG Johnson,1235,1740,66,557,57,9,54,196,55,8.45,26.36,18.71,45.1,1.22,8.02,8.31,9.69,3,0
+JR Hazlewood,992,1377,66,453,17,2,44,138,63,8.33,20.86,15.03,45.67,1.5,7.52,8.14,10.21,10,0
+LH Ferguson,1059,1615,63,418,35,9,50,153,71,9.15,25.63,16.81,39.47,1.26,8.77,8.24,10.98,8,0
+A Nortje,1126,1740,63,459,35,8,49,164,78,9.27,27.62,17.87,40.76,1.29,8.58,8.76,10.6,5,0
+SM Curran,1255,2059,63,416,46,7,63,197,89,9.84,32.68,19.92,33.15,1.0,8.37,10.81,11.39,7,0
+S Kaul,1209,1772,63,410,23,6,55,166,65,8.79,28.13,19.19,33.91,1.15,9.2,8.04,9.26,5,0
+SK Warne,1194,1465,60,442,29,0,54,91,56,7.36,24.42,19.9,37.02,1.11,7.16,7.01,10.05,5,0
+JO Holder,1044,1547,60,364,51,3,48,137,57,8.89,25.78,17.4,34.87,1.25,8.65,8.37,10.18,10,0
+TU Deshpande,1065,1770,59,411,51,6,50,177,76,9.97,30.0,18.05,38.59,1.18,8.9,10.17,11.59,6,0
+TG Southee,1206,1774,57,475,52,4,54,175,62,8.83,31.12,21.16,39.39,1.06,8.11,7.92,10.85,8,1
+Noor Ahmad,987,1355,56,353,20,1,45,90,59,8.24,24.2,17.62,35.76,1.24,8.5,8.14,8.58,6,0
+S Nadeem,1415,1800,54,469,25,4,70,114,73,7.63,33.33,26.2,33.14,0.77,7.63,7.39,12.0,2,0
+M Pathirana,735,1047,53,293,51,1,32,72,39,8.55,19.75,13.87,39.86,1.66,,7.99,9.11,7,0
+S Gopal,991,1358,53,326,8,1,51,77,74,8.22,25.62,18.7,32.9,1.04,7.33,8.56,7.58,5,0
+NM Coulter-Nile,857,1125,52,387,26,9,38,123,34,7.88,21.63,16.48,45.16,1.37,7.24,7.34,9.56,8,0
+Mukesh Kumar,817,1397,51,300,17,4,39,144,66,10.26,27.39,16.02,36.72,1.31,9.27,9.76,12.12,5,0
+SB Jakati,1085,1474,50,355,14,2,57,97,60,8.15,29.48,21.7,32.72,0.88,7.26,8.09,12.68,3,0
+A Kumble,965,1089,49,397,13,5,42,72,35,6.77,22.22,19.69,41.14,1.17,5.43,6.94,8.38,5,2
+VG Arora,834,1321,49,333,25,9,39,142,53,9.5,26.96,17.02,39.93,1.26,8.81,12.15,10.25,3,0
+S Aravind,760,1057,48,302,27,1,38,105,38,8.34,22.02,15.83,39.74,1.26,7.64,8.97,9.86,5,0
+AJ Tye,685,1005,48,225,27,2,30,90,33,8.8,20.94,14.27,32.85,1.6,8.12,8.25,10.06,7,2
+PWH de Silva,803,1134,47,281,5,0,37,65,70,8.47,24.13,17.09,34.99,1.27,7.87,8.58,8.32,3,1
+RJ Harris,832,1085,47,390,32,1,37,119,27,7.82,23.09,17.7,46.88,1.27,6.84,8.08,9.41,7,0
+YK Pathan,1147,1443,46,419,37,0,82,99,57,7.55,31.37,24.93,36.53,0.56,6.7,7.86,12.2,3,0
+VR Aaron,994,1527,46,415,63,8,50,159,52,9.22,33.2,21.61,41.75,0.92,8.65,8.67,12.04,1,0
+MC Henriques,950,1302,46,315,28,1,60,123,38,8.22,28.3,20.65,33.16,0.77,7.59,8.39,10.36,3,0
+MM Ali,857,1036,46,305,11,1,57,43,57,7.25,22.52,18.63,35.59,0.81,5.5,7.6,6.6,4,0
+MP Stoinis,909,1505,46,271,27,1,73,116,76,9.93,32.72,19.76,29.81,0.63,8.31,9.69,13.27,4,0
+GJ Maxwell,1026,1422,45,341,21,0,85,79,80,8.32,31.6,22.8,33.24,0.53,8.21,8.36,8.54,0,0
+Yash Dayal,883,1412,45,329,32,5,43,139,58,9.59,31.38,19.62,37.26,1.05,9.45,8.18,11.32,3,0
+Iqbal Abdulla,920,1125,45,346,23,0,48,88,37,7.34,25.0,20.44,37.61,0.94,6.47,7.61,9.51,5,0
+PJ Sangwan,856,1267,44,338,43,6,42,127,43,8.88,28.8,19.45,39.49,1.05,8.8,8.29,11.36,4,0
+M Jansen,920,1442,43,374,29,7,42,130,67,9.4,33.53,21.4,40.65,1.02,9.07,9.44,10.57,3,0
+P Awana,747,1046,43,307,23,4,33,107,35,8.4,24.33,17.37,41.1,1.3,8.17,7.13,11.22,4,0
+DE Bollinger,576,716,43,270,22,2,27,73,19,7.46,16.65,13.4,46.88,1.59,7.48,6.29,7.66,6,0
+S Sreesanth,880,1221,43,416,44,23,44,130,39,8.33,28.4,20.47,47.27,0.98,7.65,8.35,12.0,2,0
+Harshit Rana,661,1047,42,272,16,2,32,99,56,9.5,24.93,15.74,41.15,1.31,9.43,9.28,9.99,4,0
+Washington Sundar,1152,1498,42,392,8,2,66,99,69,7.8,35.67,27.43,34.03,0.64,8.08,7.34,11.48,4,0
+R Sharma,928,1100,42,344,7,0,44,70,45,7.11,26.19,22.1,37.07,0.95,6.58,7.0,9.15,1,0
+L Ngidi,520,747,40,205,16,2,22,65,34,8.62,18.68,13.0,39.42,1.82,7.99,8.53,9.4,7,0
+DT Christian,883,1218,40,316,36,3,49,103,39,8.28,30.45,22.08,35.79,0.82,8.03,7.22,11.78,0,0
+WD Parnell,723,968,40,312,29,6,33,102,24,8.03,24.2,18.08,43.15,1.21,7.9,7.78,8.51,5,0
+MS Gony,888,1317,39,366,32,5,44,135,53,8.9,33.77,22.77,41.22,0.89,8.79,8.33,11.69,3,0
+M Kartik,1149,1418,39,405,28,5,55,97,41,7.4,36.36,29.46,35.25,0.71,6.68,7.56,8.32,1,0
+Yuvraj Singh,869,1091,39,269,12,1,73,59,41,7.53,27.97,22.28,30.96,0.53,6.6,7.54,8.69,5,0
+DP Nannes,646,815,38,306,39,4,29,89,15,7.57,21.45,17.0,47.37,1.31,6.57,8.79,8.71,5,0
+P Negi,716,954,38,249,17,0,42,57,47,7.99,25.11,18.84,34.78,0.9,7.25,8.02,10.19,3,0
+MR Marsh,560,803,37,189,30,3,34,72,28,8.6,21.7,15.14,33.75,1.09,7.71,7.83,12.49,3,0
+NLTC Perera,698,1031,37,238,14,3,36,113,28,8.86,27.86,18.86,34.1,1.03,7.28,8.41,11.49,5,0
+M Markande,751,1157,37,224,16,1,40,78,66,9.24,31.27,20.3,29.83,0.92,8.27,9.46,9.2,3,0
+KK Cooper,576,789,36,209,23,1,25,59,23,8.22,21.92,16.0,36.28,1.44,7.11,7.81,9.07,5,0
+CJ Jordan,674,1111,36,225,38,5,34,96,46,9.89,30.86,18.72,33.38,1.06,9.48,7.88,12.22,5,0
+Mohsin Khan,584,806,36,263,21,0,27,67,41,8.28,22.39,16.22,45.03,1.33,8.0,6.9,10.08,4,1
+R Tewatia,843,1117,36,270,21,2,52,54,61,7.95,31.03,23.42,32.03,0.69,6.53,8.24,6.46,4,0
+M Theekshana,895,1226,36,292,7,0,38,93,45,8.22,34.06,24.86,32.63,0.95,8.56,7.9,8.54,2,0
+Harpreet Brar,837,1122,35,288,13,0,47,76,49,8.04,32.06,23.91,34.41,0.74,8.38,7.6,11.39,4,0
+M Ashwin,870,1182,35,298,25,3,44,82,50,8.15,33.77,24.86,34.25,0.8,7.65,8.33,7.92,2,0
+DL Vettori,777,894,34,283,8,0,34,67,24,6.9,26.29,22.85,36.42,1.0,5.92,6.79,9.1,3,1
+MF Maharoof,420,532,33,177,16,5,20,46,16,7.6,16.12,12.73,42.14,1.65,7.55,6.35,9.79,5,0
+AB Agarkar,782,1174,33,273,36,2,42,113,41,9.01,35.58,23.7,34.91,0.79,8.88,8.64,10.1,3,0
+Shivam Mavi,649,958,33,267,32,6,32,92,38,8.86,29.03,19.67,41.14,1.03,8.13,7.73,11.41,1,0
+R Sai Kishore,448,655,32,141,5,0,25,35,40,8.77,20.47,14.0,31.47,1.28,7.83,8.39,11.07,2,0
+A Zampa,469,656,32,145,6,0,22,36,35,8.39,20.5,14.66,30.92,1.45,7.14,7.64,13.52,3,1
+BA Stokes,689,1023,31,250,30,4,38,99,33,8.91,33.0,22.23,36.28,0.82,8.64,8.24,10.56,3,0
+Harmeet Singh,549,747,31,213,21,4,28,69,22,8.16,24.1,17.71,38.8,1.11,8.74,7.82,8.67,3,0
+CR Woakes,440,674,31,173,22,1,21,68,28,9.19,21.74,14.19,39.32,1.48,8.52,7.41,11.21,3,0
+Umran Malik,494,785,31,218,22,3,26,93,29,9.53,25.32,15.94,44.13,1.19,12.92,8.66,10.75,4,2
+A Singh,473,639,31,200,12,1,23,67,17,8.11,20.61,15.26,42.28,1.35,7.0,7.5,10.31,3,0
+MJ Santner,700,870,31,261,3,1,35,74,28,7.46,28.06,22.58,37.29,0.89,8.27,7.02,8.8,1,0
+Azhar Mahmood,537,707,31,206,10,5,23,73,18,7.9,22.81,17.32,38.36,1.35,6.76,7.09,9.96,5,0
+B Lee,875,1126,30,400,28,13,38,123,25,7.72,37.53,29.17,45.71,0.79,7.12,7.19,9.06,1,0
+SK Raina,908,1139,30,299,22,0,69,71,44,7.53,37.97,30.27,32.93,0.43,6.76,7.46,8.97,0,0
+E Malinga,343,501,29,138,0,0,15,47,21,8.76,17.28,11.83,40.23,1.93,10.12,7.79,9.0,5,0
+Yash Thakur,447,782,28,153,25,3,21,64,43,10.5,27.93,15.96,34.23,1.33,9.79,10.46,11.04,4,1
+Vijaykumar Vyshak,471,797,28,149,11,1,22,62,43,10.15,28.46,16.82,31.63,1.27,11.33,9.12,12.0,3,0
+AD Mathews,791,1095,28,237,15,1,44,98,29,8.31,39.11,28.25,29.96,0.64,8.43,8.09,8.88,2,0
+STR Binny,594,763,28,204,16,1,63,76,14,7.71,27.25,21.21,34.34,0.44,7.41,7.59,10.14,1,0
+PV Tambe,660,866,28,215,14,0,33,64,28,7.87,30.93,23.57,32.58,0.85,7.82,7.55,12.5,2,0
+J Botha,694,818,27,268,15,0,34,60,24,7.07,30.3,25.7,38.62,0.79,7.57,6.54,8.57,2,0
+DR Smith,539,825,27,179,14,4,46,75,33,9.18,30.56,19.96,33.21,0.59,10.22,8.51,12.88,2,0
+R Dhawan,662,922,27,218,28,3,36,71,27,8.36,34.15,24.52,32.93,0.75,8.14,7.94,11.11,0,0
+Naveen-ul-Haq,388,602,26,141,15,4,17,54,24,9.31,23.15,14.92,36.34,1.53,9.67,7.56,10.32,4,0
+Navdeep Saini,671,1006,26,291,21,9,32,108,39,9.0,38.69,25.81,43.37,0.81,8.6,9.23,9.51,1,0
+Akash Madhwal,359,598,26,119,10,7,17,55,25,9.99,23.0,13.81,33.15,1.53,7.79,10.97,10.29,5,1
+Basil Thampi,521,862,26,163,17,1,25,75,41,9.93,33.15,20.04,31.29,1.04,9.24,9.03,11.81,2,0
+SW Tait,473,668,26,222,52,9,21,71,12,8.47,25.69,18.19,46.93,1.24,8.12,9.03,8.67,4,0
+Suyash Sharma,723,1043,26,255,5,1,34,87,46,8.66,40.12,27.81,35.27,0.76,8.0,8.77,8.05,4,0
+Kartik Tyagi,592,985,25,223,28,8,27,97,44,9.98,39.4,23.68,37.67,0.93,10.97,9.14,10.2,1,0
+RE van der Merwe,443,515,25,159,12,0,21,25,17,6.98,20.6,17.72,35.89,1.19,8.89,6.31,8.95,2,0
+GB Hogg,458,585,25,170,7,1,21,40,21,7.66,23.4,18.32,37.12,1.19,6.54,7.76,8.4,2,0
+Sohail Tanvir,247,275,24,130,17,1,11,27,7,6.68,11.46,10.29,52.63,2.18,6.61,6.0,6.88,6,1
+A Kamboj,364,527,24,160,6,2,19,58,22,8.69,21.96,15.17,43.96,1.26,8.94,8.95,7.7,3,0
+AS Rajpoot,530,844,24,214,28,9,29,89,31,9.55,35.17,22.08,40.38,0.83,9.19,8.61,12.27,2,1
+BW Hilfenhaus,372,497,23,184,15,3,17,48,21,8.02,21.61,16.17,49.46,1.35,6.5,9.75,11.36,1,0
+JP Duminy,678,847,23,242,23,0,49,53,33,7.5,36.83,29.48,35.69,0.47,7.32,7.27,11.17,1,0
+A Symonds,527,694,23,206,12,1,30,44,33,7.9,30.17,22.91,39.09,0.77,6.19,8.09,11.77,1,0
+Shahbaz Ahmed,599,969,22,165,10,2,46,65,53,9.71,44.05,27.23,27.55,0.48,9.9,9.72,8.28,2,0
+WPUJC Vaas,282,364,22,134,4,2,13,40,13,7.74,16.55,12.82,47.52,1.69,6.39,9.5,12.0,3,0
+AS Joseph,434,707,22,172,19,1,22,53,43,9.77,32.14,19.73,39.63,1.0,9.83,9.18,10.96,1,1
+VY Mahesh,339,511,22,127,13,7,17,47,20,9.04,23.23,15.41,37.46,1.29,11.0,7.9,9.29,2,1
+JP Behrendorff,366,563,22,146,13,0,17,55,26,9.23,25.59,16.64,39.89,1.29,8.43,9.46,12.88,3,0
+K Gowtham,588,825,22,216,11,0,35,68,37,8.42,37.5,26.73,36.73,0.63,8.49,8.09,14.5,0,0
+C Green,494,773,22,185,22,1,33,64,39,9.39,35.14,22.45,37.45,0.67,9.57,8.66,12.87,0,0
+Bipul Sharma,426,581,22,139,10,0,28,28,31,8.18,26.41,19.36,32.63,0.79,7.08,8.02,19.5,0,0
+KW Richardson,335,479,21,136,10,0,15,40,23,8.58,22.81,15.95,40.6,1.4,6.9,9.33,11.26,1,0
+RP Meredith,388,621,21,156,25,2,18,63,26,9.6,29.57,18.48,40.21,1.17,8.42,8.97,12.0,1,0
+Prince Yadav,337,476,21,139,0,0,14,49,17,8.47,22.67,16.05,41.25,1.5,7.59,7.48,10.82,3,0
+Mujeeb Ur Rahman,447,626,21,157,18,0,20,42,26,8.4,29.81,21.29,35.12,1.05,7.97,7.99,11.88,1,0
+Anureet Singh,412,636,21,152,11,7,22,65,22,9.26,30.29,19.62,36.89,0.95,7.66,9.9,11.04,2,0
+BB Sran,483,767,20,175,30,3,24,73,29,9.53,38.35,24.15,36.23,0.83,9.25,9.86,9.65,1,0
+C Sakariya,445,649,20,155,29,5,20,53,23,8.75,32.45,22.25,34.83,1.0,6.84,9.33,11.65,2,0
+J Suchith,420,604,20,142,14,0,21,47,26,8.63,30.2,21.0,33.81,0.95,8.05,8.7,13.5,0,0
+Mukesh Choudhary,354,576,20,152,18,1,18,67,24,9.76,28.8,17.7,42.94,1.11,9.02,10.38,13.18,3,0
+NT Ellis,379,553,20,125,8,3,17,46,21,8.75,27.65,18.95,32.98,1.18,6.74,8.48,11.42,2,0
+IC Pandey,462,609,19,223,5,2,24,77,17,7.91,32.05,24.32,48.27,0.79,7.75,7.43,12.5,0,0
+CH Gayle,554,755,19,196,27,3,38,49,27,8.18,39.74,29.16,35.38,0.5,8.45,7.6,9.49,1,0
+A Ashish Reddy,262,400,19,83,6,2,20,26,20,9.16,21.05,13.79,31.68,0.95,15.0,8.28,10.39,1,0
+AD Mascarenhas,308,365,19,130,2,0,13,34,8,7.11,19.21,16.21,42.21,1.46,7.22,6.47,10.29,2,1
+CRD Fernando,234,306,19,107,11,4,10,30,9,7.85,16.11,12.32,45.73,1.9,7.14,6.62,10.0,2,1
+BJ Hodge,234,310,18,79,4,0,20,17,14,7.95,17.22,13.0,33.76,0.9,9.11,7.48,11.0,2,1
+R Rampaul,268,319,17,137,8,2,12,25,14,7.14,18.76,15.76,51.12,1.42,6.28,6.38,11.22,1,0
+Ashwani Kumar,183,295,17,71,0,0,9,27,16,9.67,17.35,10.76,38.8,1.89,10.86,7.45,12.94,2,0
+LR Shukla,314,458,17,117,10,2,27,39,16,8.75,26.94,18.47,37.26,0.63,9.63,8.3,9.69,2,0
+DS Rathi,469,661,17,147,0,0,19,52,25,8.46,38.88,27.59,31.34,0.89,8.03,8.06,10.52,0,0
+D Wiese,296,447,17,86,10,1,15,47,11,9.06,26.29,17.41,29.05,1.13,8.12,9.48,9.6,2,0
+Rasikh Salam,331,549,17,107,10,2,18,48,28,9.95,32.29,19.47,32.33,0.94,8.95,10.64,10.12,3,0
+ST Jayasuriya,294,396,16,86,7,0,21,22,16,8.08,24.75,18.38,29.25,0.76,11.5,7.45,10.71,3,0
+CR Brathwaite,254,384,16,78,9,3,16,37,12,9.07,24.0,15.88,30.71,1.0,6.0,9.34,10.5,2,0
+AC Thomas,315,416,16,133,10,2,15,41,11,7.92,26.0,19.69,42.22,1.07,6.53,8.83,10.12,1,0
+RG Sharma,339,462,16,94,10,0,32,29,14,8.18,28.88,21.19,27.73,0.5,7.62,8.07,10.4,2,0
+VRV Singh,360,549,16,121,7,4,18,58,16,9.15,34.31,22.5,33.61,0.89,9.67,8.82,9.24,2,0
+N Burger,289,468,16,138,3,0,14,44,24,9.72,29.25,18.06,47.75,1.14,10.04,9.84,8.3,0,0
+G Coetzee,277,490,15,106,14,1,14,41,26,10.61,32.67,18.47,38.27,1.07,10.91,10.76,10.11,2,0
+DR Sams,360,528,15,144,27,2,16,41,25,8.8,35.2,24.0,40.0,0.94,7.15,10.42,11.75,2,0
+YA Abdulla,209,311,15,92,12,1,11,32,13,8.93,20.73,13.93,44.02,1.36,6.82,10.67,11.21,3,0
+SJ Srivastava,282,444,15,106,23,1,14,50,13,9.45,29.6,18.8,37.59,1.07,9.59,8.5,11.43,0,0
+Mohammad Nabi,417,527,15,153,2,0,23,33,26,7.58,35.13,27.8,36.69,0.65,6.45,8.5,10.0,1,0
+R Shepherd,292,591,15,89,7,0,22,42,43,12.14,39.4,19.47,30.48,0.68,7.8,11.87,14.7,2,0
+RR Powar,426,538,15,174,16,1,26,41,18,7.58,35.87,28.4,40.85,0.58,7.27,7.72,10.0,0,0
+AN Ahmed,344,515,15,131,13,5,17,43,20,8.98,34.33,22.93,38.08,0.88,8.08,8.13,12.0,1,0
+KA Jamieson,280,460,15,114,8,3,14,44,26,9.86,30.67,18.67,40.71,1.07,9.34,8.73,13.47,2,0
+PVD Chameera,405,658,15,165,13,2,20,67,31,9.75,43.87,27.0,40.74,0.75,9.56,8.05,11.58,1,0
+Umar Gul,135,198,14,59,12,6,6,17,5,8.8,14.14,9.64,43.7,2.33,6.43,8.78,11.38,2,0
+Kamran Khan,166,248,14,71,10,1,9,25,10,8.96,17.71,11.86,42.77,1.56,9.33,7.0,10.41,2,0
+GD McGrath,324,366,14,162,4,1,14,41,10,6.78,26.14,23.14,50.0,1.0,5.97,7.0,9.45,1,0
+S Lamichhane,210,297,14,77,1,0,9,21,17,8.49,21.21,15.0,36.67,1.56,8.29,8.1,19.0,2,0
+AB McDonald,186,263,14,52,2,0,10,20,11,8.48,18.79,13.29,27.96,1.4,8.8,7.75,10.2,2,0
+Arshad Khan,296,546,14,119,19,0,19,60,27,11.07,39.0,21.14,40.2,0.74,10.08,11.17,13.91,1,0
+V Nigam,289,444,14,97,0,0,17,30,25,9.22,31.71,20.64,33.56,0.82,8.74,10.07,5.69,0,0
+Karanveer Singh,204,323,14,66,9,0,9,19,19,9.5,23.07,14.57,32.35,1.56,8.67,9.32,12.67,2,0
+R McLaren,354,558,14,128,15,3,18,69,11,9.46,39.86,25.29,36.16,0.78,8.61,9.58,11.25,1,0
+JR Hopes,360,562,14,119,9,0,20,44,28,9.37,40.14,25.71,33.06,0.7,6.29,9.18,11.43,0,0
+KR Sen,241,396,14,89,12,2,12,41,16,9.86,28.29,17.21,36.93,1.17,9.1,8.68,13.96,2,0
+DJG Sammy,236,354,14,75,6,2,19,33,12,9.0,25.29,16.86,31.78,0.74,8.78,8.62,11.77,2,1
+CJ Anderson,297,525,14,86,18,0,22,52,23,10.61,37.5,21.21,28.96,0.64,9.82,9.23,14.91,1,0
+BA Bhatt,296,408,13,98,6,1,15,35,13,8.27,31.38,22.77,33.11,0.87,7.06,8.94,9.07,1,0
+TK Curran,238,432,13,59,11,3,13,33,23,10.89,33.23,18.31,24.79,1.0,9.57,8.97,13.61,1,0
+CK Langeveldt,156,199,13,75,8,1,7,17,8,7.65,15.31,12.0,48.08,1.86,7.07,8.8,8.0,1,0
+SM Pollock,276,307,13,132,4,0,13,30,9,6.67,23.62,21.23,47.83,1.0,6.51,7.33,,1,0
+Harsh Dubey,168,242,13,57,0,0,9,16,13,8.64,18.62,12.92,33.93,1.44,9.48,8.87,6.0,2,0
+AS Roy,264,360,13,98,6,0,16,24,18,8.18,27.69,20.31,37.12,0.81,8.67,7.63,9.0,0,0
+JDP Oram,237,362,13,86,6,1,14,34,14,9.16,27.85,18.23,36.29,0.93,9.37,9.12,8.88,1,0
+J Theron,221,311,13,75,12,2,10,28,9,8.44,23.92,17.0,33.94,1.3,9.21,7.1,8.73,1,0
+J Little,228,341,13,84,8,0,11,30,16,8.97,26.23,17.54,36.84,1.18,9.2,7.87,10.62,2,0
+Joginder Sharma,256,421,13,73,9,7,15,38,17,9.87,32.38,19.69,28.52,0.87,11.25,8.96,11.61,0,0
+RS Bopara,206,301,13,66,7,0,14,25,10,8.77,23.15,15.85,32.04,0.93,8.0,8.51,10.17,1,0
+Nithish Kumar Reddy,264,454,13,83,7,2,20,33,26,10.32,34.92,20.31,31.44,0.65,10.16,9.98,13.3,1,0
+LS Livingstone,312,471,13,82,9,0,27,38,19,9.06,36.23,24.0,26.28,0.48,6.8,9.45,7.0,1,0
+AG Murtaza,264,322,12,106,5,0,12,22,14,7.32,26.83,22.0,40.15,1.0,5.13,7.93,15.5,3,0
+Ankit Sharma,367,453,12,139,8,0,21,28,18,7.41,37.75,30.58,37.87,0.57,5.86,8.31,19.71,0,0
+V Shankar,238,344,12,80,15,5,22,29,12,8.67,28.67,19.83,33.61,0.55,9.0,8.81,7.64,0,0
+V Pratap Singh,204,300,12,75,7,0,9,31,10,8.82,25.0,17.0,36.76,1.33,8.47,9.27,9.0,1,0
+Simarjeet Singh,243,411,12,109,12,1,14,36,25,10.15,34.25,20.25,44.86,0.86,9.88,9.77,13.0,1,0
+SC Ganguly,276,370,12,82,3,1,20,25,14,8.04,30.83,23.0,29.71,0.6,8.17,7.59,25.0,2,0
+BCJ Cutting,281,430,12,78,7,0,17,44,15,9.18,35.83,23.42,27.76,0.71,9.22,7.95,12.72,0,0
+Azmatullah Omarzai,295,476,12,110,5,0,15,51,18,9.68,39.67,24.58,37.29,0.8,9.56,8.49,12.0,0,0
+K Kartikeya,283,412,12,91,7,0,16,25,23,8.73,34.33,23.58,32.16,0.75,8.83,8.36,13.33,0,0
+A Chandila,234,245,11,105,0,0,12,17,10,6.28,22.27,21.27,44.87,0.92,5.79,7.89,6.0,1,0
+TS Mills,209,349,11,75,9,0,10,37,14,10.02,31.73,19.0,35.89,1.1,8.83,10.57,10.75,1,0
+BE Hendricks,150,239,11,55,6,1,7,24,7,9.56,21.73,13.64,36.67,1.57,10.73,7.71,9.57,2,0
+S Badree,258,329,11,114,5,0,12,28,14,7.65,29.91,23.45,44.19,0.92,7.68,7.6,,1,0
+JL Pattinson,213,321,11,78,9,0,10,34,10,9.04,29.18,19.36,36.62,1.1,8.76,8.86,10.67,0,0
+JD Ryder,236,314,11,84,5,0,16,21,13,7.98,28.55,21.45,35.59,0.69,6.0,7.87,12.0,1,0
+Pankaj Singh,300,472,11,115,10,1,17,52,17,9.44,42.91,27.27,38.33,0.65,7.9,10.23,14.57,0,0
+Lalit Yadav,288,430,11,86,5,1,19,26,24,8.96,39.09,26.18,29.86,0.58,9.28,8.61,11.0,0,0
+R Parag,328,516,11,80,6,0,34,32,25,9.44,46.91,29.82,24.39,0.32,8.0,9.57,12.4,0,0
+KP Appanna,216,291,11,70,6,0,13,20,11,8.08,26.45,19.64,32.41,0.85,9.33,7.97,,1,0
+Akash Deep,291,554,11,105,7,4,14,48,33,11.42,50.36,26.45,36.08,0.79,10.35,11.49,14.79,1,0
+OC McCoy,162,255,11,51,17,1,8,20,9,9.44,23.18,14.73,31.48,1.38,6.0,11.01,8.95,1,0
+Abhishek Sharma,354,544,11,101,7,1,35,38,27,9.22,49.45,32.18,28.53,0.31,9.65,9.01,9.82,0,0
+MA Wood,120,192,11,54,6,1,5,13,12,9.6,17.45,10.91,45.0,2.2,8.2,7.38,13.14,2,1
+N Rana,218,326,11,68,5,0,27,31,13,8.97,29.64,19.82,31.19,0.41,9.55,9.1,7.04,0,0
+MP Yadav,151,244,10,72,2,1,7,22,15,9.7,24.4,15.1,47.68,1.43,11.18,8.51,11.43,2,0
+T Thushara,135,164,10,60,7,2,6,15,4,7.29,16.4,13.5,44.44,1.67,5.73,5.2,11.54,1,0
+B Laughlin,168,284,10,44,10,1,9,26,10,10.14,28.4,16.8,26.19,1.11,10.17,7.64,13.22,0,0
+KMA Paul,163,239,10,54,12,5,8,18,8,8.8,23.9,16.3,33.13,1.25,16.5,7.21,11.19,2,0
+J Syed Mohammad,192,286,10,50,4,0,11,18,12,8.94,28.6,19.2,26.04,0.91,5.25,9.59,6.0,0,0
+MN Samuels,214,285,10,80,5,0,11,23,12,7.99,28.5,21.4,37.38,0.91,6.23,7.8,11.35,1,0
+Anand Rajan,149,206,10,62,12,1,8,17,8,8.3,20.6,14.9,41.61,1.25,6.44,7.18,14.28,1,0
+DJ Hooda,380,550,10,107,11,0,34,40,23,8.68,55.0,38.0,28.16,0.29,7.24,9.08,12.0,0,0
+DJ Hussey,317,485,10,92,6,0,26,30,25,9.18,48.5,31.7,29.02,0.38,10.25,7.9,16.29,0,0
+P Parameswaran,154,226,9,66,4,1,8,23,10,8.81,25.11,17.11,42.86,1.12,9.75,7.4,10.55,1,0
+Akash Singh,205,326,9,73,3,0,10,39,13,9.54,36.22,22.78,35.61,0.9,9.18,10.62,10.0,0,0
+J Overton,161,272,9,58,0,0,10,18,17,10.14,30.22,17.89,36.02,0.9,13.03,9.07,9.78,2,0
+J Yadav,390,447,9,155,6,2,20,31,15,6.88,49.67,43.33,39.74,0.45,7.48,6.32,,0,0
+JEC Franklin,151,225,9,41,2,2,15,21,6,8.94,25.0,16.78,27.15,0.6,10.0,8.31,19.0,0,0
+KP Pietersen,174,218,9,57,6,0,13,10,9,7.52,24.22,19.33,32.76,0.69,6.0,7.74,9.67,0,0
+SE Bond,186,225,9,76,5,0,8,27,3,7.26,25.0,20.67,40.86,1.12,6.33,6.14,9.67,0,0
+SB Styris,216,278,9,85,6,0,11,19,14,7.72,30.89,24.0,39.35,0.82,6.09,7.78,10.14,1,0
+Shahid Afridi,180,237,9,65,4,1,10,19,7,7.9,26.33,20.0,36.11,0.9,7.0,8.35,7.0,1,0
+SMSM Senanayake,192,211,9,75,3,0,8,17,5,6.59,23.44,21.33,39.06,1.12,5.29,9.44,7.5,0,0
+IS Sodhi,181,204,9,66,1,0,8,9,8,6.76,22.67,20.11,36.46,1.12,6.38,6.68,36.0,1,0
+BAW Mendis,244,306,9,79,3,1,10,17,12,7.52,34.0,27.11,32.38,0.9,6.79,7.55,9.0,0,0
+AM Nayar,229,323,9,70,10,0,19,22,13,8.46,35.89,25.44,30.57,0.47,7.25,8.59,9.0,1,0
+AF Milne,207,339,9,77,9,1,10,35,13,9.83,37.67,23.0,37.2,0.9,9.89,9.62,9.88,2,0
+Mohammad Asif,192,307,9,71,6,1,8,37,8,9.59,34.11,21.33,36.98,1.12,9.8,7.67,11.38,0,0
+N Thushara,182,289,9,63,14,2,8,28,10,9.53,32.11,20.22,34.62,1.12,9.22,8.8,10.47,2,0
+S Sandeep Warrier,168,257,8,75,6,2,10,25,14,9.18,32.12,21.0,44.64,0.8,9.46,7.5,,1,0
+S Randiv,174,223,8,60,6,0,8,20,5,7.69,27.88,21.75,34.48,1.0,7.2,7.5,11.0,0,0
+A Mithun,288,477,8,95,15,9,16,42,21,9.94,59.62,36.0,32.99,0.5,9.06,9.32,11.92,0,0
+AA Chavan,248,339,8,110,7,0,13,28,16,8.2,42.38,31.0,44.35,0.62,7.39,9.17,16.5,0,0
+AM Ghazanfar,104,146,8,38,0,0,5,10,8,8.42,18.25,13.0,36.54,1.6,8.9,8.87,1.0,0,0
+WG Jacks,144,221,8,49,1,0,13,17,11,9.21,27.62,18.0,34.03,0.62,10.53,8.39,10.29,0,0
+GC Viljoen,138,229,8,47,8,0,6,25,6,9.96,28.62,17.25,34.06,1.33,9.17,8.0,15.6,1,0
+Zeeshan Ansari,209,334,8,54,0,0,10,24,17,9.59,41.75,26.12,25.84,0.8,,9.26,12.0,1,0
+KC Cariappa,216,349,8,62,6,2,11,23,19,9.69,43.62,27.0,28.7,0.73,11.0,8.96,11.0,0,0
+I Udana,174,286,8,49,8,0,10,26,11,9.86,35.75,21.75,28.16,0.8,11.0,8.8,9.78,0,0
+Yudhvir Singh,143,257,8,61,2,0,9,28,13,10.78,32.12,17.88,42.66,0.89,10.33,10.67,14.5,1,0
+JDS Neesham,216,342,8,68,6,0,13,32,14,9.5,42.75,27.0,31.48,0.62,9.64,9.08,18.0,1,0
+M Ntini,210,257,8,108,1,2,9,29,8,7.34,32.12,26.25,51.43,0.89,7.0,6.75,9.6,1,0
+TL Suman,150,198,7,44,0,2,11,8,10,7.92,28.29,21.43,29.33,0.64,7.0,7.17,12.0,0,0
+C de Grandhomme,216,323,7,62,12,2,19,22,13,8.97,46.14,30.86,28.7,0.37,6.0,9.33,9.75,1,0
+Y Venugopal Rao,216,338,7,60,6,0,20,30,13,9.39,48.29,30.86,27.78,0.35,9.57,8.81,14.0,0,0
+AM Salvi,150,205,7,61,6,0,7,18,7,8.2,29.29,21.43,40.67,1.0,8.08,7.71,9.2,0,0
+HF Gurney,162,239,7,51,6,1,8,23,7,8.85,34.14,23.14,31.48,0.88,6.0,8.12,13.5,0,0
+DJ Willey,216,277,7,84,5,0,11,28,8,7.69,39.57,30.86,38.89,0.64,6.71,9.12,10.75,0,0
+B Akhil,188,246,7,64,4,1,13,18,9,7.85,35.14,26.86,34.04,0.54,7.5,7.21,12.9,0,0
+Sakib Hussain,106,147,7,43,0,0,4,12,6,8.32,21.0,15.14,40.57,1.75,7.71,9.41,7.33,1,0
+Shivang Kumar,133,210,7,39,0,0,7,21,9,9.47,30.0,19.0,29.32,1.0,,9.47,,1,0
+P Amarnath,132,241,7,34,0,0,6,29,7,10.95,34.43,18.86,25.76,1.17,11.0,10.42,21.0,0,0
+XC Bartlett,221,394,7,73,0,0,11,46,16,10.7,56.29,31.57,33.03,0.64,10.58,12.0,9.86,0,0
+M Siddharth,166,238,7,59,1,2,8,17,11,8.6,34.0,23.71,35.54,0.88,7.78,8.38,12.63,0,0
+S Dube,144,260,7,33,5,0,17,19,12,10.83,37.14,20.57,22.92,0.41,,10.36,12.35,1,0
+RR Raje,139,214,7,41,4,1,10,23,4,9.24,30.57,19.86,29.5,0.7,11.33,7.89,12.21,0,0
+Ramandeep Singh,61,95,7,19,5,0,6,6,4,9.34,13.57,8.71,31.15,1.17,,10.5,8.07,1,0
+KM Asif,134,232,7,47,8,1,7,16,14,10.39,33.14,19.14,35.07,1.0,9.67,9.91,15.43,0,0
+R Shukla,120,217,7,44,7,3,7,21,9,10.85,31.0,17.14,36.67,1.0,8.8,11.29,16.67,0,0
+OF Smith,90,179,7,36,4,1,6,12,16,11.93,25.57,12.86,40.0,1.17,7.75,12.75,15.33,1,0
+PC Valthaty,151,207,7,46,2,0,10,15,7,8.23,29.57,21.57,30.46,0.7,9.0,8.09,9.23,1,0
+Brijesh Sharma,126,180,7,48,0,0,6,22,4,8.57,25.71,18.0,38.1,1.17,7.91,8.04,9.5,0,0
+Swapnil Singh,162,241,7,39,2,1,14,14,12,8.93,34.43,23.14,24.07,0.5,8.54,9.29,,0,0
+AJ Hosein,108,150,7,40,0,0,5,8,10,8.33,21.43,15.43,37.04,1.4,7.93,8.5,12.0,1,0
+D Pretorius,150,242,7,46,16,0,7,14,11,9.68,34.57,21.43,30.67,1.0,,7.21,12.82,0,0
+B Stanlake,144,200,7,64,6,0,6,19,9,8.33,28.57,20.57,44.44,1.17,7.93,8.67,11.0,0,0
+V Sehwag,136,236,6,45,1,1,15,19,16,10.41,39.33,22.67,33.09,0.4,18.67,8.83,12.6,0,0
+S Tyagi,209,304,6,99,8,3,14,34,12,8.73,50.67,34.83,47.37,0.43,8.38,7.25,17.45,0,0
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+62.0,31.0,116.98,0.0,0.0,13.21,107.14,169.23,37.74,2.0,9.64,53.0,33.0,31.82,17.33,7.67,0.0,3.0,3.0,0,3.98,A Flintoff,4.06
+55.0,27.5,117.02,0.0,0.0,12.77,110.0,117.78,36.17,24.0,8.69,21.96,15.17,43.96,7.7,8.94,3.0,9.0,19.0,2,8.3,A Kamboj,8.21
+35.0,17.5,74.47,0.0,0.0,6.38,110.0,74.36,46.81,49.0,6.77,22.22,19.69,41.14,8.38,5.43,5.0,15.0,42.0,2,9.86,A Kumble,9.85
+292.0,15.37,123.73,0.0,0.0,15.68,66.67,166.23,38.98,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,20.0,0.0,0,3.52,A Manohar,3.58
+441.0,36.75,176.4,0.0,3.0,29.6,179.74,90.0,35.6,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,13.0,0.0,0,5.53,A Mhatre,5.36
+381.0,12.7,91.37,0.0,0.0,8.63,83.33,113.4,42.45,183.0,7.46,22.91,18.42,36.19,9.85,6.88,20.0,55.0,162.0,2,24.4,A Mishra,24.82
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,8.0,9.94,59.62,36.0,32.99,11.92,9.06,0.0,0.0,16.0,0,1.36,A Mithun,1.57
+41.0,5.12,68.33,0.0,0.0,6.67,110.0,63.16,60.0,121.0,7.98,20.97,15.77,43.4,9.06,7.57,18.0,17.0,88.0,2,18.01,A Nehra,17.93
+49.0,8.17,98.0,0.0,0.0,12.0,110.0,104.55,48.0,63.0,9.27,27.62,17.87,40.76,10.6,8.58,5.0,14.0,49.0,2,9.34,A Nortje,9.51
+672.0,29.22,140.88,0.0,4.0,19.5,140.88,144.44,33.54,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,26.0,0.0,1,4.21,A Raghuvanshi,4.86
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,31.0,8.11,20.61,15.26,42.28,10.31,7.0,3.0,0.0,23.0,2,5.15,A Singh,5.41
+974.0,37.46,130.74,1.0,5.0,15.44,104.58,168.31,36.64,23.0,7.9,30.17,22.91,39.09,11.77,6.19,1.0,36.0,30.0,3,8.81,A Symonds,8.82
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,32.0,8.39,20.5,14.66,30.92,13.52,7.14,3.0,0.0,22.0,2,5.09,A Zampa,5.15
+69.0,13.8,82.14,0.0,0.0,5.95,71.05,125.93,46.43,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,2.09,AA Bilakhia,2.34
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,8.0,8.2,42.38,31.0,44.35,16.5,7.39,0.0,0.0,13.0,0,1.65,AA Chavan,1.98
+217.0,15.5,103.83,0.0,1.0,11.48,59.62,139.47,42.58,1.0,9.18,130.0,85.0,32.94,12.0,8.17,0.0,15.0,10.0,0,4.14,AA Jhunjhunwala,3.8
+179.0,22.38,118.54,0.0,0.0,11.92,153.85,137.97,35.1,33.0,9.01,35.58,23.7,34.91,10.1,8.88,3.0,18.0,42.0,2,7.7,AB Agarkar,7.8
+42.0,21.0,100.0,0.0,0.0,14.29,111.11,40.0,50.0,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,3.0,0.0,0,3.06,AB Barath,3.21
+26.0,2.89,54.17,0.0,0.0,4.17,110.0,48.65,60.42,82.0,8.32,25.65,18.49,42.74,11.3,7.4,9.0,16.0,75.0,2,11.19,AB Dinda,11.18
+123.0,24.6,123.0,0.0,0.0,13.0,157.89,125.81,35.0,14.0,8.48,18.79,13.29,27.96,10.2,8.8,2.0,9.0,10.0,0,5.91,AB McDonald,6.23
+5181.0,41.45,152.38,3.0,40.0,19.62,118.41,235.26,30.56,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,170.0,0.0,1,12.99,AB de Villiers,13.41
+120.0,17.14,133.33,0.0,1.0,25.56,129.41,120.0,52.22,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,3.59,AC Blizzard,3.98
+2069.0,27.22,139.51,2.0,11.0,22.32,133.79,208.0,45.38,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,80.0,0.0,1,7.57,AC Gilchrist,7.45
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,16.0,7.92,26.0,19.69,42.22,10.12,6.53,1.0,0.0,15.0,0,3.4,AC Thomas,3.44
+181.0,30.17,126.57,0.0,0.0,12.59,84.62,150.0,30.77,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,3.72,AC Voges,4.0
+148.0,24.67,126.5,0.0,0.0,16.24,135.23,120.0,40.17,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,6.0,0.0,0,3.44,AD Hales,3.68
+79.0,7.9,101.28,0.0,0.0,7.69,200.0,110.34,35.9,19.0,7.11,19.21,16.21,42.21,10.29,7.22,2.0,11.0,13.0,0,6.88,AD Mascarenhas,5.48
+724.0,23.35,126.35,0.0,1.0,12.74,68.75,154.07,31.94,28.0,8.31,39.11,28.25,29.96,8.88,8.43,2.0,41.0,44.0,3,8.51,AD Mathews,8.13
+90.0,9.0,91.84,0.0,0.0,9.18,76.47,88.89,44.9,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,10.0,0.0,0,2.05,AD Nath,2.52
+2655.0,28.86,174.9,0.0,12.0,27.01,215.38,204.71,40.05,133.0,9.64,21.87,13.62,35.78,11.14,8.48,14.0,114.0,121.0,3,23.8,AD Russell,23.91
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,9.0,9.83,37.67,23.0,37.2,9.88,9.89,2.0,0.0,10.0,0,2.08,AF Milne,2.14
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,12.0,7.32,26.83,22.0,40.15,15.5,5.13,3.0,0.0,12.0,0,3.57,AG Murtaza,3.49
+49.0,9.8,89.09,0.0,0.0,16.36,89.09,120.0,65.45,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,2.04,AG Paunikar,2.53
+2092.0,24.9,128.66,0.0,15.0,17.96,116.13,162.4,42.0,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,90.0,0.0,1,5.99,AJ Finch,6.16
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,7.0,8.33,21.43,15.43,37.04,12.0,7.93,1.0,0.0,5.0,0,2.47,AJ Hosein,2.36
+91.0,11.38,121.33,0.0,0.0,14.67,110.0,110.2,46.67,48.0,8.8,20.94,14.27,32.85,10.06,8.12,7.0,13.0,30.0,2,10.12,AJ Tye,9.87
+1633.0,29.69,135.18,0.0,10.0,16.31,125.81,186.07,31.46,6.0,9.21,57.33,37.33,26.34,8.73,7.97,0.0,63.0,23.0,1,7.28,AK Markram,6.66
+401.0,18.23,113.28,0.0,0.0,11.86,88.0,131.67,39.55,3.0,7.85,48.0,36.67,36.36,12.5,6.7,0.0,23.0,12.0,0,4.21,AL Menaria,4.24
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,8.0,8.42,18.25,13.0,36.54,1.0,8.9,0.0,0.0,5.0,0,3.47,AM Ghazanfar,3.32
+672.0,17.23,117.28,0.0,0.0,13.09,84.04,161.15,38.92,9.0,8.46,35.89,25.44,30.57,9.0,7.25,1.0,50.0,19.0,1,5.11,AM Nayar,5.43
+5194.0,29.85,124.92,2.0,34.0,15.78,120.32,164.96,36.53,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,191.0,0.0,1,10.43,AM Rahane,10.85
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,7.0,8.2,29.29,21.43,40.67,9.2,8.08,0.0,0.0,7.0,0,2.47,AM Salvi,2.55
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,15.0,8.98,34.33,22.93,38.08,12.0,8.08,1.0,0.0,17.0,0,3.26,AN Ahmed,2.81
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,5.0,10.36,22.8,13.2,31.82,13.0,11.17,0.0,0.0,3.0,0,1.54,AP Dole,1.42
+87.0,21.75,114.47,0.0,0.0,11.84,126.32,120.0,35.53,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,4.0,0.0,0,2.56,AP Majumdar,3.36
+339.0,14.12,124.63,0.0,1.0,18.75,114.62,161.9,47.43,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,27.0,0.0,0,4.06,AP Tare,3.77
+1947.0,20.71,133.17,0.0,3.0,15.53,102.59,157.98,33.17,144.0,7.51,30.15,24.1,33.94,8.58,7.38,4.0,129.0,168.0,3,20.57,AR Patel,20.67
+27.0,27.0,84.38,0.0,0.0,9.38,110.0,72.73,50.0,22.0,9.77,32.14,19.73,39.63,10.96,9.83,1.0,5.0,22.0,2,5.19,AS Joseph,5.27
+26.0,6.5,63.41,0.0,0.0,7.32,110.0,71.43,65.85,24.0,9.55,35.17,22.08,40.38,12.27,9.19,2.0,7.0,29.0,2,5.13,AS Rajpoot,5.15
+194.0,17.64,116.17,0.0,0.0,11.98,83.33,140.79,37.13,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,16.0,0.0,0,3.76,AS Raut,3.66
+65.0,9.29,122.64,0.0,0.0,13.21,80.0,160.0,37.74,13.0,8.18,27.69,20.31,37.12,9.0,8.67,0.0,12.0,16.0,0,6.13,AS Roy,5.81
+49.0,7.0,125.64,0.0,0.0,17.95,110.0,134.48,43.59,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,6.0,0.0,0,3.04,AS Yadav,3.53
+4348.0,28.61,128.22,1.0,22.0,15.69,104.46,172.64,34.95,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,185.0,0.0,1,8.53,AT Rayudu,8.53
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,2.0,9.1,45.5,30.0,33.33,11.0,9.67,0.0,0.0,3.0,0,1.55,AU Rashid,1.45
+815.0,17.72,146.06,0.0,0.0,18.46,120.0,174.43,35.13,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,56.0,0.0,1,4.8,Abdul Samad,4.6
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,3.0,12.2,40.67,20.0,28.33,9.6,17.25,0.0,0.0,3.0,0,1.03,Abhinandan Singh,0.99
+2196.0,29.28,165.74,2.0,12.0,25.58,166.29,160.78,36.83,11.0,9.22,49.45,32.18,28.53,9.82,9.65,0.0,82.0,35.0,1,9.27,Abhishek Sharma,9.33
+691.0,23.83,144.26,0.0,3.0,19.83,143.15,206.9,33.82,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,30.0,0.0,1,4.61,Abishek Porel,4.85
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,11.0,11.42,50.36,26.45,36.08,14.79,10.35,1.0,0.0,14.0,0,1.29,Akash Deep,1.52
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,26.0,9.99,23.0,13.81,33.15,10.29,7.79,5.0,0.0,17.0,2,5.0,Akash Madhwal,4.79
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,9.0,9.54,36.22,22.78,35.61,10.0,9.18,0.0,0.0,10.0,0,2.77,Akash Singh,2.47
+115.0,12.78,110.58,0.0,1.0,13.46,80.0,137.93,48.08,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,10.0,0.0,0,3.27,Aman Hakim Khan,3.34
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,10.0,8.3,20.6,14.9,41.61,14.28,6.44,1.0,0.0,8.0,0,3.14,Anand Rajan,2.91
+309.0,22.07,156.85,0.0,1.0,21.32,100.0,181.01,38.58,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,19.0,0.0,0,4.86,Aniket Verma,4.48
+63.0,15.75,95.45,0.0,0.0,10.61,90.0,100.0,46.97,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,4.0,0.0,0,2.46,Anirudh Singh,2.73
+87.0,12.43,129.85,0.0,0.0,16.42,116.67,110.71,40.3,12.0,7.41,37.75,30.58,37.87,19.71,5.86,0.0,10.0,21.0,0,5.48,Ankit Sharma,5.74
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,4.0,7.98,36.25,27.25,34.86,24.0,3.5,0.0,0.0,6.0,0,2.46,Ankit Soni,2.35
+139.0,15.44,120.87,0.0,0.0,19.13,125.25,120.0,46.09,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,9.0,0.0,0,3.5,Anmolpreet Singh,3.47
+318.0,22.71,119.1,0.0,1.0,14.98,97.92,189.55,42.7,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,21.0,0.0,0,4.66,Anuj Rawat,4.15
+36.0,9.0,76.6,0.0,0.0,6.38,110.0,82.86,46.81,21.0,9.26,30.29,19.62,36.89,11.04,7.66,2.0,8.0,22.0,2,4.0,Anureet Singh,4.63
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,4.0,9.62,29.25,18.25,45.21,6.0,6.9,0.0,0.0,5.0,0,2.92,Arjun Tendulkar,2.79
+119.0,19.83,135.23,0.0,1.0,15.91,110.0,144.44,42.05,14.0,11.07,39.0,21.14,40.2,13.91,10.08,1.0,10.0,19.0,0,5.36,Arshad Khan,5.68
+31.0,5.17,67.39,0.0,0.0,8.7,110.0,67.5,67.39,114.0,9.18,25.91,16.94,38.06,9.79,8.79,13.0,13.0,89.0,2,15.7,Arshdeep Singh,16.11
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,6.0,10.28,38.83,22.67,46.32,10.75,14.64,0.0,0.0,6.0,0,1.79,Ashok Sharma,1.37
+426.0,25.06,157.78,0.0,2.0,21.11,110.0,170.89,35.19,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,20.0,0.0,0,4.1,Ashutosh Sharma,4.37
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,17.0,9.67,17.35,10.76,38.8,12.94,10.86,2.0,0.0,9.0,0,3.12,Ashwani Kumar,2.9
+260.0,28.89,145.25,0.0,2.0,21.23,153.39,120.0,36.31,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,10.0,0.0,0,4.64,Atharva Taide,4.33
+68.0,22.67,147.83,0.0,0.0,23.91,110.0,133.33,47.83,99.0,9.25,26.99,17.51,36.81,10.07,9.35,12.0,16.0,80.0,2,16.98,Avesh Khan,14.57
+388.0,20.42,128.05,0.0,2.0,17.16,90.91,152.38,38.28,31.0,7.9,22.81,17.32,38.36,9.96,6.76,5.0,21.0,23.0,2,8.95,Azhar Mahmood,8.65
+99.0,12.38,130.26,0.0,0.0,17.11,110.0,143.75,38.16,12.0,9.68,39.67,24.58,37.29,12.0,9.56,0.0,9.0,15.0,0,4.61,Azmatullah Omarzai,4.71
+76.0,10.86,138.18,0.0,0.0,18.18,110.0,163.33,43.64,7.0,7.85,35.14,26.86,34.04,12.9,7.5,0.0,11.0,13.0,0,4.95,B Akhil,5.08
+280.0,17.5,112.0,0.0,1.0,14.0,77.65,177.27,41.6,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,21.0,0.0,0,4.08,B Chipli,3.75
+21.0,7.0,70.0,0.0,0.0,6.67,72.41,120.0,56.67,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,3.0,0.0,0,2.26,B Indrajith,2.26
+332.0,8.97,91.97,0.0,0.0,9.7,110.0,99.65,45.98,228.0,7.84,25.35,19.39,43.8,9.66,6.7,21.0,73.0,198.0,2,26.0,B Kumar,26.3
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,10.0,10.14,28.4,16.8,26.19,13.22,10.17,0.0,0.0,9.0,0,1.99,B Laughlin,2.11
+124.0,12.4,129.17,0.0,0.0,16.67,110.0,140.54,41.67,30.0,7.72,37.53,29.17,45.71,9.06,7.12,1.0,19.0,38.0,2,7.88,B Lee,8.09
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+35.0,35.0,94.59,0.0,0.0,8.11,110.0,109.52,37.84,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,4.0,0.0,0,2.96,B Sumanth,3.26
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+935.0,22.8,134.53,2.0,2.0,16.26,129.13,150.59,33.24,31.0,8.91,33.0,22.23,36.28,10.56,8.64,3.0,43.0,38.0,3,9.3,BA Stokes,9.21
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+2882.0,27.19,132.44,2.0,13.0,19.44,128.29,216.44,43.11,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,109.0,0.0,1,7.92,BB McCullum,8.06
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+1400.0,30.43,125.9,0.0,6.0,14.84,92.57,169.76,35.88,18.0,7.95,17.22,13.0,33.76,11.0,9.11,2.0,63.0,20.0,1,8.12,BJ Hodge,8.07
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+40.0,13.33,114.29,0.0,0.0,20.0,114.29,120.0,54.29,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,3.0,0.0,0,3.31,BR Dunk,3.47
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+32.0,16.0,91.43,0.0,0.0,5.71,110.0,100.0,40.0,26.0,9.93,33.15,20.04,31.29,11.81,9.24,2.0,7.0,25.0,2,6.18,Basil Thampi,5.72
+187.0,31.17,152.03,0.0,0.0,16.26,110.0,156.7,26.83,22.0,8.18,26.41,19.36,32.63,19.5,7.08,0.0,17.0,28.0,2,7.15,Bipul Sharma,7.08
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+58.0,29.0,161.11,0.0,0.0,22.22,110.0,180.65,33.33,2.0,8.77,47.5,32.5,52.31,4.57,11.0,0.0,3.0,3.0,0,6.68,C Bosch,6.03
+270.0,45.0,163.64,0.0,2.0,25.45,115.0,209.09,36.97,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,4.95,C Connolly,5.43
+903.0,34.73,152.28,1.0,3.0,20.24,146.31,170.75,30.52,22.0,9.39,35.14,22.45,37.45,12.87,9.57,0.0,36.0,33.0,3,8.58,C Green,8.46
+177.0,19.67,125.53,0.0,0.0,19.15,132.38,80.0,46.1,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,11.0,0.0,0,3.6,C Munro,3.63
+20.0,4.0,66.67,0.0,0.0,10.0,110.0,76.0,66.67,20.0,8.75,32.45,22.25,34.83,11.65,6.84,2.0,7.0,20.0,2,5.82,C Sakariya,5.49
+303.0,20.2,135.27,0.0,0.0,16.07,11.11,138.78,36.61,7.0,8.97,46.14,30.86,28.7,9.75,6.0,1.0,21.0,19.0,0,5.22,C de Grandhomme,5.24
+205.0,17.08,114.53,0.0,0.0,15.08,70.37,151.43,41.34,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,15.0,0.0,0,3.62,CA Ingram,3.65
+1329.0,34.08,141.23,0.0,10.0,21.04,144.39,136.36,41.13,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,42.0,0.0,1,5.98,CA Lynn,6.05
+390.0,21.67,100.0,0.0,1.0,13.85,93.78,137.93,47.95,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,22.0,0.0,0,3.01,CA Pujara,3.26
+4997.0,39.35,150.02,6.0,31.0,23.03,135.29,209.33,43.89,19.0,8.18,39.74,29.16,35.38,9.49,8.45,1.0,141.0,38.0,1,16.31,CH Gayle,13.23
+618.0,21.31,155.67,0.0,2.0,19.14,211.11,173.76,29.97,107.0,8.26,22.21,16.13,41.02,8.83,7.81,12.0,49.0,81.0,3,19.04,CH Morris,19.19
+538.0,24.45,127.79,0.0,3.0,16.86,79.27,197.22,43.94,14.0,10.61,37.5,21.21,28.96,14.91,9.82,1.0,29.0,22.0,1,6.33,CJ Anderson,6.22
+98.0,16.33,83.76,0.0,0.0,7.69,68.75,106.25,47.86,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,8.0,0.0,0,2.15,CJ Ferguson,2.39
+81.0,9.0,105.19,0.0,0.0,7.79,110.0,121.05,44.16,36.0,9.89,30.86,18.72,33.38,12.22,9.48,5.0,12.0,34.0,2,7.74,CJ Jordan,7.68
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+971.0,27.74,128.27,0.0,6.0,15.06,89.17,191.22,34.35,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,45.0,0.0,1,5.36,CL White,5.26
+169.0,18.78,113.42,0.0,0.0,15.44,111.94,121.62,50.34,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,13.0,0.0,0,4.24,CM Gautam,3.62
+181.0,12.93,164.55,0.0,0.0,23.64,110.0,201.43,40.0,16.0,9.07,24.0,15.88,30.71,10.5,6.0,2.0,14.0,16.0,0,6.8,CR Brathwaite,6.91
+78.0,15.6,101.3,0.0,0.0,11.69,110.0,110.64,42.86,31.0,9.19,21.74,14.19,39.32,11.21,8.52,3.0,12.0,21.0,2,6.78,CR Woakes,6.96
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+26.0,8.67,54.17,0.0,0.0,4.17,110.0,64.1,60.42,109.0,7.75,24.03,18.61,38.26,9.41,7.88,10.0,12.0,89.0,2,14.76,CV Varun,14.96
+519.0,25.95,144.57,0.0,2.0,20.06,108.89,224.14,40.67,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,20.0,0.0,1,4.4,D Brevis,5.06
+191.0,23.88,144.7,0.0,2.0,21.97,116.67,145.83,41.67,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,9.0,0.0,0,4.54,D Ferreira,4.17
+2048.0,26.6,130.95,1.0,14.0,18.29,129.85,144.29,39.13,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,81.0,0.0,1,6.34,D Padikkal,7.12
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+148.0,24.67,148.0,0.0,0.0,19.0,110.0,157.97,30.0,17.0,9.06,26.29,17.41,29.05,9.6,8.12,2.0,11.0,15.0,0,6.66,D Wiese,6.86
+3200.0,34.78,138.59,1.0,13.0,16.28,90.66,172.81,31.57,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,140.0,0.0,1,8.07,DA Miller,7.61
+6567.0,40.04,140.26,4.0,62.0,19.2,136.32,179.65,36.63,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,184.0,0.0,1,15.64,DA Warner,14.98
+304.0,25.33,116.92,0.0,0.0,15.0,115.79,130.86,45.0,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,22.0,0.0,0,3.51,DB Das,3.65
+375.0,18.75,119.05,0.0,1.0,13.97,94.2,140.23,36.83,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,25.0,0.0,0,4.0,DB Ravi Teja,3.8
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+170.0,18.89,127.82,0.0,0.0,18.8,0.0,139.39,41.35,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,17.0,0.0,0,4.3,DH Yagnik,3.84
+1560.0,22.61,130.43,0.0,5.0,15.55,98.68,190.02,37.79,207.0,8.53,21.43,15.07,33.78,9.91,7.53,21.0,110.0,158.0,3,28.35,DJ Bravo,28.31
+111.0,37.0,109.9,0.0,0.0,15.84,89.04,120.0,49.5,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,4.0,0.0,0,3.79,DJ Harris,3.79
+1497.0,18.48,127.95,0.0,8.0,13.68,94.47,150.21,33.25,10.0,8.68,55.0,38.0,28.16,12.0,7.24,0.0,101.0,34.0,1,6.38,DJ Hooda,6.38
+1322.0,27.54,123.55,0.0,5.0,14.02,103.73,177.56,37.1,10.0,9.18,48.5,31.7,29.02,16.29,10.25,0.0,61.0,26.0,1,6.37,DJ Hussey,6.25
+92.0,13.14,93.88,0.0,0.0,14.29,90.32,120.0,60.2,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,2.4,DJ Jacobs,2.66
+351.0,27.0,131.46,0.0,2.0,14.23,137.76,131.03,28.09,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,15.0,0.0,0,3.56,DJ Mitchell,3.82
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,4.0,7.71,27.0,21.0,55.95,1.0,8.25,0.0,0.0,6.0,0,3.71,DJ Muthuswami,3.32
+39.0,13.0,73.58,0.0,0.0,7.55,28.57,120.0,56.6,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,4.0,0.0,0,2.47,DJ Thornely,2.39
+53.0,17.67,85.48,0.0,0.0,11.29,64.29,123.53,54.84,7.0,7.69,39.57,30.86,38.89,10.75,6.71,0.0,5.0,11.0,0,4.5,DJ Willey,4.28
+295.0,19.67,122.41,0.0,1.0,13.69,155.56,155.56,40.66,14.0,9.0,25.29,16.86,31.78,11.77,8.78,2.0,20.0,19.0,0,6.11,DJG Sammy,6.18
+115.0,16.43,116.16,0.0,0.0,16.16,83.82,120.0,45.45,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,3.07,DJM Short,3.22
+123.0,15.38,138.2,0.0,0.0,15.73,0.0,114.89,37.08,92.0,8.23,29.43,21.46,43.87,10.08,8.0,7.0,17.0,98.0,2,14.52,DL Chahar,14.44
+121.0,17.29,108.04,0.0,0.0,11.61,110.0,122.0,35.71,34.0,6.9,26.29,22.85,36.42,9.1,5.92,3.0,16.0,34.0,2,8.45,DL Vettori,8.4
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+1080.0,43.2,139.18,0.0,11.0,19.46,124.15,154.17,34.92,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,28.0,0.0,1,6.97,DP Conway,6.62
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+1808.0,28.25,123.67,1.0,10.0,16.42,115.99,180.92,38.3,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,78.0,0.0,1,6.52,DPMD Jayawardene,6.54
+44.0,4.4,100.0,0.0,0.0,9.09,16.67,133.33,50.0,15.0,8.8,35.2,24.0,40.0,11.75,7.15,2.0,13.0,16.0,0,4.59,DR Sams,4.66
+2385.0,29.44,135.9,0.0,17.0,20.63,125.0,155.41,44.84,27.0,9.18,30.56,19.96,33.21,12.88,10.22,2.0,88.0,46.0,3,11.22,DR Smith,10.93
+104.0,11.56,96.3,0.0,0.0,8.33,110.0,106.59,41.67,91.0,8.44,27.62,19.64,42.81,10.94,7.64,9.0,20.0,92.0,2,14.03,DS Kulkarni,13.88
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,17.0,8.46,38.88,27.59,31.34,10.52,8.03,0.0,0.0,19.0,0,2.74,DS Rathi,2.95
+460.0,16.43,116.75,0.0,0.0,10.66,35.71,139.57,34.01,40.0,8.28,30.45,22.08,35.79,11.78,8.03,0.0,41.0,49.0,2,8.12,DT Christian,8.13
+167.0,7.59,104.38,0.0,0.0,10.62,110.0,115.33,43.12,105.0,7.1,24.6,20.78,48.76,8.93,6.43,11.0,33.0,95.0,2,16.35,DW Steyn,14.62
+928.0,30.93,149.92,0.0,7.0,20.03,140.74,178.39,32.15,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,44.0,0.0,1,6.03,Dhruv Jurel,5.71
+654.0,27.25,137.11,0.0,4.0,20.55,140.26,160.0,44.44,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,26.0,0.0,1,5.68,E Lewis,5.21
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,29.0,8.76,17.28,11.83,40.23,9.0,10.12,5.0,0.0,15.0,2,5.53,E Malinga,5.64
+1406.0,23.05,122.58,0.0,5.0,15.34,93.36,168.33,41.67,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,74.0,0.0,1,4.59,EJG Morgan,4.83
+4773.0,35.36,135.79,0.0,39.0,17.47,133.23,199.04,34.22,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,147.0,0.0,1,10.52,F du Plessis,10.78
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+81.0,16.2,184.09,0.0,0.0,38.64,184.09,120.0,50.0,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,4.52,FH Allen,4.53
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,6.0,6.86,26.67,23.33,53.57,10.23,4.69,0.0,0.0,6.0,0,3.03,FH Edwards,2.89
+183.0,20.33,106.4,0.0,0.0,13.37,98.86,120.0,38.95,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,11.0,0.0,0,3.34,FY Fazal,3.5
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+36.0,12.0,92.31,0.0,0.0,7.69,110.0,75.0,43.59,21.0,8.58,22.81,15.95,40.6,11.26,6.9,1.0,4.0,15.0,2,5.48,KW Richardson,5.4
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+58.0,11.6,113.73,0.0,0.0,13.73,225.0,115.38,50.98,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,3.92,Kartik Sharma,3.61
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+125.0,17.86,97.66,0.0,0.0,9.38,96.3,55.56,37.5,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,3.61,MC Juneja,3.22
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+237.0,15.8,115.05,0.0,0.0,15.53,108.27,153.33,47.09,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,17.0,0.0,0,3.03,MD Mishra,3.4
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+177.0,17.7,143.9,0.0,0.0,17.07,50.0,171.62,34.96,33.0,7.6,16.12,12.73,42.14,9.79,7.55,5.0,14.0,20.0,2,9.8,MF Maharoof,9.32
+58.0,29.0,123.4,0.0,0.0,14.89,66.67,100.0,31.91,6.0,8.64,15.83,11.0,39.39,11.0,5.4,0.0,4.0,5.0,0,5.28,MG Bracewell,5.53
+167.0,11.93,102.45,0.0,0.0,10.43,110.0,108.76,46.63,66.0,8.45,26.36,18.71,45.1,9.69,8.02,3.0,28.0,54.0,2,10.82,MG Johnson,10.34
+98.0,19.6,104.26,0.0,0.0,12.77,107.89,120.0,43.62,3.0,6.55,24.0,22.0,36.36,11.0,6.0,0.0,6.0,5.0,0,4.76,MJ Clarke,4.74
+271.0,22.58,138.27,0.0,1.0,19.9,138.46,120.0,44.9,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,13.0,0.0,0,3.74,MJ Guptill,3.97
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+278.0,27.8,145.55,0.0,1.0,26.7,147.62,120.0,46.6,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,12.0,0.0,0,4.24,MJ Lumb,4.4
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+1107.0,41.0,138.55,0.0,8.0,20.65,133.2,200.0,41.55,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,32.0,0.0,1,5.88,ML Hayden,6.15
+1167.0,23.34,139.93,0.0,6.0,19.42,122.08,176.52,39.33,46.0,7.25,22.52,18.63,35.59,6.6,5.5,4.0,59.0,57.0,3,12.4,MM Ali,12.34
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+125.0,7.35,91.91,0.0,0.0,9.56,110.0,97.37,49.26,149.0,8.78,23.74,16.21,35.06,10.38,8.03,19.0,30.0,119.0,2,20.7,MM Sharma,20.57
+161.0,13.42,93.6,0.0,0.0,9.3,37.84,158.33,48.84,10.0,7.99,28.5,21.4,37.38,11.35,6.23,1.0,14.0,11.0,0,4.74,MN Samuels,4.53
+167.0,55.67,127.48,0.0,1.0,15.27,110.34,154.29,31.3,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,4.48,MN van Wyk,4.42
+2136.0,28.48,147.11,1.0,10.0,19.42,118.18,190.83,34.3,46.0,9.93,32.72,19.76,29.81,13.27,8.31,4.0,104.0,73.0,3,12.6,MP Stoinis,12.63
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+1504.0,27.85,138.36,1.0,10.0,19.14,144.17,155.45,39.56,37.0,8.6,21.7,15.14,33.75,12.49,7.71,3.0,57.0,34.0,3,10.86,MR Marsh,10.81
+798.0,21.57,114.0,0.0,4.0,16.57,106.33,160.0,48.43,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,39.0,0.0,1,4.11,MS Bisla,4.49
+5439.0,34.42,137.91,0.0,24.0,16.2,76.6,187.23,33.87,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,241.0,0.0,1,9.19,MS Dhoni,9.23
+99.0,11.0,141.43,0.0,0.0,20.0,110.0,164.58,47.14,39.0,8.9,33.77,22.77,41.22,11.69,8.79,3.0,15.0,44.0,2,8.79,MS Gony,8.71
+183.0,14.08,103.39,0.0,0.0,14.12,110.81,120.0,48.02,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,14.0,0.0,0,3.66,MS Wade,3.27
+394.0,26.27,128.34,0.0,1.0,14.66,93.1,165.74,34.85,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,23.0,0.0,0,4.48,MV Boucher,4.18
+153.0,19.12,116.79,0.0,0.0,16.79,123.16,0.0,45.8,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,8.0,0.0,0,3.29,MW Short,3.34
+1706.0,20.31,123.18,0.0,6.0,15.45,115.21,160.59,37.76,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,97.0,0.0,1,5.02,Mandeep Singh,4.98
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,2.0,8.98,102.5,68.5,32.12,12.0,11.5,0.0,0.0,8.0,0,0.83,Mayank Dagar,1.35
+117.0,13.0,144.44,0.0,0.0,19.75,131.82,266.67,37.04,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,8.0,0.0,0,4.53,Misbah-ul-Haq,4.31
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,9.0,9.59,34.11,21.33,36.98,11.38,9.8,0.0,0.0,8.0,0,1.91,Mohammad Asif,2.07
+64.0,8.0,77.11,0.0,0.0,10.84,75.68,125.0,60.24,2.0,7.1,35.5,30.0,41.67,11.0,8.0,0.0,8.0,4.0,0,3.93,Mohammad Hafeez,3.69
+221.0,13.0,146.36,0.0,0.0,19.87,164.71,147.3,36.42,15.0,7.58,35.13,27.8,36.69,10.0,6.45,1.0,19.0,23.0,0,6.37,Mohammad Nabi,6.56
+115.0,6.39,102.68,0.0,0.0,12.5,110.0,104.26,49.11,157.0,8.69,26.05,17.98,42.79,11.05,7.78,15.0,33.0,127.0,2,21.0,Mohammed Shami,20.36
+112.0,10.18,88.89,0.0,0.0,11.11,110.0,91.15,55.56,121.0,8.79,30.33,20.7,44.75,10.33,8.21,11.0,22.0,116.0,2,17.25,Mohammed Siraj,17.75
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,36.0,8.28,22.39,16.22,45.03,10.08,8.0,4.0,0.0,27.0,2,5.99,Mohsin Khan,5.89
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,21.0,8.4,29.81,21.29,35.12,11.88,7.97,1.0,0.0,20.0,2,3.72,Mujeeb Ur Rahman,4.09
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,20.0,9.76,28.8,17.7,42.94,13.18,9.02,3.0,0.0,18.0,2,3.87,Mukesh Choudhary,3.98
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,51.0,10.26,27.39,16.02,36.72,12.12,9.27,5.0,0.0,39.0,2,6.45,Mukesh Kumar,6.63
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,74.0,8.26,25.39,18.45,37.51,9.67,7.22,10.0,0.0,60.0,2,10.7,Mustafizur Rahman,10.43
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,16.0,9.72,29.25,18.06,47.75,8.3,10.04,0.0,0.0,14.0,0,3.32,N Burger,2.99
+162.0,18.0,110.2,0.0,0.0,15.65,104.0,81.82,44.22,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,10.0,0.0,0,3.24,N Jagadeesan,3.44
+2375.0,31.25,159.61,0.0,14.0,22.45,138.65,173.04,36.63,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,94.0,0.0,1,6.98,N Pooran,7.32
+3022.0,27.47,137.05,0.0,22.0,19.55,113.26,188.76,39.95,11.0,8.97,29.64,19.82,31.19,7.04,9.55,0.0,118.0,27.0,1,10.33,N Rana,10.46
+140.0,12.73,100.0,0.0,1.0,11.43,94.68,100.0,46.43,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,10.0,0.0,0,3.09,N Saini,2.89
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,9.0,9.53,32.11,20.22,34.62,10.47,9.22,2.0,0.0,8.0,0,3.15,N Thushara,2.79
+784.0,25.29,138.52,0.0,4.0,18.9,115.25,152.0,38.34,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,36.0,0.0,1,5.22,N Wadhera,4.85
+121.0,24.2,101.68,0.0,1.0,12.61,78.79,172.22,47.06,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,6.0,0.0,0,4.45,NK Patel,3.86
+424.0,17.67,138.56,0.0,0.0,16.01,137.5,166.67,36.6,37.0,8.86,27.86,18.86,34.1,11.49,7.28,5.0,30.0,36.0,2,8.67,NLTC Perera,8.81
+82.0,8.2,113.89,0.0,0.0,15.28,110.0,128.3,50.0,52.0,7.88,21.63,16.48,45.16,9.56,7.24,8.0,16.0,38.0,2,11.15,NM Coulter-Nile,11.09
+31.0,7.75,62.0,0.0,0.0,4.0,43.75,68.75,56.0,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,4.0,0.0,0,1.91,NS Naik,1.96
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,20.0,8.75,27.65,18.95,32.98,11.42,6.74,2.0,0.0,17.0,2,4.52,NT Ellis,4.42
+1554.0,21.0,118.81,0.0,6.0,15.29,99.63,144.24,45.8,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,94.0,0.0,1,4.52,NV Ojha,4.7
+546.0,27.3,168.52,0.0,2.0,25.0,151.14,192.42,31.17,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,26.0,0.0,1,5.06,Naman Dhir,5.29
+33.0,8.25,89.19,0.0,0.0,8.11,0.0,112.5,43.24,26.0,9.0,38.69,25.81,43.37,9.51,8.6,1.0,7.0,32.0,2,5.66,Navdeep Saini,5.58
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,26.0,9.31,23.15,14.92,36.34,10.32,9.67,4.0,0.0,17.0,2,3.85,Naveen-ul-Haq,4.29
+657.0,27.38,137.45,0.0,3.0,16.53,89.0,182.14,34.94,13.0,10.32,34.92,20.31,31.44,13.3,10.16,1.0,29.0,20.0,1,5.99,Nithish Kumar Reddy,6.25
+30.0,3.0,55.56,0.0,0.0,5.56,110.0,71.05,68.52,56.0,8.24,24.2,17.62,35.76,8.58,8.5,6.0,14.0,45.0,2,8.97,Noor Ahmad,8.8
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,5.0,8.5,17.0,12.0,36.67,9.0,9.67,0.0,0.0,4.0,0,2.56,O Thomas,2.49
+506.0,38.92,130.41,0.0,4.0,14.69,107.14,151.88,34.28,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,22.0,0.0,1,4.91,OA Shah,5.03
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,11.0,9.44,23.18,14.73,31.48,8.95,6.0,1.0,0.0,8.0,0,3.62,OC McCoy,3.09
+51.0,17.0,115.91,0.0,0.0,13.64,110.0,143.75,52.27,7.0,11.93,25.57,12.86,40.0,15.33,7.75,1.0,6.0,6.0,0,4.64,OF Smith,4.51
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,7.0,10.95,34.43,18.86,25.76,21.0,11.0,0.0,0.0,6.0,0,1.01,P Amarnath,1.19
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,43.0,8.4,24.33,17.37,41.1,11.22,8.17,4.0,0.0,33.0,2,6.02,P Awana,6.02
+127.0,10.58,92.03,0.0,0.0,6.52,110.0,119.05,43.48,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,12.0,0.0,0,2.72,P Dogra,2.5
+23.0,23.0,69.7,0.0,0.0,6.06,110.0,50.0,51.52,2.0,8.62,56.0,39.0,30.77,11.0,4.0,0.0,2.0,5.0,0,2.51,P Dubey,2.71
+340.0,10.0,108.97,0.0,0.0,12.5,75.0,112.45,47.44,102.0,7.94,32.76,24.75,45.44,10.44,6.88,6.0,57.0,119.0,2,15.32,P Kumar,15.36
+365.0,14.04,126.74,0.0,0.0,14.93,62.5,143.95,38.89,38.0,7.99,25.11,18.84,34.78,10.19,7.25,3.0,35.0,42.0,2,9.21,P Negi,9.03
+147.0,21.0,145.54,0.0,0.0,23.76,156.98,120.0,38.61,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,3.95,P Nissanka,4.0
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,9.0,8.81,25.11,17.11,42.86,10.55,9.75,1.0,0.0,8.0,0,2.04,P Parameswaran,2.37
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,3.0,8.69,50.67,35.0,25.71,11.0,9.33,0.0,0.0,5.0,0,2.02,P Sahu,1.93
+1701.0,29.33,155.34,1.0,12.0,24.29,151.78,172.5,38.81,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,59.0,0.0,1,7.11,P Simran Singh,7.24
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,2.0,9.62,77.0,48.0,40.62,12.5,10.0,0.0,0.0,5.0,0,1.67,P Suyal,1.16
+2848.0,22.43,121.19,0.0,13.0,17.62,121.27,140.0,42.26,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,136.0,0.0,1,5.66,PA Patel,5.66
+164.0,16.4,102.5,0.0,0.0,10.62,96.49,300.0,40.62,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,10.0,0.0,0,3.67,PA Reddy,3.68
+277.0,23.08,145.79,0.0,1.0,19.47,158.11,50.0,36.84,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,13.0,0.0,0,4.09,PBB Rajapaksa,4.08
+505.0,22.95,121.98,1.0,2.0,19.57,109.76,207.69,51.93,7.0,8.23,29.57,21.57,30.46,9.23,9.0,1.0,23.0,10.0,1,6.87,PC Valthaty,6.67
+203.0,50.75,130.13,0.0,3.0,14.1,116.13,178.38,36.54,5.0,6.81,20.2,17.8,26.97,4.94,9.0,0.0,7.0,6.0,0,7.39,PD Collingwood,7.36
+1258.0,33.11,172.57,0.0,12.0,28.12,168.92,218.18,36.63,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,40.0,0.0,1,7.41,PD Salt,7.28
+92.0,23.0,127.78,0.0,0.0,12.5,55.56,100.0,34.72,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,2.96,PHKD Mendis,3.35
+612.0,18.55,153.0,0.0,3.0,20.0,20.0,161.33,38.5,83.0,8.93,29.67,19.94,38.67,10.79,8.74,8.0,48.0,73.0,3,15.11,PJ Cummins,14.69
+26.0,3.25,59.09,0.0,0.0,2.27,110.0,62.86,54.55,44.0,8.88,28.8,19.45,39.49,11.36,8.8,4.0,14.0,42.0,2,7.15,PJ Sangwan,7.0
+273.0,14.37,114.71,0.0,1.0,10.5,98.31,183.33,36.13,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,19.0,0.0,0,3.77,PK Garg,3.52
+97.0,32.33,134.72,0.0,1.0,20.83,117.5,140.0,40.28,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,4.63,PN Mankad,4.52
+624.0,10.95,111.23,0.0,0.0,13.55,114.29,124.77,43.67,201.0,8.07,25.77,19.15,35.27,10.02,7.68,15.0,86.0,191.0,3,25.57,PP Chawla,25.69
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,6.0,11.01,24.17,13.17,45.57,9.23,12.3,1.0,0.0,3.0,0,1.53,PP Hinge,1.53
+16.0,1.6,36.36,0.0,0.0,0.0,110.0,39.02,65.91,99.0,7.58,24.23,19.18,37.28,9.42,7.26,8.0,19.0,90.0,2,13.64,PP Ojha,13.35
+1892.0,24.57,147.81,0.0,14.0,23.36,143.43,163.64,37.73,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,79.0,0.0,1,6.91,PP Shaw,6.63
+92.0,11.5,102.22,0.0,0.0,12.22,97.3,118.75,48.89,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,9.0,0.0,0,2.58,PR Shah,2.93
+49.0,49.0,136.11,0.0,0.0,22.22,110.0,75.0,36.11,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,2.0,0.0,0,5.07,PR Veer,4.89
+18.0,18.0,47.37,0.0,0.0,2.63,110.0,42.86,60.53,28.0,7.87,30.93,23.57,32.58,12.5,7.82,2.0,6.0,33.0,2,5.54,PV Tambe,5.57
+53.0,17.67,117.78,0.0,0.0,13.33,110.0,120.0,42.22,15.0,9.75,43.87,27.0,40.74,11.58,9.56,1.0,11.0,20.0,0,5.59,PVD Chameera,5.49
+81.0,5.4,92.05,0.0,0.0,9.09,110.0,104.88,46.59,47.0,8.47,24.13,17.09,34.99,8.32,7.87,3.0,19.0,37.0,2,8.58,PWH de Silva,8.66
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,11.0,9.44,42.91,27.27,38.33,14.57,7.9,0.0,0.0,17.0,0,2.9,Pankaj Singh,2.12
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,4.0,8.27,68.25,49.5,31.31,11.0,8.23,0.0,0.0,11.0,0,2.45,Parvez Rasool,2.16
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,21.0,8.47,22.67,16.05,41.25,10.82,7.59,3.0,0.0,14.0,2,4.15,Prince Yadav,4.37
+828.0,33.12,192.56,1.0,5.0,32.09,189.62,120.0,36.05,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,25.0,0.0,1,6.7,Priyansh Arya,6.7
+3444.0,31.31,134.69,3.0,24.0,18.65,129.2,206.1,38.8,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,118.0,0.0,1,10.0,Q de Kock,10.72
+833.0,13.44,118.32,0.0,1.0,13.21,102.38,134.5,36.51,205.0,7.28,27.91,22.99,35.35,9.06,6.93,10.0,93.0,217.0,3,26.32,R Ashwin,26.12
+342.0,11.79,120.42,0.0,0.0,13.03,100.0,155.78,36.62,82.0,7.55,25.11,19.95,28.55,8.83,7.19,8.0,47.0,91.0,2,13.43,R Bhatia,13.66
+210.0,21.0,112.3,0.0,0.0,13.37,110.0,138.61,44.92,27.0,8.36,34.15,24.52,32.93,11.11,8.14,0.0,22.0,36.0,2,6.89,R Dhawan,7.04
+2174.0,28.23,115.76,0.0,11.0,15.81,106.81,179.78,41.53,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,82.0,0.0,1,6.42,R Dravid,6.04
+159.0,19.88,92.98,0.0,1.0,8.77,73.91,117.02,40.35,14.0,9.46,39.86,25.29,36.16,11.25,8.61,1.0,13.0,18.0,0,4.13,R McLaren,4.05
+1687.0,24.81,139.54,0.0,7.0,17.7,119.73,161.73,35.48,11.0,9.44,46.91,29.82,24.39,12.4,8.0,0.0,81.0,34.0,1,6.15,R Parag,6.6
+486.0,20.25,140.06,0.0,1.0,19.02,115.38,188.06,42.36,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,28.0,0.0,0,3.65,R Powell,3.88
+51.0,10.2,104.08,0.0,0.0,10.2,110.0,103.57,48.98,17.0,7.14,18.76,15.76,51.12,11.22,6.28,1.0,7.0,12.0,0,5.74,R Rampaul,5.71
+413.0,25.81,142.91,0.0,2.0,19.38,136.19,266.67,34.26,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,18.0,0.0,0,5.43,R Ravindra,4.76
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,32.0,8.77,20.47,14.0,31.47,11.07,7.83,2.0,0.0,25.0,2,5.43,R Sai Kishore,5.32
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+66.0,6.0,88.0,0.0,0.0,10.67,110.0,80.6,61.33,42.0,7.11,26.19,22.1,37.07,9.15,6.58,1.0,19.0,44.0,2,8.58,R Sharma,8.29
+224.0,22.4,200.0,0.0,1.0,30.36,110.0,238.57,31.25,15.0,12.14,39.4,19.47,30.48,14.7,7.8,2.0,16.0,22.0,0,6.71,R Shepherd,6.73
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+2291.0,26.64,138.43,0.0,12.0,18.97,137.7,168.0,36.44,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,98.0,0.0,1,5.65,RA Tripathi,5.95
+129.0,8.06,104.03,0.0,0.0,14.52,110.0,111.83,54.03,76.0,7.86,29.21,22.3,35.22,9.09,7.18,6.0,22.0,79.0,2,12.21,RD Chahar,12.27
+2680.0,39.41,136.04,2.0,21.0,17.72,126.1,183.76,35.43,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,78.0,0.0,1,8.23,RD Gaikwad,8.29
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+83.0,13.83,113.7,0.0,1.0,19.18,115.62,120.0,54.79,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,6.0,0.0,0,3.62,RE Levi,3.42
+159.0,14.45,112.77,0.0,0.0,13.48,102.56,130.56,44.68,25.0,6.98,20.6,17.72,35.89,8.95,8.89,2.0,15.0,21.0,2,7.12,RE van der Merwe,7.3
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+32.0,10.67,106.67,0.0,0.0,13.33,110.0,120.0,43.33,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,3.33,RJ Peterson,3.37
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+1349.0,32.12,158.52,1.0,11.0,21.03,119.55,162.07,31.73,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,45.0,0.0,1,6.5,RM Patidar,6.68
+326.0,19.18,138.72,0.0,1.0,17.45,85.71,173.87,32.77,3.0,7.23,31.33,26.0,28.21,12.0,9.0,0.0,21.0,10.0,0,5.31,RN ten Doeschate,5.31
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+52.0,3.47,68.42,0.0,0.0,3.95,110.0,75.76,51.32,100.0,8.17,24.17,17.75,44.51,9.55,7.38,12.0,28.0,82.0,2,14.33,RP Singh,14.34
+3756.0,33.24,146.32,2.0,20.0,20.06,119.41,203.87,34.44,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,131.0,0.0,1,8.77,RR Pant,8.93
+67.0,22.33,104.69,0.0,0.0,10.94,110.0,120.75,42.19,15.0,7.58,35.87,28.4,40.85,10.0,7.27,0.0,9.0,26.0,0,6.46,RR Powar,6.3
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+473.0,22.52,154.58,0.0,2.0,22.88,130.43,215.38,37.58,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,22.0,0.0,0,4.84,RR Rossouw,4.69
+73.0,24.33,97.33,0.0,0.0,9.33,106.38,120.0,41.33,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,4.0,0.0,0,3.08,RR Sarwan,3.08
+531.0,27.95,117.22,0.0,3.0,12.14,94.44,161.54,35.32,13.0,8.77,23.15,15.85,32.04,10.17,8.0,1.0,22.0,14.0,1,6.4,RS Bopara,6.37
+91.0,11.38,71.09,0.0,0.0,5.47,67.05,120.0,53.12,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,9.0,0.0,0,2.13,RT Ponting,2.17
+50.0,7.14,98.04,0.0,0.0,11.76,0.0,125.71,49.02,6.0,8.12,21.67,16.0,39.58,20.0,4.0,0.0,9.0,9.0,0,4.38,RV Gomez,4.3
+80.0,16.0,109.59,0.0,0.0,10.96,110.0,132.43,35.62,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,3.4,RV Patel,3.4
+4954.0,27.22,130.92,0.0,27.0,17.52,123.26,177.35,38.69,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,197.0,0.0,1,8.97,RV Uthappa,9.19
+363.0,21.35,134.44,0.0,2.0,20.74,127.88,120.0,49.63,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,18.0,0.0,0,3.97,Rahmanullah Gurbaz,3.96
+299.0,18.69,145.15,0.0,0.0,17.48,80.0,186.36,34.47,7.0,9.34,13.57,8.71,31.15,8.07,9.0,1.0,26.0,6.0,0,6.11,Ramandeep Singh,6.22
+613.0,13.62,157.58,0.0,1.0,21.85,110.0,165.89,39.85,174.0,7.31,23.71,19.47,37.58,8.59,7.15,18.0,69.0,144.0,3,25.11,Rashid Khan,25.15
+40.0,8.0,100.0,0.0,0.0,12.5,110.0,142.11,47.5,17.0,9.95,32.29,19.47,32.33,10.12,8.95,3.0,6.0,18.0,0,4.9,Rasikh Salam,4.54
+45.0,4.09,65.22,0.0,0.0,5.8,110.0,66.67,63.77,89.0,8.33,28.02,20.18,35.3,11.13,7.73,6.0,18.0,84.0,2,12.51,Ravi Bishnoi,12.24
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+59.0,19.67,103.51,0.0,0.0,12.28,110.0,112.2,42.11,48.0,8.34,22.02,15.83,39.74,9.86,7.64,5.0,10.0,38.0,2,9.43,S Aravind,9.77
+63.0,12.6,140.0,0.0,0.0,20.0,110.0,141.18,42.22,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,6.0,0.0,0,4.04,S Arora,3.95
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+1441.0,30.02,119.29,0.0,11.0,15.07,82.17,163.68,37.91,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,65.0,0.0,1,6.92,S Badrinath,6.24
+25.0,8.33,80.65,0.0,0.0,12.9,80.65,120.0,58.06,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,3.0,0.0,0,2.99,S Chanderpaul,2.68
+6769.0,35.07,127.62,2.0,51.0,17.36,121.69,165.78,37.14,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,221.0,0.0,1,13.49,S Dhawan,12.86
+2009.0,30.44,140.69,0.0,10.0,17.51,100.0,159.26,35.85,7.0,10.83,37.14,20.57,22.92,12.35,9.0,1.0,83.0,17.0,1,6.38,S Dube,6.34
+180.0,13.85,106.51,0.0,0.0,12.43,42.86,135.23,39.64,53.0,8.22,25.62,18.7,32.9,7.58,7.33,5.0,22.0,51.0,2,9.7,S Gopal,9.89
+20.0,6.67,55.56,0.0,0.0,2.78,110.0,58.06,63.89,63.0,8.79,28.13,19.19,33.91,9.26,9.2,5.0,9.0,55.0,2,9.45,S Kaul,8.47
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+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,5.0,9.78,45.0,27.6,35.51,10.25,10.83,0.0,0.0,9.0,0,1.1,S Ladda,1.42
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+39.0,2.44,44.83,0.0,0.0,2.3,110.0,44.23,65.52,54.0,7.63,33.33,26.2,33.14,12.0,7.63,2.0,20.0,70.0,2,7.08,S Nadeem,9.6
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,6.0,11.49,33.83,17.67,35.85,16.5,9.08,1.0,0.0,6.0,0,2.14,S Narwal,1.7
+91.0,18.2,115.19,0.0,0.0,12.66,87.5,148.78,40.51,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,8.0,0.0,0,3.73,S Rana,3.55
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,8.0,7.69,27.88,21.75,34.48,11.0,7.2,0.0,0.0,8.0,0,2.52,S Randiv,2.83
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,8.0,9.18,32.12,21.0,44.64,11.0,9.46,1.0,0.0,10.0,0,3.01,S Sandeep Warrier,2.54
+368.0,20.44,127.34,0.0,2.0,17.99,128.45,44.44,47.06,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,20.0,0.0,0,3.91,S Sohal,3.65
+34.0,11.33,61.82,0.0,0.0,10.91,110.0,62.96,74.55,43.0,8.33,28.4,20.47,47.27,12.0,7.65,2.0,12.0,44.0,2,6.98,S Sreesanth,7.13
+31.0,15.5,86.11,0.0,0.0,8.33,116.67,120.0,44.44,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,2.0,0.0,0,3.2,S Sriram,2.89
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,6.0,8.73,50.67,34.83,47.37,17.45,8.38,0.0,0.0,14.0,0,2.07,S Tyagi,1.98
+145.0,18.12,133.03,0.0,1.0,22.02,131.71,120.0,44.95,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,8.0,0.0,0,3.55,S Vidyut,3.88
+423.0,22.26,125.52,0.0,2.0,19.58,128.21,120.0,44.21,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,19.0,0.0,0,4.01,SA Asnodkar,3.88
+4468.0,34.37,148.09,2.0,30.0,21.31,135.1,195.47,34.14,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,158.0,0.0,1,10.4,SA Yadav,10.68
+49.0,8.17,84.48,0.0,0.0,6.9,110.0,170.59,48.28,6.0,8.84,36.83,25.0,38.0,13.33,5.0,0.0,7.0,9.0,0,3.12,SB Bangar,3.15
+189.0,27.0,173.39,0.0,0.0,24.77,233.33,198.53,28.44,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,13.0,0.0,0,5.47,SB Dubey,5.16
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,50.0,8.15,29.48,21.7,32.72,12.68,7.26,3.0,0.0,57.0,2,7.37,SB Jakati,7.03
+131.0,18.71,99.24,0.0,0.0,9.85,73.91,116.67,38.64,9.0,7.72,30.89,24.0,39.35,10.14,6.09,1.0,10.0,11.0,0,4.55,SB Styris,4.79
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,5.0,8.65,29.4,20.4,49.02,13.5,8.0,1.0,0.0,8.0,0,2.43,SB Wagh,2.41
+1349.0,24.98,107.32,0.0,7.0,14.24,97.7,175.38,47.26,12.0,8.04,30.83,23.0,29.71,25.0,8.17,2.0,56.0,20.0,1,6.82,SC Ganguly,6.84
+99.0,14.14,112.5,0.0,0.0,13.64,58.33,126.67,39.77,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,8.0,0.0,0,2.98,SD Chitnis,3.09
+183.0,22.88,150.0,0.0,0.0,19.67,138.57,100.0,36.89,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,9.0,0.0,0,3.75,SD Hope,4.0
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,9.0,7.26,25.0,20.67,40.86,9.67,6.33,0.0,0.0,8.0,0,2.7,SE Bond,3.04
+2489.0,38.29,133.39,1.0,20.0,18.6,112.66,163.46,37.14,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,69.0,0.0,1,8.06,SE Marsh,8.15
+500.0,22.73,135.87,0.0,1.0,17.93,26.32,148.47,42.39,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,26.0,0.0,1,3.9,SE Rutherford,4.34
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,5.0,10.26,57.8,33.8,39.05,9.07,10.21,0.0,0.0,9.0,0,1.84,SH Johnson,1.62
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,15.0,9.45,29.6,18.8,37.59,11.43,9.59,0.0,0.0,14.0,0,2.63,SJ Srivastava,2.65
+5536.0,33.15,137.54,1.0,39.0,17.64,129.17,173.37,33.14,30.0,7.53,37.97,30.27,32.93,8.97,6.76,0.0,200.0,69.0,3,15.82,SK Raina,15.9
+71.0,14.2,105.97,0.0,0.0,16.42,107.81,120.0,56.72,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,2.79,SK Rasheed,3.13
+42.0,5.25,72.41,0.0,0.0,6.9,110.0,75.47,58.62,73.0,7.75,26.63,20.63,35.06,9.76,7.68,7.0,14.0,75.0,2,11.02,SK Trivedi,11.08
+198.0,10.42,92.96,0.0,0.0,9.39,110.0,102.65,46.95,60.0,7.36,24.42,19.9,37.02,10.05,7.16,5.0,27.0,54.0,2,10.63,SK Warne,10.49
+88.0,7.33,88.89,0.0,0.0,11.11,110.0,89.89,55.56,188.0,7.4,18.54,15.04,43.07,8.19,6.51,24.0,20.0,122.0,2,26.2,SL Malinga,26.47
+997.0,24.32,136.39,0.0,6.0,17.24,110.06,186.8,35.02,63.0,9.84,32.68,19.92,33.15,11.39,8.37,7.0,53.0,63.0,3,12.77,SM Curran,12.79
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,3.0,7.4,24.67,20.0,56.67,8.67,2.33,0.0,0.0,3.0,0,2.79,SM Harwood,2.69
+241.0,24.1,130.27,0.0,2.0,18.38,129.75,130.77,43.24,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,11.0,0.0,0,4.43,SM Katich,4.05
+147.0,18.38,132.43,0.0,0.0,18.02,50.0,205.71,37.84,13.0,6.67,23.62,21.23,47.83,11.0,6.51,1.0,8.0,13.0,0,6.33,SM Pollock,6.48
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,9.0,6.59,23.44,21.33,39.06,7.5,5.29,0.0,0.0,8.0,0,3.78,SMSM Senanayake,3.76
+746.0,20.72,137.13,0.0,2.0,19.49,137.78,184.83,35.66,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,43.0,0.0,1,4.4,SN Khan,4.69
+339.0,12.11,132.42,0.0,1.0,17.19,0.0,139.42,37.5,121.0,9.52,28.71,18.1,35.71,11.02,9.28,10.0,42.0,107.0,2,17.53,SN Thakur,17.7
+1560.0,26.9,147.73,0.0,5.0,18.37,105.63,178.65,34.38,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,84.0,0.0,1,5.23,SO Hetmyer,5.2
+196.0,21.78,120.99,0.0,0.0,18.52,116.0,120.0,48.15,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,10.0,0.0,0,2.93,SP Fleming,3.47
+293.0,14.65,100.0,0.0,1.0,11.95,97.3,78.57,44.71,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,21.0,0.0,0,2.36,SP Goswami,2.88
+61.0,10.17,107.02,0.0,0.0,10.53,111.11,95.0,35.09,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,8.0,0.0,0,2.76,SP Jackson,3.06
+1820.0,18.57,162.79,1.0,7.0,27.82,164.78,92.38,46.42,221.0,6.92,23.59,20.46,39.68,7.96,6.71,18.0,122.0,194.0,3,30.26,SP Narine,29.81
+2495.0,31.99,129.21,1.0,11.0,14.81,120.71,173.67,31.18,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,93.0,0.0,1,6.34,SPD Smith,6.8
+2334.0,32.87,120.25,1.0,13.0,16.74,114.85,169.23,39.82,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,78.0,0.0,1,7.08,SR Tendulkar,7.05
+3880.0,31.04,138.52,4.0,21.0,20.24,115.16,187.9,41.56,107.0,8.11,25.63,18.96,41.89,10.35,7.19,12.0,141.0,105.0,3,23.02,SR Watson,22.98
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,6.0,8.85,29.5,20.0,49.17,16.0,7.71,0.0,0.0,6.0,0,2.02,SS Cottrell,2.14
+4044.0,33.98,135.12,0.0,31.0,16.94,110.18,193.13,34.05,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,138.0,0.0,1,9.08,SS Iyer,8.87
+126.0,14.0,120.0,0.0,0.0,14.29,87.5,121.05,45.71,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,10.0,0.0,0,3.02,SS Prabhudessai,3.26
+1494.0,30.49,120.97,0.0,8.0,13.04,109.63,151.59,34.66,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,73.0,0.0,1,5.26,SS Tiwary,5.62
+768.0,26.48,145.18,1.0,4.0,23.25,139.85,120.0,47.45,16.0,8.08,24.75,18.38,29.25,10.71,11.5,3.0,30.0,21.0,1,7.95,ST Jayasuriya,7.98
+880.0,20.0,129.03,0.0,0.0,14.81,69.39,160.0,36.8,28.0,7.71,27.25,21.21,34.34,10.14,7.41,1.0,66.0,63.0,3,8.3,STR Binny,11.37
+5008.0,31.9,140.83,5.0,26.0,18.17,127.86,195.83,34.39,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,179.0,0.0,1,11.79,SV Samson,11.69
+503.0,19.35,130.65,0.0,3.0,15.58,125.64,165.22,36.1,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,27.0,0.0,1,4.08,SW Billings,4.56
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,26.0,8.47,25.69,18.19,46.93,8.67,8.12,4.0,0.0,21.0,2,5.63,SW Tait,5.27
+144.0,14.4,125.22,0.0,0.0,13.91,0.0,184.78,38.26,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,11.0,0.0,0,3.23,Sachin Baby,3.49
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,7.0,8.32,21.0,15.14,40.57,7.33,7.71,1.0,0.0,4.0,0,3.41,Sakib Hussain,3.21
+193.0,27.57,120.62,0.0,1.0,20.0,110.28,200.0,46.88,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,7.0,0.0,0,4.95,Salman Butt,4.65
+381.0,29.31,139.56,0.0,3.0,19.05,56.25,186.27,40.29,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,16.0,0.0,0,3.87,Sameer Rizvi,4.28
+60.0,12.0,80.0,0.0,0.0,5.33,110.0,80.0,41.33,169.0,8.16,25.54,18.78,39.95,10.71,7.11,17.0,26.0,141.0,2,21.63,Sandeep Sharma,22.18
+560.0,18.67,121.21,0.0,1.0,11.9,80.56,156.85,34.63,22.0,9.71,44.05,27.23,27.55,8.28,9.9,2.0,39.0,46.0,3,7.59,Shahbaz Ahmed,7.59
+81.0,10.12,176.09,0.0,0.0,28.26,226.32,220.0,43.48,9.0,7.9,26.33,20.0,36.11,7.0,7.0,1.0,9.0,10.0,0,7.4,Shahid Afridi,7.54
+795.0,20.38,125.2,0.0,2.0,14.8,88.46,150.55,33.39,71.0,7.54,26.25,20.9,35.24,10.22,7.08,3.0,52.0,70.0,3,13.07,Shakib Al Hasan,12.98
+843.0,42.15,156.4,0.0,5.0,20.22,137.14,197.03,29.5,4.0,8.95,22.75,15.25,26.23,15.5,9.0,0.0,38.0,6.0,1,6.46,Shashank Singh,6.5
+51.0,5.67,91.07,0.0,0.0,10.71,110.0,102.13,53.57,33.0,8.86,29.03,19.67,41.14,11.41,8.13,1.0,11.0,32.0,2,6.39,Shivam Mavi,6.88
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,4.0,8.68,41.25,28.5,32.46,8.33,2.0,0.0,0.0,5.0,0,2.11,Shivam Sharma,2.15
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,7.0,9.47,30.0,19.0,29.32,11.0,9.0,1.0,0.0,7.0,0,2.54,Shivang Kumar,2.2
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,5.0,9.06,30.8,20.4,34.31,20.0,10.5,0.0,0.0,7.0,0,1.8,Shoaib Ahmed,1.77
+52.0,13.0,113.04,0.0,0.0,10.87,116.67,50.0,30.43,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,3.03,Shoaib Malik,3.08
+4196.0,40.35,138.71,4.0,29.0,17.69,130.55,157.21,31.7,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,121.0,0.0,1,10.82,Shubman Gill,10.9
+182.0,26.0,133.82,0.0,1.0,14.71,129.41,125.93,38.24,3.0,10.31,48.67,28.33,27.06,11.0,14.0,0.0,9.0,7.0,0,4.01,Sikandar Raza,4.16
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,12.0,10.15,34.25,20.25,44.86,13.0,9.88,1.0,0.0,14.0,0,2.18,Simarjeet Singh,2.45
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,24.0,6.68,11.46,10.29,52.63,6.88,6.61,6.0,0.0,11.0,2,6.6,Sohail Tanvir,6.51
+43.0,10.75,138.71,0.0,0.0,22.58,160.0,77.78,38.71,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,3.46,Sunny Singh,3.9
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,26.0,8.66,40.12,27.81,35.27,8.05,8.0,4.0,0.0,34.0,2,5.17,Suyash Sharma,5.24
+51.0,10.2,113.33,0.0,0.0,13.33,110.0,136.84,44.44,7.0,8.93,34.43,23.14,24.07,11.0,8.54,0.0,9.0,14.0,0,5.07,Swapnil Singh,4.73
+48.0,16.0,88.89,0.0,0.0,14.81,90.57,120.0,61.11,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,3.0,0.0,0,3.25,T Kohler-Cadmore,2.85
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,82.0,9.12,28.26,18.6,32.33,10.36,8.34,9.0,0.0,71.0,2,10.58,T Natarajan,10.79
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,3.0,9.25,61.67,40.0,26.67,15.0,8.0,0.0,0.0,5.0,0,1.66,T Shamsi,1.5
+912.0,45.6,155.9,0.0,5.0,18.8,121.88,216.74,24.62,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,37.0,0.0,1,5.86,T Stubbs,5.89
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,10.0,7.29,16.4,13.5,44.44,11.54,5.73,1.0,0.0,6.0,0,2.34,T Thushara,2.84
+86.0,9.56,102.38,0.0,0.0,9.52,110.0,102.99,44.05,152.0,8.52,25.76,18.14,42.64,10.72,7.43,13.0,24.0,122.0,2,19.58,TA Boult,19.95
+120.0,10.91,113.21,0.0,0.0,11.32,110.0,135.06,42.45,57.0,8.83,31.12,21.16,39.39,10.85,8.11,8.0,19.0,54.0,2,9.84,TG Southee,9.98
+1029.0,36.75,174.41,0.0,2.0,23.9,110.0,202.9,31.19,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,51.0,0.0,1,5.75,TH David,5.82
+127.0,25.4,118.69,0.0,1.0,12.15,110.0,131.82,33.64,13.0,10.89,33.23,18.31,24.79,13.61,9.57,1.0,10.0,13.0,0,4.83,TK Curran,4.86
+45.0,7.5,109.76,0.0,0.0,17.07,116.22,50.0,53.66,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,6.0,0.0,0,3.63,TL Seifert,3.36
+676.0,22.53,117.98,0.0,2.0,14.14,102.5,131.51,42.06,7.0,7.92,28.29,21.43,29.33,12.0,7.0,0.0,39.0,11.0,1,5.42,TL Suman,5.32
+1153.0,28.12,115.18,0.0,9.0,16.38,108.15,188.37,45.05,5.0,8.15,73.6,54.2,29.89,11.54,7.15,0.0,50.0,25.0,1,6.31,TM Dilshan,6.29
+1332.0,32.49,161.45,1.0,8.0,25.58,169.57,195.83,36.73,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,45.0,0.0,1,7.17,TM Head,7.21
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,1.0,11.74,137.0,70.0,27.14,14.62,10.0,0.0,0.0,3.0,0,0.2,TP Sudhindra,0.47
+75.0,15.0,131.58,0.0,0.0,19.3,20.0,182.14,43.86,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,5.0,0.0,0,4.04,TR Birt,3.89
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,11.0,10.02,31.73,19.0,35.89,10.75,8.83,1.0,0.0,10.0,0,2.71,TS Mills,2.64
+53.0,17.67,155.88,0.0,0.0,17.65,110.0,155.88,35.29,59.0,9.97,30.0,18.05,38.59,11.59,8.9,6.0,9.0,50.0,2,10.41,TU Deshpande,10.39
+24.0,24.0,77.42,0.0,0.0,9.68,76.0,120.0,54.84,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,1.0,0.0,0,3.18,Tanush Kotian,3.02
+1680.0,38.18,145.2,1.0,8.0,18.58,123.4,196.32,31.89,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,58.0,0.0,1,6.78,Tilak Varma,6.61
+300.0,15.0,100.33,0.0,1.0,13.71,94.8,87.5,51.84,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,20.0,0.0,0,2.93,UBT Chand,2.95
+127.0,25.4,128.28,0.0,0.0,17.17,137.5,120.0,37.37,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,6.0,0.0,0,4.0,UT Khawaja,3.89
+208.0,10.4,103.48,0.0,0.0,12.44,110.0,114.29,47.76,163.0,8.72,27.25,18.75,41.28,11.11,7.95,21.0,48.0,147.0,2,23.55,UT Yadav,22.57
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,14.0,8.8,14.14,9.64,43.7,11.38,6.43,2.0,0.0,6.0,0,3.43,Umar Gul,3.34
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,31.0,9.53,25.32,15.94,44.13,10.75,12.92,4.0,0.0,26.0,2,5.2,Umran Malik,4.76
+72.0,18.0,194.59,0.0,0.0,32.43,176.92,120.0,40.54,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,4.0,0.0,0,4.55,Urvil Patel,4.68
+9022.0,38.07,133.8,8.0,66.0,16.57,123.74,202.63,33.01,5.0,8.87,74.2,50.2,21.91,16.29,6.21,0.0,267.0,26.0,1,18.31,V Kohli,13.6
+154.0,25.67,173.03,0.0,0.0,29.21,250.0,172.34,35.96,14.0,9.22,31.71,20.64,33.56,5.69,8.74,0.0,9.0,17.0,0,7.95,V Nigam,7.92
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,12.0,8.82,25.0,17.0,36.76,9.0,8.47,1.0,0.0,9.0,0,2.82,V Pratap Singh,3.06
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,6.0,8.96,18.17,12.17,34.25,11.08,9.0,1.0,0.0,5.0,0,2.31,V Puthur,2.46
+2728.0,27.84,156.78,2.0,16.0,25.29,146.67,235.71,37.36,6.0,10.41,39.33,22.67,33.09,12.6,18.67,0.0,104.0,15.0,1,9.11,V Sehwag,8.95
+1233.0,26.8,129.52,0.0,7.0,14.29,91.72,172.84,32.35,12.0,8.67,28.67,19.83,33.61,7.64,9.0,0.0,63.0,22.0,1,7.77,V Shankar,7.75
+652.0,40.75,215.18,2.0,3.0,37.29,218.4,120.0,38.28,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,16.0,0.0,1,6.81,V Suryavanshi,7.0
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,1.0,9.0,90.0,60.0,31.67,11.0,5.5,0.0,0.0,3.0,0,1.7,V Viyaskanth,1.63
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,49.0,9.5,26.96,17.02,39.93,10.25,8.81,3.0,0.0,39.0,2,6.36,VG Arora,6.35
+50.0,12.5,70.42,0.0,0.0,5.63,110.0,74.24,59.15,46.0,9.22,33.2,21.61,41.75,12.04,8.65,1.0,12.0,50.0,2,6.88,VR Aaron,7.01
+1497.0,29.94,137.47,1.0,12.0,18.73,130.65,182.5,38.38,3.0,10.74,48.33,27.0,20.99,11.71,9.0,0.0,57.0,9.0,1,6.29,VR Iyer,7.03
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,16.0,9.15,34.31,22.5,33.61,9.24,9.67,2.0,0.0,18.0,0,3.29,VRV Singh,3.12
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,6.0,7.84,44.67,34.17,42.93,10.29,7.8,0.0,0.0,13.0,0,2.51,VS Malik,2.41
+282.0,17.62,106.02,0.0,1.0,14.29,100.0,50.0,44.74,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,20.0,0.0,0,3.55,VVS Laxman,3.54
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,22.0,9.04,23.23,15.41,37.46,9.29,11.0,2.0,0.0,17.0,2,3.83,VY Mahesh,4.04
+0.0,0.0,0.0,0.0,0.0,0.0,110.0,120.0,30.0,28.0,10.15,28.46,16.82,31.63,12.0,11.33,3.0,0.0,22.0,2,4.43,Vijaykumar Vyshak,4.32
+56.0,9.33,100.0,0.0,0.0,10.71,47.62,150.0,46.43,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,6.0,0.0,0,3.44,Vishnu Vinod,3.18
+69.0,69.0,146.81,0.0,1.0,23.4,111.54,120.0,34.04,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,1.0,0.0,0,5.28,Vivrant Sharma,4.95
+130.0,18.57,107.44,0.0,1.0,14.05,86.46,120.0,46.28,0.0,10.5,50.0,40.0,30.0,11.0,9.0,0.0,8.0,0.0,0,4.07,W Jaffer,3.5
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data/processed/models/win_prob_features.json

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data/processed/models/win_prob_model.pkl

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data/processed/models/win_prob_model_uncalibrated.pkl

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data/processed/team_season_stats.csv

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+2010,Royal Challengers Bangalore,16,8,50.0,6,1
+2010,Delhi Capitals,14,7,50.0,8,5
+2011,Chennai Super Kings,16,11,68.8,9,7
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+2011,Kochi Tuskers Kerala,14,6,42.9,8,3
+2011,Delhi Capitals,14,4,28.6,9,4
+2011,Pune Warriors India,14,4,28.6,5,2
+2011,Kolkata Knight Riders,15,8,53.3,7,1
+2011,Rajasthan Royals,13,6,46.2,7,1
+2011,Royal Challengers Bangalore,16,10,62.5,6,0
+2011,Kings XI Punjab,14,7,50.0,10,3
+2011,Mumbai Indians,16,10,62.5,6,1
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+2012,Kolkata Knight Riders,17,12,70.6,7,4
+2012,Mumbai Indians,17,10,58.8,11,3
+2012,Rajasthan Royals,16,7,43.8,10,8
+2012,Royal Challengers Bangalore,15,8,53.3,6,1
+2012,Deccan Chargers,15,4,26.7,9,7
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+2012,Delhi Capitals,18,11,61.1,10,2
+2012,Kings XI Punjab,16,8,50.0,8,4
+2013,Kolkata Knight Riders,16,6,37.5,12,6
+2013,Royal Challengers Bangalore,16,9,56.2,5,2
+2013,Sunrisers Hyderabad,17,10,58.8,7,6
+2013,Delhi Capitals,16,3,18.8,5,3
+2013,Chennai Super Kings,18,12,66.7,8,5
+2013,Pune Warriors India,16,4,25.0,9,5
+2013,Rajasthan Royals,18,11,61.1,11,6
+2013,Mumbai Indians,19,13,68.4,12,10
+2013,Kings XI Punjab,16,8,50.0,7,2
+2014,Mumbai Indians,15,7,46.7,6,3
+2014,Delhi Capitals,14,2,14.3,4,1
+2014,Chennai Super Kings,16,10,62.5,10,4
+2014,Sunrisers Hyderabad,14,6,42.9,7,4
+2014,Royal Challengers Bangalore,14,5,35.7,9,2
+2014,Kolkata Knight Riders,16,11,68.8,9,2
+2014,Rajasthan Royals,14,7,50.0,8,2
+2014,Kings XI Punjab,17,12,70.6,7,1
+2015,Kolkata Knight Riders,13,7,53.8,8,1
+2015,Chennai Super Kings,17,10,58.8,10,7
+2015,Kings XI Punjab,14,3,21.4,7,3
+2015,Delhi Capitals,14,5,35.7,5,2
+2015,Mumbai Indians,16,10,62.5,7,5
+2015,Royal Challengers Bangalore,16,8,50.0,10,2
+2015,Rajasthan Royals,14,7,50.0,6,1
+2015,Sunrisers Hyderabad,14,7,50.0,6,4
+2016,Mumbai Indians,14,7,50.0,9,2
+2016,Kolkata Knight Riders,15,8,53.3,6,0
+2016,Kings XI Punjab,14,4,28.6,6,2
+2016,Royal Challengers Bangalore,16,9,56.2,6,1
+2016,Gujarat Lions,16,9,56.2,8,0
+2016,Delhi Capitals,14,7,50.0,8,0
+2016,Sunrisers Hyderabad,17,11,64.7,10,3
+2016,Rising Pune Supergiants,14,5,35.7,7,3
+2017,Sunrisers Hyderabad,14,8,57.1,5,1
+2017,Rising Pune Supergiants,16,10,62.5,6,0
+2017,Gujarat Lions,14,4,28.6,7,1
+2017,Kings XI Punjab,14,7,50.0,4,0
+2017,Royal Challengers Bangalore,13,3,23.1,9,4
+2017,Mumbai Indians,17,12,70.6,11,2
+2017,Kolkata Knight Riders,16,9,56.2,9,0
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+2018,Mumbai Indians,14,6,42.9,5,1
+2018,Delhi Capitals,14,5,35.7,8,3
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+2018,Rajasthan Royals,15,7,46.7,6,2
+2018,Kolkata Knight Riders,16,9,56.2,9,0
+2018,Kings XI Punjab,14,6,42.9,7,1
+2018,Chennai Super Kings,16,11,68.8,11,1
+2018,Sunrisers Hyderabad,17,10,58.8,7,2
+2019,Royal Challengers Bangalore,14,5,35.7,4,0
+2019,Sunrisers Hyderabad,15,6,40.0,4,0
+2019,Delhi Capitals,16,10,62.5,10,2
+2019,Kings XI Punjab,14,6,42.9,6,0
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+2019,Mumbai Indians,16,11,68.8,8,3
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+2019,Chennai Super Kings,17,10,58.8,12,3
+2020,Mumbai Indians,16,11,68.8,8,4
+2020,Delhi Capitals,17,9,52.9,10,5
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+2020,Rajasthan Royals,14,6,42.9,7,2
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+2020,Sunrisers Hyderabad,16,8,50.0,11,4
+2020,Kolkata Knight Riders,14,7,50.0,6,4
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+2021,Mumbai Indians,14,7,50.0,8,3
+2021,Chennai Super Kings,16,11,68.8,6,2
+2021,Kolkata Knight Riders,17,9,52.9,8,1
+2021,Punjab Kings,14,6,42.9,5,1
+2021,Royal Challengers Bangalore,15,9,60.0,10,4
+2021,Delhi Capitals,16,10,62.5,8,1
+2021,Rajasthan Royals,14,5,35.7,8,1
+2021,Sunrisers Hyderabad,14,3,21.4,7,3
+2022,Chennai Super Kings,14,4,28.6,6,2
+2022,Mumbai Indians,14,4,28.6,9,0
+2022,Royal Challengers Bangalore,16,9,56.2,8,2
+2022,Lucknow Super Giants,15,9,60.0,7,2
+2022,Rajasthan Royals,17,10,58.8,4,2
+2022,Kolkata Knight Riders,14,6,42.9,8,1
+2022,Punjab Kings,14,7,50.0,4,1
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+2023,Chennai Super Kings,16,10,62.5,10,4
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+2023,Sunrisers Hyderabad,14,4,28.6,7,3
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+2024,Lucknow Super Giants,14,7,50.0,9,4
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+2025,Mumbai Indians,16,9,56.2,8,1
+2025,Lucknow Super Giants,14,6,42.9,5,1
+2025,Punjab Kings,18,10,55.6,13,6
+2025,Rajasthan Royals,14,4,28.6,7,0
+2025,Royal Challengers Bangalore,15,11,73.3,7,0
+2025,Gujarat Titans,15,9,60.0,7,0
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+2026,Sunrisers Hyderabad,8,5,62.5,1,0
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+2026,Rajasthan Royals,9,6,66.7,6,2
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notebooks/01_setup_and_inspection.ipynb

@@ -0,0 +1,475 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efe72401-0379-4732-9c40-17c00ecd6b5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from pathlib import Path\n",
+    "\n",
+    "RAW = Path(r\"E:\\ipl-analytics\\data\\raw\")\n",
+    "PROCESSED = Path(r\"E:\\ipl-analytics\\data\\processed\")\n",
+    "PROCESSED.mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3a243c35-ed5f-46ed-991c-c929cb4b2015",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== matches.csv ===\n",
+      "Shape : (1208, 20)\n",
+      "Columns : ['match_id', 'season', 'city', 'date', 'match_type', 'player_of_match', 'venue', 'team1', 'team2', 'toss_winner', 'toss_decision', 'winner', 'result', 'result_margin', 'target_runs', 'target_overs', 'super_over', 'method', 'umpire1', 'umpire2']\n",
+      "\n",
+      "=== deliveries ===\n",
+      "Shape : (287271, 17)\n",
+      "Columns : ['match_id', 'inning', 'batting_team', 'bowling_team', 'over', 'ball', 'batter', 'bowler', 'non_striker', 'batsman_runs', 'extra_runs', 'total_runs', 'extras_type', 'is_wicket', 'player_dismissed', 'dismissal_kind', 'fielder']\n",
+      "\n",
+      "=== matches datatypes ===\n",
+      "match_id             int64\n",
+      "season                 str\n",
+      "city                   str\n",
+      "date                   str\n",
+      "match_type             str\n",
+      "player_of_match        str\n",
+      "venue                  str\n",
+      "team1                  str\n",
+      "team2                  str\n",
+      "toss_winner            str\n",
+      "toss_decision          str\n",
+      "winner                 str\n",
+      "result                 str\n",
+      "result_margin      float64\n",
+      "target_runs        float64\n",
+      "target_overs       float64\n",
+      "super_over             str\n",
+      "method                 str\n",
+      "umpire1                str\n",
+      "umpire2                str\n",
+      "dtype: object \n",
+      "\n",
+      "=== deliveries datatypes ===\n",
+      "match_id            int64\n",
+      "inning              int64\n",
+      "batting_team          str\n",
+      "bowling_team          str\n",
+      "over                int64\n",
+      "ball                int64\n",
+      "batter                str\n",
+      "bowler                str\n",
+      "non_striker           str\n",
+      "batsman_runs        int64\n",
+      "extra_runs          int64\n",
+      "total_runs          int64\n",
+      "extras_type           str\n",
+      "is_wicket           int64\n",
+      "player_dismissed      str\n",
+      "dismissal_kind        str\n",
+      "fielder               str\n",
+      "dtype: object \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "matches = pd.read_csv(RAW / \"matches.csv\")\n",
+    "deliveries = pd.read_csv(RAW / \"deliveries.csv\", low_memory=False)\n",
+    "\n",
+    "print(\"=== matches.csv ===\")\n",
+    "print(f\"Shape : {matches.shape}\")\n",
+    "print(f\"Columns : {list(matches.columns)}\\n\")\n",
+    "\n",
+    "print(\"=== deliveries ===\")\n",
+    "print(f\"Shape : {deliveries.shape}\")\n",
+    "print(f\"Columns : {list(deliveries.columns)}\\n\")\n",
+    "\n",
+    "print(\"=== matches datatypes ===\")\n",
+    "print(matches.dtypes, \"\\n\")\n",
+    "\n",
+    "print(\"=== deliveries datatypes ===\")\n",
+    "print(deliveries.dtypes, \"\\n\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "277b0d5b-14af-4ed7-b146-db39e4475946",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== nulls in matches ===\n",
+      "city                 51\n",
+      "match_type          113\n",
+      "player_of_match     118\n",
+      "toss_winner         113\n",
+      "toss_decision       113\n",
+      "winner                5\n",
+      "result_margin        19\n",
+      "target_runs         116\n",
+      "target_overs        116\n",
+      "super_over          113\n",
+      "method             1187\n",
+      "umpire1             113\n",
+      "umpire2             113\n",
+      "dtype: int64 \n",
+      "\n",
+      "=== nulls in deliveries ===\n",
+      "extras_type         273146\n",
+      "player_dismissed    272973\n",
+      "dismissal_kind      272973\n",
+      "fielder             276874\n",
+      "dtype: int64 \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"=== nulls in matches ===\")\n",
+    "null_matches = matches.isnull().sum()\n",
+    "print(null_matches[null_matches > 0], \"\\n\")\n",
+    "\n",
+    "print(\"=== nulls in deliveries ===\")\n",
+    "null_deliveries = deliveries.isnull().sum()\n",
+    "print(null_deliveries[null_deliveries > 0], \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "db0f4e9c-e5f0-4f28-b42e-c0c206cdda72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== matches.describe() ===\n",
+      "                  count unique                  top  freq           mean  \\\n",
+      "match_id         1208.0    NaN                  NaN   NaN  953614.823675   \n",
+      "season             1208     19                 2013    76            NaN   \n",
+      "city               1157     37               Mumbai   184            NaN   \n",
+      "date               1208    916           2008-04-19     2            NaN   \n",
+      "match_type         1095      8               League  1029            NaN   \n",
+      "player_of_match    1090    291       AB de Villiers    25            NaN   \n",
+      "venue              1208     59         Eden Gardens    77            NaN   \n",
+      "team1              1208     19  Chennai Super Kings   140            NaN   \n",
+      "team2              1208     19       Mumbai Indians   149            NaN   \n",
+      "toss_winner        1095     19       Mumbai Indians   143            NaN   \n",
+      "toss_decision      1095      2                field   704            NaN   \n",
+      "winner             1203     20       Mumbai Indians   155            NaN   \n",
+      "result             1208      5              wickets   637            NaN   \n",
+      "result_margin    1189.0    NaN                  NaN   NaN      17.369218   \n",
+      "target_runs      1092.0    NaN                  NaN   NaN     165.684066   \n",
+      "target_overs     1092.0    NaN                  NaN   NaN      19.759341   \n",
+      "super_over         1095      2                    N  1081            NaN   \n",
+      "method               21      1                  D/L    21            NaN   \n",
+      "umpire1            1095     62         AK Chaudhary   115            NaN   \n",
+      "umpire2            1095     62               S Ravi    83            NaN   \n",
+      "\n",
+      "                           std       min        25%        50%         75%  \\\n",
+      "match_id         381648.321465  335982.0  548361.75  1082617.5  1304076.25   \n",
+      "season                     NaN       NaN        NaN        NaN         NaN   \n",
+      "city                       NaN       NaN        NaN        NaN         NaN   \n",
+      "date                       NaN       NaN        NaN        NaN         NaN   \n",
+      "match_type                 NaN       NaN        NaN        NaN         NaN   \n",
+      "player_of_match            NaN       NaN        NaN        NaN         NaN   \n",
+      "venue                      NaN       NaN        NaN        NaN         NaN   \n",
+      "team1                      NaN       NaN        NaN        NaN         NaN   \n",
+      "team2                      NaN       NaN        NaN        NaN         NaN   \n",
+      "toss_winner                NaN       NaN        NaN        NaN         NaN   \n",
+      "toss_decision              NaN       NaN        NaN        NaN         NaN   \n",
+      "winner                     NaN       NaN        NaN        NaN         NaN   \n",
+      "result                     NaN       NaN        NaN        NaN         NaN   \n",
+      "result_margin        22.037405       0.0        6.0        8.0        20.0   \n",
+      "target_runs          33.427048      43.0      146.0      166.0       187.0   \n",
+      "target_overs          1.581108       5.0       20.0       20.0        20.0   \n",
+      "super_over                 NaN       NaN        NaN        NaN         NaN   \n",
+      "method                     NaN       NaN        NaN        NaN         NaN   \n",
+      "umpire1                    NaN       NaN        NaN        NaN         NaN   \n",
+      "umpire2                    NaN       NaN        NaN        NaN         NaN   \n",
+      "\n",
+      "                       max  \n",
+      "match_id         1426425.0  \n",
+      "season                 NaN  \n",
+      "city                   NaN  \n",
+      "date                   NaN  \n",
+      "match_type             NaN  \n",
+      "player_of_match        NaN  \n",
+      "venue                  NaN  \n",
+      "team1                  NaN  \n",
+      "team2                  NaN  \n",
+      "toss_winner            NaN  \n",
+      "toss_decision          NaN  \n",
+      "winner                 NaN  \n",
+      "result                 NaN  \n",
+      "result_margin        146.0  \n",
+      "target_runs          288.0  \n",
+      "target_overs          20.0  \n",
+      "super_over             NaN  \n",
+      "method                 NaN  \n",
+      "umpire1                NaN  \n",
+      "umpire2                NaN   \n",
+      "\n",
+      "=== deliveries.describe() ===\n",
+      "                 count           mean            std       min       25%  \\\n",
+      "match_id      287271.0  954701.438732  381397.139578  335982.0  548363.0   \n",
+      "inning        287271.0       1.482764       0.502517       1.0       1.0   \n",
+      "over          287271.0       9.282232       5.686963       0.0       4.0   \n",
+      "ball          287271.0       3.628121       1.818273       1.0       2.0   \n",
+      "batsman_runs  287271.0       1.283199       1.657209       0.0       0.0   \n",
+      "extra_runs    287271.0       0.068214       0.342788       0.0       0.0   \n",
+      "total_runs    287271.0       1.351414       1.643276       0.0       0.0   \n",
+      "is_wicket     287271.0       0.049772       0.217474       0.0       0.0   \n",
+      "\n",
+      "                    50%        75%        max  \n",
+      "match_id      1082621.0  1304077.0  1426425.0  \n",
+      "inning              1.0        2.0        6.0  \n",
+      "over                9.0       14.0       20.0  \n",
+      "ball                4.0        5.0       11.0  \n",
+      "batsman_runs        1.0        1.0        6.0  \n",
+      "extra_runs          0.0        0.0        7.0  \n",
+      "total_runs          1.0        1.0        7.0  \n",
+      "is_wicket           0.0        0.0        1.0   \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"=== matches.describe() ===\")\n",
+    "print(matches.describe(include=\"all\").T, \"\\n\")\n",
+    "\n",
+    "print(\"=== deliveries.describe() ===\")\n",
+    "print(deliveries.describe().T, \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "42030b94-d9a6-42b2-8488-674edd4fb917",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== unique teams in matches ===\n",
+      "['Chennai Super Kings', 'Deccan Chargers', 'Delhi Capitals', 'Delhi Daredevils', 'Gujarat Lions', 'Gujarat Titans', 'Kings XI Punjab', 'Kochi Tuskers Kerala', 'Kolkata Knight Riders', 'Lucknow Super Giants', 'Mumbai Indians', 'Pune Warriors', 'Punjab Kings', 'Rajasthan Royals', 'Rising Pune Supergiant', 'Rising Pune Supergiants', 'Royal Challengers Bangalore', 'Royal Challengers Bengaluru', 'Sunrisers Hyderabad'] \n",
+      "\n",
+      "=== seasons in a dataset ===\n",
+      "['2007/08', '2009', '2009/10', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020/21', '2021', '2022', '2023', '2024', '2025', '2026'] \n",
+      "\n",
+      "=== Venues (top 15 by match count ===\n",
+      "venue\n",
+      "Eden Gardens                                  77\n",
+      "Wankhede Stadium                              73\n",
+      "M Chinnaswamy Stadium                         65\n",
+      "Feroz Shah Kotla                              60\n",
+      "Wankhede Stadium, Mumbai                      56\n",
+      "Rajiv Gandhi International Stadium, Uppal     49\n",
+      "MA Chidambaram Stadium, Chepauk               48\n",
+      "Sawai Mansingh Stadium                        47\n",
+      "Dubai International Cricket Stadium           46\n",
+      "MA Chidambaram Stadium, Chepauk, Chennai      38\n",
+      "Narendra Modi Stadium, Ahmedabad              36\n",
+      "Punjab Cricket Association Stadium, Mohali    35\n",
+      "Sheikh Zayed Stadium                          29\n",
+      "Sharjah Cricket Stadium                       28\n",
+      "Arun Jaitley Stadium, Delhi                   27\n",
+      "Name: count, dtype: int64 \n",
+      "\n",
+      "=== Toss Descision ===\n",
+      "toss_decision\n",
+      "field    704\n",
+      "bat      391\n",
+      "Name: count, dtype: int64 \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"=== unique teams in matches ===\")\n",
+    "all_teams = pd.concat([matches[\"team1\"], matches[\"team2\"]]).unique()\n",
+    "print(sorted(all_teams), \"\\n\")\n",
+    "\n",
+    "print(\"=== seasons in a dataset ===\")\n",
+    "print(sorted(matches[\"season\"].unique()), \"\\n\")\n",
+    "\n",
+    "print(\"=== Venues (top 15 by match count ===\")\n",
+    "print(matches[\"venue\"].value_counts().head(15), \"\\n\")\n",
+    "\n",
+    "print(\"=== Toss Descision ===\")\n",
+    "print(matches[\"toss_decision\"].value_counts(), \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "407af91d-cf0e-4b7a-bc1b-0b29aa72904c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check chart saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "\n",
+    "matches[\"season\"].value_counts().sort_index().plot(\n",
+    "    kind=\"bar\", ax=axes[0], color=\"#7F77DD\"\n",
+    ")\n",
+    "axes[0].set_title(\"Matches per Season\")\n",
+    "axes[0].set_xlabel(\"Season\")\n",
+    "axes[0].set_ylabel(\"Match Count\")\n",
+    "\n",
+    "deliveries.groupby(\"match_id\")[\"total_runs\"].sum().plot(\n",
+    "    kind=\"hist\", bins=30, ax=axes[1], color=\"#1D9E75\"\n",
+    ")\n",
+    "axes[1].set_title(\"Distribution of total runs per match\")\n",
+    "axes[1].set_xlabel(\"total runs\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(r\"E:\\ipl-analytics\\data\\processed\\sanity_check.png\", dpi=120)\n",
+    "plt.show()\n",
+    "print(\"Sanity check chart saved\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5274f225-3cea-43c7-8a1e-f2b2ce7661a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Merged Shappe\n",
+      "(287271, 21)\n",
+      "\n",
+      "=== Merge null check (winner column) ===\n",
+      "490 unmatched rows\n",
+      "\n",
+      "=== Sample merged rows ===\n",
+      "   match_id  inning           batting_team                 bowling_team  over  ball       batter   bowler  non_striker  batsman_runs  extra_runs  total_runs extras_type  is_wicket player_dismissed dismissal_kind fielder   season                        team1                  team2                 winner\n",
+      "0    335982       1  Kolkata Knight Riders  Royal Challengers Bangalore     0     1   SC Ganguly  P Kumar  BB McCullum             0           1           1     legbyes          0              NaN            NaN     NaN  2007/08  Royal Challengers Bangalore  Kolkata Knight Riders  Kolkata Knight Riders\n",
+      "1    335982       1  Kolkata Knight Riders  Royal Challengers Bangalore     0     2  BB McCullum  P Kumar   SC Ganguly             0           0           0         NaN          0              NaN            NaN     NaN  2007/08  Royal Challengers Bangalore  Kolkata Knight Riders  Kolkata Knight Riders\n",
+      "2    335982       1  Kolkata Knight Riders  Royal Challengers Bangalore     0     3  BB McCullum  P Kumar   SC Ganguly             0           1           1       wides          0              NaN            NaN     NaN  2007/08  Royal Challengers Bangalore  Kolkata Knight Riders  Kolkata Knight Riders\n"
+     ]
+    }
+   ],
+   "source": [
+    "merged = deliveries.merge(\n",
+    "    matches[[\"match_id\", \"season\", \"team1\", \"team2\", \"winner\"]],\n",
+    "    left_on=\"match_id\",\n",
+    "    right_on=\"match_id\",\n",
+    "    how=\"left\"\n",
+    ")\n",
+    "\n",
+    "print(\"=== Merged Shappe\")\n",
+    "print(merged.shape)\n",
+    "\n",
+    "print(\"\\n=== Merge null check (winner column) ===\")\n",
+    "print(merged[\"winner\"].isnull().sum(), \"unmatched rows\")\n",
+    "\n",
+    "print(\"\\n=== Sample merged rows ===\")\n",
+    "print(merged.head(3).to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "afe201b0-7494-4a78-9c8c-c0a28174be68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== Anomaly Report ===\n",
+      " duplicate_match_ids                : 0\n",
+      " matches_without_winner             : 5\n",
+      " deliveries_without_batsman         : 0\n",
+      " total_seasons                      : 19\n",
+      " total_teams_ever                   : 19\n",
+      " total_matches                      : 1208\n",
+      " total_deliveries                   : 287271\n"
+     ]
+    }
+   ],
+   "source": [
+    "anomalies = {\n",
+    "    \"duplicate_match_ids\": int(matches[\"match_id\"].duplicated().sum()),\n",
+    "    \"matches_without_winner\": int(matches[\"winner\"].isnull().sum()),\n",
+    "    \"deliveries_without_batsman\": int(deliveries[\"batter\"].isnull().sum()),\n",
+    "    \"total_seasons\": int(matches[\"season\"].nunique()),\n",
+    "    \"total_teams_ever\": len(all_teams),\n",
+    "    \"total_matches\": len(matches),\n",
+    "    \"total_deliveries\": len(deliveries),\n",
+    "}\n",
+    "\n",
+    "print(\"\\n=== Anomaly Report ===\")\n",
+    "for k, v in anomalies.items():\n",
+    "    print(f\" {k:<35}: {v}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40da6ce3-2a15-42b9-82e4-7fecc8d6168b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/02_cleaning_and_features.ipynb

@@ -0,0 +1,503 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "812251b0-5245-4688-8c4d-c7371abe6af6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "from src.clean import clean_matches, clean_deliveries\n",
+    "from src.features import (\n",
+    "    build_batting_features,\n",
+    "    build_bowling_features,\n",
+    "    build_match_summary,\n",
+    "    build_team_season_stats,\n",
+    ")\n",
+    "RAW = Path(r\"E:\\ipl-analytics\\data\\raw\")\n",
+    "PROCESSED = Path(r\"E:\\ipl-analytics\\data\\processed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9c4a93ae-ed2a-4d10-92b2-77b736e4617a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RAW matches: (1209, 20)\n",
+      "RAW deliveries: (1209, 20)\n",
+      "\n",
+      "Raw season (before cleaning):\n",
+      "['2007/08', '2009', '2009/10', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020/21', '2021', '2022', '2023', '2024', '2025', '2026']\n"
+     ]
+    }
+   ],
+   "source": [
+    "matches_raw = pd.read_csv(RAW / \"matches.csv\")\n",
+    "deliveries_raw = pd.read_csv(RAW / \"deliveries.csv\", low_memory=False)\n",
+    "\n",
+    "print(f\"RAW matches: {matches_raw.shape}\")\n",
+    "print(f\"RAW deliveries: {matches_raw.shape}\")\n",
+    "print(f\"\\nRaw season (before cleaning):\")\n",
+    "print(sorted(matches_raw[\"season\"].astype(str).unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "13eb99e8-d89d-4e10-9894-8354fbb0fe73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OK — all seasons are valid IPL years.\n",
+      "OK — all expected IPL seasons are present.\n",
+      "\n",
+      "Season distribution : [np.int32(2008), np.int32(2009), np.int32(2010), np.int32(2011), np.int32(2012), np.int32(2013), np.int32(2014), np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024), np.int32(2025), np.int32(2026)]\n",
+      "Season range        : 2008 – 2026\n",
+      "Total seasons       : 19\n",
+      "Total matches       : 1209\n",
+      "\n",
+      "clean_matches done: (1209, 20) → (1209, 24)\n",
+      "Remaining nulls:\n",
+      "city              51\n",
+      "match_type       114\n",
+      "target_overs     117\n",
+      "super_over       114\n",
+      "method          1188\n",
+      "umpire1          114\n",
+      "umpire2          114\n",
+      "\n",
+      "clean_deliveries: (287513, 17) → (287513, 24)\n",
+      "  Nulls remaining:\n",
+      "extras_type    273388\n",
+      "fielder        277108\n"
+     ]
+    }
+   ],
+   "source": [
+    "matches = clean_matches(matches_raw)\n",
+    "deliveries = clean_deliveries(deliveries_raw)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aa7b3c1d-e6d1-42c0-b0e4-9679559bab1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All assertions passed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "assert pd.api.types.is_datetime64_any_dtype(matches[\"date\"]), \\\n",
+    "   \"date column should be datetime64\"\n",
+    "\n",
+    "matches[\"season\"] = pd.to_numeric(matches[\"season\"], errors='coerce').astype('int64')\n",
+    "\n",
+    "assert matches[\"season\"].dtype == int or matches[\"season\"].dtype == \"int64\", \\\n",
+    "    \"season should be int\"\n",
+    "\n",
+    "assert matches[\"season\"].min() == 2008, \\\n",
+    "    f\"Earliest season should be 2008, got {matches['season'].min()}\"\n",
+    "\n",
+    "assert 2007 not in matches[\"season\"].values, \\\n",
+    "    \"2007 should not exist - IPL started in 2008\"\n",
+    "\n",
+    "assert deliveries[\"is_wicket\"].sum() > 0, \\\n",
+    "    \"is_ wicket column looks wrong - no wickets found\"\n",
+    "\n",
+    "assert deliveries[\"is_four\"].sum() > 0, \\\n",
+    "    \"is_four columns wrong - no fours found\"\n",
+    "\n",
+    "assert (\n",
+    "    deliveries[\"match_id\"].isin(matches[\"match_id\"]).all()\n",
+    "), \"some delivery match_ids have no corresponding match row\"   \n",
+    "\n",
+    "print(\"All assertions passed.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3fdd4af4-e1fc-490f-9a89-e0111f89d692",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Raw season values that might be 2020:\n",
+      "['2007/08', '2009', '2009/10', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020/21', '2021', '2022', '2023', '2024', '2025', '2026']\n",
+      "\n",
+      "Matches with dates in 2020: 60\n",
+      "Their season values: <StringArray>\n",
+      "['2020/21']\n",
+      "Length: 1, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Raw season values that might be 2020:\")\n",
+    "raw_seasons = matches_raw[\"season\"].astype(str).unique()\n",
+    "candidates  = [s for s in raw_seasons if \"20\" in s]\n",
+    "print(sorted(candidates))\n",
+    "\n",
+    "if \"date\" in matches_raw.columns:\n",
+    "    matches_raw[\"_date\"] = pd.to_datetime(matches_raw[\"date\"], errors=\"coerce\")\n",
+    "    year_2020 = matches_raw[matches_raw[\"_date\"].dt.year == 2020]\n",
+    "    print(f\"\\nMatches with dates in 2020: {len(year_2020)}\")\n",
+    "    if len(year_2020) > 0:\n",
+    "        print(f\"Their season values: {year_2020['season'].unique()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3053c77c-a9a4-42ce-8614-c1569c9da973",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "build_batting_features: 460 batters with 30+ balls faced\n",
+      "build_bowling_features: 402 bowlers with 60+ balls bowled\n",
+      "build_match_summary: (1209, 45)\n",
+      "build_team_season_stats: (166, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "batting_stats = build_batting_features(deliveries)\n",
+    "bowling_stats = build_bowling_features(deliveries)\n",
+    "match_summary = build_match_summary(matches, deliveries)\n",
+    "team_season_stats = build_team_season_stats(match_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "be5a9fd0-9446-4c7b-94d8-30e0e6f3d8f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "All files saved to data/processed/\n"
+     ]
+    }
+   ],
+   "source": [
+    "matches.to_csv(PROCESSED / \"matches_clean.csv\", index=False)\n",
+    "deliveries.to_csv(PROCESSED / \"deliveries_clean.csv\", index=False)\n",
+    "batting_stats.to_csv(PROCESSED / \"batting_features.csv\", index=False)\n",
+    "bowling_stats.to_csv(PROCESSED / \"bowling_features.csv\", index=False)\n",
+    "match_summary.to_csv(PROCESSED / \"match_summary.csv\", index=False)\n",
+    "team_season_stats.to_csv(PROCESSED / \"team_season_stats.csv\", index=False)\n",
+    "\n",
+    "print(\"\\nAll files saved to data/processed/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ec238a06-b38a-41f3-a11e-efeedb404d62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SQLite DB saved: data/processed/ipl.db\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sqlite3\n",
+    "\n",
+    "conn = sqlite3.connect(PROCESSED / \"ipl.db\")\n",
+    "matches.to_sql(\"matches\", conn, if_exists=\"replace\", index=False)\n",
+    "deliveries.to_sql(\"deliveries\", conn, if_exists=\"replace\", index=False)\n",
+    "batting_stats.to_sql(\"batting\", conn, if_exists=\"replace\", index=False)\n",
+    "bowling_stats.to_sql(\"bowling\", conn, if_exists=\"replace\", index=False)\n",
+    "match_summary.to_sql(\"match_summary\", conn, if_exists=\"replace\", index=False)\n",
+    "team_season_stats.to_sql(\"team_season\", conn, if_exists=\"replace\", index=False)\n",
+    "conn.close()\n",
+    "\n",
+    "print(\"SQLite DB saved: data/processed/ipl.db\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d22080d1-858d-4b86-a9f5-7e30fa4d9409",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== OUTPUT SUMMARY ===\n",
+      " matches_clean         : shape=(1209, 24)       nulls=1812\n",
+      " deliveries_clean      : shape=(287513, 24)     nulls=550496\n",
+      " batting_features      : shape=(460, 19)        nulls=164\n",
+      " bowling_features      : shape=(402, 20)        nulls=37\n",
+      " match_summary         : shape=(1209, 45)       nulls=1866\n",
+      " team_season_stats     : shape=(166, 7)         nulls=0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n=== OUTPUT SUMMARY ===\")\n",
+    "for name, df in {\n",
+    "    \"matches_clean\" :  matches,\n",
+    "    \"deliveries_clean\": deliveries,\n",
+    "    \"batting_features\": batting_stats,\n",
+    "    \"bowling_features\": bowling_stats,\n",
+    "    \"match_summary\": match_summary,\n",
+    "    \"team_season_stats\": team_season_stats,\n",
+    "}.items():\n",
+    "    nulls = df.isnull().sum().sum()\n",
+    "    print(f\" {name:<22}: shape={str(df.shape):<16} nulls={nulls}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "48df6a69-39d5-4cc2-b329-0b988fb458dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== Top 10 run scorers ===\n",
+      "        batter  total_runs  batting_average  strike_rate  hundreds  fifties\n",
+      "       V Kohli        9022            38.07       133.80         8       66\n",
+      "     RG Sharma        7185            28.86       132.64         2       48\n",
+      "      S Dhawan        6769            35.07       127.62         2       51\n",
+      "     DA Warner        6567            40.04       140.26         4       62\n",
+      "      KL Rahul        5593            45.47       138.17         6       42\n",
+      "      SK Raina        5536            33.15       137.54         1       39\n",
+      "      MS Dhoni        5439            34.42       137.91         0       24\n",
+      "     AM Rahane        5194            29.85       124.92         2       34\n",
+      "AB de Villiers        5181            41.45       152.38         3       40\n",
+      "     SV Samson        5008            31.90       140.83         5       26\n",
+      "\n",
+      "=== Top 10 wicket takers ===\n",
+      "    bowler  wickets  economy_rate  bowling_average  dot_ball_pct\n",
+      " YS Chahal      237          8.11            22.59         34.59\n",
+      "   B Kumar      228          7.84            25.35         43.80\n",
+      " SP Narine      221          6.92            23.59         39.68\n",
+      "  DJ Bravo      207          8.53            21.43         33.78\n",
+      " JJ Bumrah      206          7.43            21.23         42.51\n",
+      "  R Ashwin      205          7.28            27.91         35.35\n",
+      " PP Chawla      201          8.07            25.77         35.27\n",
+      "SL Malinga      188          7.40            18.54         43.07\n",
+      " RA Jadeja      185          7.74            29.11         33.06\n",
+      "  A Mishra      183          7.46            22.91         36.19\n",
+      "\n",
+      "=== New columns in deliveries ===\n",
+      " is_wicket  is_four  is_six  is_dot_ball over_phase  is_legal_delivery\n",
+      "         0        0       0            1  powerplay                  1\n",
+      "         0        0       0            1  powerplay                  1\n",
+      "         0        0       0            0  powerplay                  0\n",
+      "         0        0       0            1  powerplay                  1\n",
+      "         0        0       0            1  powerplay                  1\n",
+      "         0        0       0            1  powerplay                  1\n",
+      "         0        0       0            1  powerplay                  1\n",
+      "         0        0       0            1  powerplay                  1\n",
+      "         0        1       0            0  powerplay                  1\n",
+      "         0        1       0            0  powerplay                  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n=== Top 10 run scorers ===\")\n",
+    "print(batting_stats[[\"batter\", \"total_runs\", \"batting_average\", \"strike_rate\", \"hundreds\", \"fifties\"]].head(10).to_string(index=False))\n",
+    "\n",
+    "print(\"\\n=== Top 10 wicket takers ===\")\n",
+    "print(bowling_stats[[\"bowler\", \"wickets\", \"economy_rate\", \"bowling_average\", \"dot_ball_pct\"]].head(10).to_string(index=False))\n",
+    "\n",
+    "print(\"\\n=== New columns in deliveries ===\")\n",
+    "new_cols = [\"is_wicket\", \"is_four\", \"is_six\", \"is_dot_ball\", \"over_phase\", \"is_legal_delivery\"]\n",
+    "print(deliveries[new_cols].head(10).to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67d96b8e-4306-4fb3-b00d-859b4ca6efdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "['match_id', 'season', 'city', 'date', 'match_type', 'player_of_match', 'venue', 'team1', 'team2', 'toss_winner', 'toss_decision', 'winner', 'result', 'result_margin', 'target_runs', 'target_overs', 'super_over', 'method', 'umpire1', 'umpire2', 'result_type', 'is_no_result', 'day_of_week', 'month', 'inn1_batting_team', 'inn1_total_runs', 'inn1_total_wickets', 'inn1_total_balls', 'inn1_total_fours', 'inn1_total_sixes', 'inn1_dot_balls', 'inn1_run_rate', 'inn2_batting_team', 'inn2_total_runs', 'inn2_total_wickets', 'inn2_total_balls', 'inn2_total_fours', 'inn2_total_sixes', 'inn2_dot_balls', 'inn2_run_rate', 'toss_winner_won', 'batting_first_team', 'batting_first_won', 'score_diff', 'season_num']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quick check — run this in a notebook\n",
+    "import pandas as pd\n",
+    "summary = pd.read_csv(r\"E:/ipl-analytics/data/processed/match_summary.csv\")\n",
+    "print(\"batting_first_won\" in summary.columns)\n",
+    "print(summary.columns.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b9a549cc-5eaf-46e3-a89a-08def3234b43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['match_id', 'season', 'city', 'date', 'match_type']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# In callbacks.py, check these lines at the top\n",
+    "matches    = pd.read_csv(PROCESSED / \"matches_clean.csv\",  parse_dates=[\"date\"])\n",
+    "print(matches.columns.tolist()[:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9edf3ceb-9a25-423a-9461-175553fc6431",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['A_form5', 'B_form5', 'form_diff5', 'A_form10', 'B_form10', 'form_diff10', 'A_overall_wr', 'B_overall_wr', 'overall_wr_diff', 'A_venue_wr', 'B_venue_wr', 'venue_wr_diff', 'venue_bat_first_wr', 'A_avg_score', 'B_avg_score', 'score_diff', 'A_avg_wickets', 'B_avg_wickets', 'wicket_diff', 'h2h_wr_A', 'h2h_n', 'toss_decision_bat', 'toss_correct', 'A_consistency', 'B_consistency', 'season_stage']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "with open(r\"E:/ipl-analytics/data/processed/models/win_prob_features.json\") as f:\n",
+    "    print(json.load(f))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "aa04d396-d78c-478b-8269-c10d2f2a508e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "(1209, 45)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(r\"E:/ipl-analytics/data/processed/match_summary.csv\")\n",
+    "print(\"batting_first_won\" in df.columns)\n",
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "74439927-12bc-4a07-bc8d-177e3e1f06be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\BHUMI\\AppData\\Local\\Temp\\ipykernel_25556\\3219913361.py:3: DtypeWarning: Columns (0: extras_type) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  deliveries = pd.read_csv(r\"E:/ipl-analytics/data/processed/deliveries_clean.csv\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "batting_team\n",
+      "Mumbai Indians                 34133\n",
+      "Royal Challengers Bangalore    32592\n",
+      "Delhi Capitals                 32365\n",
+      "Kolkata Knight Riders          31779\n",
+      "Chennai Super Kings            31414\n",
+      "Name: count, dtype: int64\n",
+      "['match_id', 'inning', 'batting_team', 'bowling_team', 'over', 'ball', 'batter', 'bowler', 'non_striker', 'batsman_runs', 'extra_runs', 'total_runs', 'extras_type', 'is_wicket', 'player_dismissed', 'dismissal_kind', 'fielder', 'is_wide', 'is_noball', 'is_four', 'is_six', 'is_dot_ball', 'over_phase', 'is_legal_delivery']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# In your notebook or terminal\n",
+    "import pandas as pd\n",
+    "deliveries = pd.read_csv(r\"E:/ipl-analytics/data/processed/deliveries_clean.csv\")\n",
+    "\n",
+    "# Check what batting_team values look like\n",
+    "print(deliveries[\"batting_team\"].value_counts().head(5))\n",
+    "print(deliveries.columns.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66c87d74-17eb-4828-b5b6-ac0a2b18b3c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

render.yaml

@@ -0,0 +1,6 @@
+services:
+  - type: web
+    name: ipl-analytics
+    runtime: python
+    buildCommand: pip install -r requirements.txt
+    startCommand: python waitress_server.py

requirements.txt

@@ -0,0 +1,14 @@
+pandas
+numpy
+matplotlib
+seaborn
+plotly
+dash
+dash-bootstrap-components
+scikit-learn
+xgboost
+statsmodels
+shap
+jupyter
+ipykernel
+waitress

src/__init__.py

@@ -0,0 +1 @@
+from .models import get_available_teams, get_available_venues

src/clean.py

@@ -0,0 +1,187 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+
+RAW = Path("E:/ipl-analytics/data/raw")
+PROCESSED = Path("E:/ipl-analytics/data/processed")
+PROCESSED.mkdir(parents=True, exist_ok=True)
+
+TEAM_NAME_MAP = {
+    "Rising Pune Supergiant" : "Rising Pune Supergiants",
+    "King XI Punjab" : "Punjab Kings",
+    "Pune Warriors" : "Pune Warriors India",
+    "Deccan Chargrers" : "Sunrisers Hyderabad",
+    "Delhi Daredevils" : "Delhi Capitals",
+    "Royal Challengers Bengaluru" : "Royal Challengers Bangalore",
+    
+}
+
+VENUE_KEYWORDS = {
+    "Wankhede": "Wankhede Stadium",
+    "Chinnaswamy": "M Chinnaswamy Stadium",
+    "Chidambaram": "MA Chidambaram Stadium",
+    "Chepauk": "MA Chidambaram Stadium",
+    "Eden Gardens": "Eden Gardens",
+    "Arun Jaitley": "Feroz Shah Kotla",
+    "Feroz Shah Kotla": "Feroz Shah Kotla",
+    "Rajasekhara": "Vizag Stadium",
+    "Yadavindra": "Mullanpur Stadium",
+    "Ekana": "Ekana Stadium",
+    "Rajiv Gandhi": "Uppal Stadium",
+    "IS Bindra": "PCA Stadium",
+    "Mohali": "PCA Stadium",
+    "Narendra Modi": "Motera Stadium",
+    "Dy Patil": "DY Patil Stadium",
+    "Brabourne": "Brabourne Stadium",
+}
+
+IPL_VALID_SEASONS = set(range(2008, 2025))
+
+import datetime
+IPL_VALID_SEASONS = set(range(2008, datetime.date.today().year + 1))
+
+SLASH_SEASON_MAP = {
+    "2007/08" : 2008,
+    "2009/10" : 2010,
+    "2020/21" : 2020,
+}
+
+def clean_matches(matches: pd.DataFrame) -> pd.DataFrame:
+    df = matches.copy()
+
+    if "id" in df.columns:
+        df = df.rename(columns={"id": "match_id"})
+
+    df["date"] = pd.to_datetime(df["date"], errors="coerce")
+
+    def fix_season(val):
+        val = str(val).strip()
+        
+        if val in SLASH_SEASON_MAP:
+            return SLASH_SEASON_MAP[val]
+
+        if "/" in val:
+            parts  = val.split("/")
+            suffix = parts[1].strip()
+            prefix = parts[0].strip()[:2]
+            return int(prefix + suffix)
+        
+        val_clean = val.replace("IPL", "").replace("Season", "").strip()
+        year = int(float(val_clean))
+
+        if year == 2007:
+            return 2008
+        return year
+
+    df["season"] = df["season"].apply(fix_season).astype("int32")
+
+    detected  = set(df["season"].unique())
+    unexpected = detected - IPL_VALID_SEASONS
+    missing    = IPL_VALID_SEASONS - detected
+
+    if unexpected:
+        print(f"WARNING — unexpected seasons found    : {sorted(unexpected)}")
+        print(f"  Dropping {len(df[df['season'].isin(unexpected)])} rows.")
+        df = df[df["season"].isin(IPL_VALID_SEASONS)].copy()
+    else:
+        print("OK — all seasons are valid IPL years.")
+
+    if missing:
+        print(f"INFO — seasons not in dataset : {sorted(missing)}")
+        print(f"  Expected if your CSV doesn't cover those years yet.")
+    else:
+        print("OK — all expected IPL seasons are present.")
+
+    print(f"\nSeason distribution : {sorted(df['season'].unique())}")
+    print(f"Season range        : {df['season'].min()} – {df['season'].max()}")
+    print(f"Total seasons       : {df['season'].nunique()}")
+    print(f"Total matches       : {len(df)}")
+
+    df["venue"] = df["venue"].astype(str).str.strip()
+    for keyword, clean_name in VENUE_KEYWORDS.items():
+        mask = df["venue"].str.contains(keyword, case=False, na=False)
+        df.loc[mask, "venue"] = clean_name
+
+    for col in ["result_margin", "dl_applied", "target_runs"]:
+        if col in df.columns:
+            df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0).astype(int)
+
+    for col in ["team1", "team2", "winner", "toss_winner"]:
+        if col in df.columns:
+            df[col] = df[col].replace(TEAM_NAME_MAP)
+
+    df["player_of_match"] = df["player_of_match"].fillna("N/A")
+
+    def result_type(row):
+        r = str(row.get("result", "")).lower().strip()
+        if r == "runs":     return "runs"
+        if r == "wickets":  return "wickets"
+        return "no_result"
+
+    df["result_type"] = df.apply(result_type, axis=1)
+
+    df["is_no_result"] = df["winner"].isna().astype(int)
+    df["winner"]       = df["winner"].fillna("No Result")
+
+    df["day_of_week"] = df["date"].dt.day_name()
+    df["month"]       = df["date"].dt.month
+
+    df = df.sort_values(["season", "date"]).reset_index(drop=True)
+
+    print(f"\nclean_matches done: {matches.shape} → {df.shape}")
+    remaining_nulls = df.isnull().sum()
+    remaining_nulls = remaining_nulls[remaining_nulls > 0]
+    if len(remaining_nulls):
+        print(f"Remaining nulls:\n{remaining_nulls.to_string()}")
+    else:
+        print("No nulls remaining.")
+
+    return df
+
+def clean_deliveries(deliveries: pd.DataFrame) -> pd.DataFrame:
+    df = deliveries.copy()
+    
+    df = df.rename(columns={"batter":"batsman"})
+    
+    for col in ["batting_team", "bowling_team"]:
+        if col in df.columns:
+            df[col] = df[col].replace(TEAM_NAME_MAP)
+    
+    num_cols = ["inning", "over", "ball", "batsman_runs", "extra_runs", "total_runs"]
+    for col in num_cols:
+        if col in df.columns:
+            df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0).astype(int)
+            
+    df["is_wide"] = (df["extras_type"] == "wides").astype(int)
+    df["is_noball"] = (df["extras_type"] == "noballs").astype(int)
+
+    for col in ["batting_team", "bowling_team"]:
+        df[col] = df[col].replace(TEAM_NAME_MAP)
+            
+    df["player_dismissed"] = df["player_dismissed"].fillna("not_out")
+    df["dismissal_kind"] = df["dismissal_kind"].fillna("not_out")
+    
+    df["is_wicket"] = df["is_wicket"].fillna(0).astype(int)
+    
+    df["is_four"] = (df["batsman_runs"] == 4).astype(int)
+    df["is_six"] = (df["batsman_runs"] == 6).astype(int)
+    
+    df["is_dot_ball"] = (
+        (df["batsman_runs"] == 0) & 
+        (df["is_wide"] == 0) & 
+        (df["is_noball"] == 0)
+    ).astype(int)
+    
+    def over_phase(over):
+        if over <= 6: return "powerplay"
+        if over <= 15: return "middle"
+        return "death"
+    
+    df["over_phase"] = df["over"].apply(over_phase)
+    
+    df["is_legal_delivery"] = ((df["is_wide"] == 0) & (df["is_noball"] == 0)).astype(int)
+    
+    df = df.sort_values(["match_id", "inning", "over", "ball"]).reset_index(drop=True)
+    print(f"\nclean_deliveries: {deliveries.shape} → {df.shape}")
+    print(f"  Nulls remaining:\n{df.isnull().sum()[df.isnull().sum()>0].to_string()}")
+    return df

src/diagnose_cricsheet.py

@@ -0,0 +1,24 @@
+# src/diagnose_cricsheet.py
+from pathlib import Path
+import pandas as pd
+
+cricsheet_dir = Path("E:/ipl-analytics/data/raw/cricsheet_ipl")
+
+match_files   = sorted([f for f in cricsheet_dir.glob("*.csv")
+                        if "_info" not in f.stem])
+
+print(f"Match files : {len(match_files)}")
+
+print("\nScanning all match files for seasons (30 seconds)...")
+season_counts = {}
+for fp in match_files:
+    try:
+        row = pd.read_csv(fp, usecols=["season"], nrows=1)
+        s   = str(row["season"].iloc[0]).strip()
+        season_counts[s] = season_counts.get(s, 0) + 1
+    except Exception:
+        continue
+
+print(f"\nAll unique season labels in Cricsheet:")
+for s in sorted(season_counts):
+    print(f"  '{s}' : {season_counts[s]} matches")

src/features.py

@@ -0,0 +1,159 @@
+import pandas as pd
+import numpy as np
+
+def build_batting_features(deliveries: pd.DataFrame) -> pd.DataFrame:
+    
+    d = deliveries[deliveries["is_wide"] == 0].copy()
+    
+    agg = d.groupby("batter").agg(
+        total_runs = ("batsman_runs", "sum"),
+        balls_faced = ("is_legal_delivery", "sum"),
+        matches_batted = ("match_id", "nunique"),
+        innings = ("inning", "count"),
+        fours = ("is_four", "sum"),
+        sixes = ("is_six", "sum"),
+        dot_balls = ("is_dot_ball", "sum"),
+        dismissals = ("is_wicket", "sum"),
+    ).reset_index()
+    
+    agg["strike_rate"] = (agg["total_runs"] / agg["balls_faced"].replace(0, np.nan) * 100).round(2)
+    agg["batting_average"] = (agg["total_runs"] / agg["dismissals"].replace(0, np.nan)).round(2)
+    agg["boundary_rate"] = ((agg["fours"] + agg["sixes"]) / agg["balls_faced"].replace(0, np.nan) * 100).round(2)
+    agg["dot_ball_pct"] = (agg["dot_balls"] / agg["balls_faced"].replace(0, np.nan) * 100).round(2)
+    agg["runs_per_match"] = (agg["total_runs"] / agg["matches_batted"].replace(0, np.nan)).round(2)
+    
+    for phase in ["powerplay", "middle", "death"]:
+        phase_df = d[d["over_phase"] == phase].groupby("batter").agg(
+            _runs = ("batsman_runs", "sum"),
+            _balls = ("is_legal_delivery", "sum"),
+        ).reset_index()
+        phase_df[f"sr_{phase}"] = (phase_df["_runs"] / phase_df["_balls"].replace(0, np.nan) * 100).round(2)
+        agg = agg.merge(phase_df[["batter", f"sr_{phase}"]], on="batter", how="left")
+        
+    inning_scores = d.groupby(["batter", "match_id", "inning"])["batsman_runs"].sum().reset_index()
+    inning_scores.columns = ["batter", "match_id", "inning", "innings_runs"]
+    
+    fifties = inning_scores[inning_scores["innings_runs"].between(50, 99)].groupby("batter").size().rename("fifties")
+    hundreds = inning_scores[inning_scores["innings_runs"] >= 100].groupby("batter").size().rename("hundreds")
+    
+    agg = agg.merge(fifties, on="batter", how="left")
+    agg = agg.merge(hundreds, on="batter", how="left")
+    agg["fifties"] = agg["fifties"].fillna(0).astype(int)
+    agg["hundreds"] = agg["hundreds"].fillna(0).astype(int)
+    
+    agg = agg[agg["balls_faced"] >= 30].copy()
+    
+    print(f"build_batting_features: {len(agg)} batters with 30+ balls faced")
+    return agg.sort_values("total_runs", ascending=False).reset_index(drop=True)
+
+
+def build_bowling_features(deliveries: pd.DataFrame) -> pd.DataFrame:
+    d = deliveries.copy()
+    
+    agg = d.groupby("bowler").agg(
+        balls_bowled = ("is_legal_delivery", "sum"),
+        runs_conceded = ("total_runs", "sum"),
+        wickets = ("is_wicket", "sum"),
+        dot_balls = ("is_dot_ball", "sum"),
+        wides = ("is_wide", "sum"),
+        no_balls = ("is_noball", "sum"),
+        matches_bowled = ("match_id", "nunique"),
+        fours_conceded = ("is_four", "sum"),
+        sixes_conceded = ("is_six", "sum"),
+    ).reset_index()
+    
+    overs = agg["balls_bowled"] / 6
+    agg["economy_rate"] = (agg["runs_conceded"] / overs.replace(0, np.nan)).round(2)
+    agg["bowling_average"] = (agg["runs_conceded"] / agg["wickets"].replace(0, np.nan)).round(2)
+    agg["bowling_sr"] = (agg["balls_bowled"] / agg["wickets"].replace(0, np.nan)).round(2)
+    agg["dot_ball_pct"] = (agg["dot_balls"] / agg["balls_bowled"].replace(0, np.nan) * 100).round(2)
+    agg["wickets_per_match"] = (agg["wickets"] / agg["matches_bowled"].replace(0, np.nan)).round(2)
+    
+    for phase in  ["powerplay", "middle", 'death']:
+        phase_df = d[d["over_phase"] == phase].groupby("bowler").agg(
+            _runs = ("total_runs", "sum"),
+            _balls = ("is_legal_delivery", "sum"),
+        ).reset_index()
+        phase_df[f"economy_{phase}"] = (phase_df["_runs"] / (phase_df["_balls"] / 6).replace(0, np.nan)).round(2)
+        agg = agg.merge(phase_df[["bowler", f"economy_{phase}"]], on="bowler", how="left")
+        
+    inning_wkt = d.groupby(["bowler", "match_id", "inning"])["is_wicket"].sum().reset_index()
+    inning_wkt.columns = ["bowler", "match_id", "inning", "wickets_in_inning"]
+        
+    three_wkt = inning_wkt[inning_wkt["wickets_in_inning"] >= 3].groupby("bowler").size().rename("three_wicket_haul")
+    five_wkt = inning_wkt[inning_wkt["wickets_in_inning"] >= 5].groupby("bowler").size().rename("five_wicket_haul")
+        
+    agg = agg.merge(three_wkt, on="bowler", how="left")
+    agg = agg.merge(five_wkt, on="bowler", how="left")
+    agg["three_wicket_haul"] = agg["three_wicket_haul"].fillna(0).astype(int)
+    agg["five_wicket_haul"] = agg["five_wicket_haul"].fillna(0).astype(int)
+        
+    agg = agg[agg["balls_bowled"] >= 60].copy()
+        
+    print(f"build_bowling_features: {len(agg)} bowlers with 60+ balls bowled")
+    return agg.sort_values("wickets", ascending=False).reset_index(drop=True)
+    
+def build_match_summary(matches: pd.DataFrame, deliveries: pd.DataFrame) -> pd.DataFrame:
+    
+    innings_total = deliveries.groupby(["match_id", "inning", "batting_team"]).agg(
+        total_runs = ("total_runs", "sum"),
+        total_wickets = ("is_wicket", "sum"),
+        total_balls = ("is_legal_delivery", "sum"),
+        total_fours = ("is_four", "sum"),
+        total_sixes = ("is_six", "sum"),
+        dot_balls = ("is_dot_ball", "sum"),
+    ).reset_index()
+    
+    innings_total["run_rate"] = (
+        innings_total["total_runs"] / (innings_total["total_balls"] / 6).replace(0, np.nan)
+    ).round(2)
+    
+    inn1 = innings_total[innings_total["inning"] == 1].add_prefix("inn1_").rename(columns={"inn1_match_id": "match_id"})
+    inn2 = innings_total[innings_total["inning"] == 2].add_prefix("inn2_").rename(columns={"inn2_match_id": "match_id"})
+    
+    summary = matches.merge(inn1.drop(columns=["inn1_inning"]), on="match_id", how="left")
+    summary = summary.merge(inn2.drop(columns=["inn2_inning"]), on="match_id", how="left")
+    
+    summary["toss_winner_won"] = (summary["toss_winner"] == summary["winner"]).astype(int)
+    summary["batting_first_team"] = summary["inn1_batting_team"]
+    
+    summary["batting_first_won"] = (
+        summary["inn1_batting_team"] == summary["winner"]
+    ).astype(int)
+    
+    summary["score_diff"] = summary["inn1_total_runs"] - summary["inn2_total_runs"]
+    
+    min_season = summary["season"].min()
+    summary["season_num"] = summary["season"] - min_season + 1
+    
+    print(f"build_match_summary: {summary.shape}")
+    return summary.reset_index(drop=True)
+
+def build_team_season_stats(matches: pd.DataFrame) -> pd.DataFrame:
+    
+    records = []
+    for season in sorted(matches["season"].unique()):
+        season_df = matches[matches["season"] == season]
+        all_teams = pd.concat([season_df["team1"], season_df["team2"]]).unique()
+        
+        for team in all_teams:
+            played = season_df[(season_df["team1"] == team) | (season_df["team2"] == team)]
+            won = season_df[season_df["winner"] == team]
+            
+            bat_first = played[played["inn1_batting_team"] == team] if "inn1_batting_team" in played.columns else pd.DataFrame()
+            toss_won = played[played["toss_winner"] == team]
+            toss_bat = toss_won[toss_won["toss_decision"] == "bat"]
+            
+            records.append({
+                "season": season,
+                "team": team,
+                "matches_played": len(played),
+                "matches_won": len(won),
+                "win_pct": round(len(won) / len(played) * 100, 1) if len(played) > 0 else 0,
+                "toss_wins": len(toss_won),
+                "toss_bat_choice": len(toss_bat),
+            })
+            
+    df = pd.DataFrame(records)
+    print(f"build_team_season_stats: {df.shape}")
+    return df

src/fix_raw_ids.py

@@ -0,0 +1,99 @@
+import pandas as pd
+from pathlib import Path
+
+RAW = Path("E:/ipl-analytics/data/raw")
+
+matches    = pd.read_csv(RAW / "matches.csv")
+deliveries = pd.read_csv(RAW / "deliveries.csv")
+
+print("=== RAW matches.csv ===")
+print(f"  shape   : {matches.shape}")
+print(f"  columns : {matches.columns.tolist()}")
+
+print("\n=== RAW deliveries.csv ===")
+print(f"  shape   : {deliveries.shape}")
+print(f"  columns : {deliveries.columns.tolist()}")
+
+match_id_col    = "match_id" if "match_id" in matches.columns    else "id"
+delivery_id_col = "match_id" if "match_id" in deliveries.columns else "id"
+
+print(f"\nMatches ID col    : '{match_id_col}'")
+print(f"Deliveries ID col : '{delivery_id_col}'")
+
+print(f"\nMatches   {match_id_col} nulls : {matches[match_id_col].isna().sum()}")
+print(f"Deliveries {delivery_id_col} nulls : {deliveries[delivery_id_col].isna().sum()}")
+
+null_mask = matches[match_id_col].isna()
+print(f"\nRows with null match ID : {null_mask.sum()}")
+
+if null_mask.sum() > 0:
+    last_valid_id = int(matches[match_id_col].dropna().max())
+    new_ids = range(last_valid_id + 1,
+                    last_valid_id + 1 + null_mask.sum())
+    matches.loc[null_mask, match_id_col] = list(new_ids)
+    matches[match_id_col] = matches[match_id_col].astype(int)
+    print(f"Assigned new IDs : {last_valid_id + 1} → {last_valid_id + null_mask.sum()}")
+else:
+    print("No null match IDs — matches.csv is fine.")
+
+if match_id_col == "id":
+    matches = matches.rename(columns={"id": "match_id"})
+    print("Renamed 'id' → 'match_id' in matches.csv")
+    match_id_col = "match_id"
+
+if delivery_id_col == "id":
+    deliveries = deliveries.rename(columns={"id": "match_id"})
+    print("Renamed 'id' → 'match_id' in deliveries.csv")
+    delivery_id_col = "match_id"
+
+valid_ids = set(matches["match_id"].unique())
+delivery_ids = set(deliveries["match_id"].unique())
+orphans = delivery_ids - valid_ids
+
+print(f"\nOrphaned delivery match_ids : {len(orphans)}")
+
+if orphans:
+    print(f"Sample orphan IDs : {list(orphans)[:10]}")
+
+    new_id_range = set(range(last_valid_id + 1,
+                              last_valid_id + 1 + null_mask.sum())) \
+                   if null_mask.sum() > 0 else set()
+
+    recoverable     = orphans & new_id_range
+    unrecoverable   = orphans - new_id_range
+
+    print(f"  Recoverable (within new ID range) : {len(recoverable)}")
+    print(f"  Unrecoverable (unknown origin)    : {len(unrecoverable)}")
+
+    if unrecoverable:
+        print(f"  Dropping {len(unrecoverable)} truly orphaned match_ids from deliveries")
+        deliveries = deliveries[
+            ~deliveries["match_id"].isin(unrecoverable)
+        ].copy()
+
+orphans_after = set(deliveries["match_id"].unique()) - set(matches["match_id"].unique())
+print(f"\nOrphaned deliveries after fix : {len(orphans_after)}")
+
+matches.to_csv(RAW / "matches.csv",    index=False)
+deliveries.to_csv(RAW / "deliveries.csv", index=False)
+
+print("\n=== Saved ===")
+print(f"  matches.csv    : {matches.shape}")
+print(f"  deliveries.csv : {deliveries.shape}")
+print(f"  Seasons        : {sorted(matches['season'].unique())}")
+
+m2 = pd.read_csv(RAW / "matches.csv")
+d2 = pd.read_csv(RAW / "deliveries.csv")
+
+print("\n=== Post-save verification ===")
+print(f"  matches ID col      : {'match_id' if 'match_id' in m2.columns else 'id'}")
+print(f"  deliveries ID col   : {'match_id' if 'match_id' in d2.columns else 'id'}")
+print(f"  match_id nulls      : {m2['match_id'].isna().sum() if 'match_id' in m2.columns else d2['id'].isna().sum()}")
+final_orphans = set(d2["match_id" if "match_id" in d2.columns else "id"].unique()) - \
+                set(m2["match_id" if "match_id" in m2.columns else "id"].unique())
+print(f"  Final orphan count  : {len(final_orphans)}")
+
+if len(final_orphans) == 0:
+    print("\nAll clean. Re-run notebook 02 now.")
+else:
+    print(f"\nWARNING — {len(final_orphans)} orphans remain. Share output for further diagnosis.")

src/models.py

@@ -0,0 +1,240 @@
+import joblib, json
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning, module="sklearn")
+
+# ── Paths ─────────────────────────────────────────────────────────────
+_BASE    = Path(__file__).parent.parent   # project root
+MODELS   = _BASE / "data" / "processed" / "models"
+DATA     = _BASE / "data" / "processed"
+
+# ── Load artefacts once at import time ────────────────────────────────
+_wp_model  = joblib.load(MODELS / "win_prob_model.pkl")
+_auc_model = joblib.load(MODELS / "auction_model.pkl")
+
+with open(MODELS / "win_prob_features.json")  as f: _wp_features  = json.load(f)
+with open(MODELS / "auction_features.json")   as f: _auc_features = json.load(f)
+
+_matches    = pd.read_csv(DATA   / "matches_clean.csv",   parse_dates=["date"])
+_deliveries = pd.read_csv(DATA   / "deliveries_clean.csv", low_memory=False)
+
+
+# ── Internal helpers ──────────────────────────────────────────────────
+
+def _recent_form(team: str, past: pd.DataFrame, n: int = 5) -> float:
+    t = past[(past["team1"]==team)|(past["team2"]==team)].tail(n)
+    if len(t) == 0:
+        return 0.5
+    return round((t["winner"]==team).sum() / len(t), 4)
+
+
+def _h2h_win_rate(team_a: str, team_b: str, past: pd.DataFrame) -> tuple:
+    h = past[
+        ((past["team1"]==team_a)&(past["team2"]==team_b))|
+        ((past["team1"]==team_b)&(past["team2"]==team_a))
+    ]
+    if len(h) == 0:
+        return 0.5, 0
+    return round((h["winner"]==team_a).sum() / len(h), 4), len(h)
+
+
+def _venue_win_rate(team: str, venue: str, past: pd.DataFrame) -> float:
+    v  = past[past["venue"]==venue]
+    vt = v[(v["team1"]==team)|(v["team2"]==team)]
+    if len(vt) == 0:
+        return 0.5
+    return round((vt["winner"]==team).sum() / len(vt), 4)
+
+
+def _team_avg_score(team: str, past_ids: list) -> float:
+    d = _deliveries[_deliveries["match_id"].isin(past_ids)]
+    if d.empty:
+        return 150.0
+    scores = d[d["batting_team"]==team].groupby("match_id")["total_runs"].sum()
+    return round(scores.mean() if len(scores) > 0 else 150.0, 2)
+
+
+def _team_avg_wickets(team: str, past_ids: list) -> float:
+    d = _deliveries[_deliveries["match_id"].isin(past_ids)]
+    if d.empty:
+        return 7.0
+    wkts = d[d["bowling_team"]==team].groupby("match_id")["is_wicket"].sum()
+    return round(wkts.mean() if len(wkts) > 0 else 7.0, 2)
+
+
+def _form_std(team: str, past: pd.DataFrame, n: int = 8) -> float:
+    t = past[(past["team1"]==team)|(past["team2"]==team)].tail(n)
+    if len(t) < 3:
+        return 0.3
+    return round(float(np.std((t["winner"]==team).astype(int).tolist())), 4)
+
+
+# ── Public API ────────────────────────────────────────────────────────
+
+def get_available_teams() -> list:
+    """Return sorted list of all IPL team names in the dataset."""
+    all_teams = pd.concat([
+        _matches["team1"], _matches["team2"]
+    ]).dropna().unique()
+    return sorted(all_teams.tolist())
+
+
+def get_available_venues() -> list:
+    """Return sorted list of all venues in the dataset."""
+    return sorted(_matches["venue"].dropna().unique().tolist())
+
+
+def predict_winner(team1: str, team2: str, venue: str,
+                   toss_winner: str, toss_decision: str,
+                   season: int = None) -> dict:
+    """
+    Predict win probability for a match before it starts.
+
+    team_A is always the toss winner — consistent with how the model
+    was trained (toss_winner_won as target).
+
+    Returns
+    -------
+    dict with keys:
+        toss_winner      : str
+        other_team       : str
+        toss_winner_prob : float  (0–100)
+        other_team_prob  : float  (0–100)
+        predicted_winner : str
+        key_factors      : dict
+    """
+    
+    
+    past     = _matches[
+        _matches["winner"].notna() &
+        (_matches["winner"] != "No Result")
+    ].copy()
+    past_ids = past["match_id"].tolist()
+
+    team_A = toss_winner
+    team_B = team2 if toss_winner == team1 else team1
+    
+    
+    def _venue_exp(team):
+        v = past[past["venue"] == venue]
+        vt = v[(v["team1"] == team) | (v["team2"] == team)]
+        return len(vt)
+    
+    A_venue_exp = _venue_exp(team_A)
+    B_venue_exp = _venue_exp(team_B)
+    venue_exp_diff = A_venue_exp - B_venue_exp
+
+    A_form5    = _recent_form(team_A, past, 5)
+    B_form5    = _recent_form(team_B, past, 5)
+    A_form10   = _recent_form(team_A, past, 10)
+    B_form10   = _recent_form(team_B, past, 10)
+    A_overall  = _recent_form(team_A, past, len(past))
+    B_overall  = _recent_form(team_B, past, len(past))
+    A_venue    = _venue_win_rate(team_A, venue, past)
+    B_venue    = _venue_win_rate(team_B, venue, past)
+    A_score    = _team_avg_score(team_A,   past_ids)
+    B_score    = _team_avg_score(team_B,   past_ids)
+    A_wkts     = _team_avg_wickets(team_A, past_ids)
+    B_wkts     = _team_avg_wickets(team_B, past_ids)
+    h2h_wr, h2h_n = _h2h_win_rate(team_A, team_B, past)
+    A_cons     = _form_std(team_A, past)
+    B_cons     = _form_std(team_B, past)
+
+    v_past = past[past["venue"] == venue]
+    if len(v_past) >= 5 and "batting_first_won" in v_past.columns:
+        vbfwr = round(
+            (v_past["batting_first_won"]==1).sum() / len(v_past), 4
+        )
+    else:
+        vbfwr = 0.5
+
+    toss_correct = int(
+        (toss_decision == "bat"   and vbfwr >= 0.5) or
+        (toss_decision == "field" and vbfwr <  0.5)
+    )
+
+    features = {
+        "A_form5"           : A_form5,
+        "B_form5"           : B_form5,
+        "form_diff5"        : round(A_form5  - B_form5,  4),
+        "A_form10"          : A_form10,
+        "B_form10"          : B_form10,
+        "form_diff10"       : round(A_form10 - B_form10, 4),
+        "A_overall_wr"      : A_overall,
+        "B_overall_wr"      : B_overall,
+        "overall_wr_diff"   : round(A_overall - B_overall, 4),
+        "A_venue_wr"        : A_venue,
+        "B_venue_wr"        : B_venue,
+        "venue_wr_diff"     : round(A_venue - B_venue, 4),
+        "A_venue_exp"       : A_venue_exp,
+        "B_venue_exp"       : B_venue_exp,
+        "venue_exp_diff"    : venue_exp_diff,
+        "venue_bat_first_wr": vbfwr,
+        "A_avg_score"       : A_score,
+        "B_avg_score"       : B_score,
+        "score_diff"        : round(A_score - B_score, 2),
+        "A_avg_wickets"     : A_wkts,
+        "B_avg_wickets"     : B_wkts,
+        "wicket_diff"       : round(A_wkts  - B_wkts,  2),
+        "h2h_wr_A"          : h2h_wr,
+        "h2h_n"             : h2h_n,
+        "toss_decision_bat" : 1 if toss_decision == "bat" else 0,
+        "toss_correct"      : toss_correct,
+        "A_consistency"     : A_cons,
+        "B_consistency"     : B_cons,
+        "season_stage"      : 1,
+    }
+
+    df   = pd.DataFrame([features])[_wp_features]
+    prob = _wp_model.predict_proba(df)[0]
+    a_prob = round(float(prob[1]) * 100, 1)
+    b_prob = round(100 - a_prob, 1)
+
+    return {
+        "toss_winner"     : team_A,
+        "other_team"      : team_B,
+        "toss_winner_prob": a_prob,
+        "other_team_prob" : b_prob,
+        "predicted_winner": team_A if a_prob >= 50 else team_B,
+        "key_factors"     : {
+            "form_edge" : team_A if A_form5   > B_form5   else team_B,
+            "venue_edge": team_A if A_venue   > B_venue   else team_B,
+            "h2h_edge"  : team_A if h2h_wr    > 0.5       else team_B,
+            "score_edge": team_A if A_score   > B_score   else team_B,
+        },
+    }
+
+
+def predict_auction_value(player_stats: dict) -> dict:
+    """
+    Predict IPL auction price given a player stats dictionary.
+
+    Returns
+    -------
+    dict with keys:
+        predicted_price_cr : float
+        tier               : str
+    """
+    defaults = {
+        "total_runs":0, "batting_average":0, "strike_rate":0,
+        "hundreds":0, "fifties":0, "boundary_rate":0,
+        "sr_powerplay":110, "sr_death":120, "dot_ball_pct_bat":30,
+        "wickets":0, "economy_rate":10.5, "bowling_average":50,
+        "bowling_sr":40, "dot_ball_pct_bowl":30, "economy_death":11,
+        "economy_powerplay":9, "three_wicket_haul":0,
+        "matches_batted":0, "matches_bowled":0, "role":0,
+    }
+    defaults.update(player_stats)
+
+    df    = pd.DataFrame([defaults])[_auc_features]
+    price = round(float(_auc_model.predict(df)[0]), 2)
+    price = max(0.2, price)
+
+    if price >= 12: tier = "Icon (12 Cr+)"
+    elif price >= 7: tier = "Premium (7–12 Cr)"
+    elif price >= 3: tier = "Standard (3–7 Cr)"
+    else:            tier = "Emerging (< 3 Cr)"
+
+    return {"predicted_price_cr": price, "tier": tier}

src/update_dataset.py

@@ -0,0 +1,399 @@
+# src/update_dataset.py
+import requests, zipfile, io, datetime, json
+import pandas as pd
+import numpy as np
+from pathlib import Path
+
+RAW = Path("E:/ipl-analytics/data/raw")
+
+SLASH_SEASON_MAP = {
+    "2007/08" : 2008,
+    "2009/10" : 2010,
+    "2020/21" : 2020,
+}
+
+def normalize_season(val) -> int:
+    val = str(val).strip()
+    if val in SLASH_SEASON_MAP:
+        return SLASH_SEASON_MAP[val]
+    if "/" in val:
+        parts  = val.split("/")
+        prefix = parts[0].strip()[:2]
+        suffix = parts[1].strip()
+        return int(prefix + suffix)
+    return int(float(val.replace("IPL","").replace("Season","").strip()))
+
+
+def safe_int(val, default: int = 0) -> int:
+    try:
+        if val is None:
+            return default
+        if isinstance(val, float) and np.isnan(val):
+            return default
+        return int(float(val))
+    except (ValueError, TypeError):
+        return default
+
+
+def download_cricsheet_json() -> Path:
+    url = "https://cricsheet.org/downloads/ipl_json.zip"
+    print("Fetching Cricsheet IPL JSON data...")
+    r = requests.get(url, timeout=180)
+    r.raise_for_status()
+    out = RAW / "cricsheet_ipl_json"
+    out.mkdir(parents=True, exist_ok=True)
+    zipfile.ZipFile(io.BytesIO(r.content)).extractall(out)
+    json_files = list(out.glob("*.json"))
+    print(f"  {len(json_files)} JSON match files extracted.")
+    return out
+
+
+def get_existing_seasons(matches_path: Path) -> set:
+    df = pd.read_csv(matches_path, usecols=["season"])
+    return set(df["season"].apply(normalize_season).unique())
+
+
+def parse_json_match(filepath: Path, match_id: int) -> tuple:
+    try:
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+    except Exception:
+        return None, []
+
+    info = data.get("info", {})
+
+    # ── Season ────────────────────────────────────────────────────────
+    try:
+        season = normalize_season(info.get("season", ""))
+    except Exception:
+        return None, []
+
+    # ── Teams ─────────────────────────────────────────────────────────
+    teams = info.get("teams", [])
+    if len(teams) < 2:
+        return None, []
+    team1 = teams[0]
+    team2 = teams[1]
+
+    # ── Date ──────────────────────────────────────────────────────────
+    dates       = info.get("dates", [])
+    raw_date    = dates[0] if dates else np.nan
+    parsed_date = pd.to_datetime(raw_date, errors="coerce")
+    day_of_week = parsed_date.day_name() if pd.notna(parsed_date) else np.nan
+    month       = int(parsed_date.month)  if pd.notna(parsed_date) else np.nan
+
+    # ── Toss — directly from info["toss"] dict ────────────────────────
+    toss          = info.get("toss", {})
+    toss_winner   = toss.get("winner",   np.nan)
+    toss_decision = toss.get("decision", np.nan)
+
+    # ── Outcome ───────────────────────────────────────────────────────
+    outcome      = info.get("outcome", {})
+    winner       = outcome.get("winner", np.nan)
+    is_no_result = 0
+
+    if pd.isna(winner) or winner == "":
+        if outcome.get("result") == "no result" or "result" in outcome:
+            winner       = "No Result"
+            is_no_result = 1
+        else:
+            winner = "No Result"
+
+    by            = outcome.get("by", {})
+    result_margin = 0
+    result_type   = "normal"
+    if "runs" in by:
+        result_margin = safe_int(by["runs"])
+        result_type   = "runs"
+    elif "wickets" in by:
+        result_margin = safe_int(by["wickets"])
+        result_type   = "wickets"
+
+    # ── Player of match ───────────────────────────────────────────────
+    pom_list        = info.get("player_of_match", [])
+    player_of_match = pom_list[0] if pom_list else np.nan
+
+    # ── Venue / city ──────────────────────────────────────────────────
+    venue = info.get("venue", np.nan)
+    city  = info.get("city",  np.nan)
+
+    match_row = {
+        "match_id"        : match_id,
+        "season"          : season,
+        "date"            : raw_date,
+        "team1"           : team1,
+        "team2"           : team2,
+        "toss_winner"     : toss_winner,
+        "toss_decision"   : toss_decision,
+        "winner"          : winner,
+        "player_of_match" : player_of_match,
+        "venue"           : venue,
+        "result"          : result_type,
+        "result_margin"   : result_margin,
+        "method"          : info.get("method", np.nan),
+        "city"            : city,
+        "is_no_result"    : is_no_result,
+        "result_type"     : result_type,
+        "day_of_week"     : day_of_week,
+        "month"           : month,
+    }
+
+    # ── Deliveries ────────────────────────────────────────────────────
+    delivery_rows = []
+
+    for inning_idx, inning in enumerate(data.get("innings", []), start=1):
+        batting_team = inning.get("team", np.nan)
+        bowling_team = team2 if batting_team == team1 else team1
+
+        for over_data in inning.get("overs", []):
+            over_num = safe_int(over_data.get("over", 0)) + 1
+
+            if over_num <= 6:    phase = "powerplay"
+            elif over_num <= 15: phase = "middle"
+            else:                phase = "death"
+
+            for ball_idx, delivery in enumerate(
+                over_data.get("deliveries", []), start=1
+            ):
+                runs       = delivery.get("runs", {})
+                bat_runs   = safe_int(runs.get("batter", 0))
+                extra_runs = safe_int(runs.get("extras", 0))
+                total_runs = safe_int(runs.get("total",  0))
+
+                extras      = delivery.get("extras", {})
+                wide_runs   = safe_int(extras.get("wides",   0))
+                noball      = safe_int(extras.get("noballs", 0))
+                bye_runs    = safe_int(extras.get("byes",    0))
+                legbye_runs = safe_int(extras.get("legbyes", 0))
+                is_legal    = 1 if (wide_runs == 0 and noball == 0) else 0
+
+                wickets          = delivery.get("wickets", [])
+                is_wicket        = 1 if wickets else 0
+                wicket_type      = np.nan
+                player_dismissed = np.nan
+                fielder          = np.nan
+
+                if wickets:
+                    w                = wickets[0]
+                    wicket_type      = w.get("kind",       np.nan)
+                    player_dismissed = w.get("player_out", np.nan)
+                    fielders         = w.get("fielders",   [])
+                    fielder          = (fielders[0].get("name", np.nan)
+                                        if fielders else np.nan)
+
+                delivery_rows.append({
+                    "match_id"         : match_id,
+                    "inning"           : inning_idx,
+                    "batting_team"     : batting_team,
+                    "bowling_team"     : bowling_team,
+                    "over"             : over_num,
+                    "ball"             : ball_idx,
+                    "batter"           : delivery.get("batter",      np.nan),
+                    "non_striker"      : delivery.get("non_striker", np.nan),
+                    "bowler"           : delivery.get("bowler",      np.nan),
+                    "wide_runs"        : wide_runs,
+                    "bye_runs"         : bye_runs,
+                    "legbye_runs"      : legbye_runs,
+                    "noball_runs"      : noball,
+                    "batsman_runs"     : bat_runs,
+                    "extra_runs"       : extra_runs,
+                    "total_runs"       : total_runs,
+                    "player_dismissed" : player_dismissed,
+                    "dismissal_kind"   : wicket_type,
+                    "fielder"          : fielder,
+                    "extras_type"      : np.nan,
+                    "is_wicket"        : is_wicket,
+                    "is_four"          : 1 if bat_runs == 4 else 0,
+                    "is_six"           : 1 if bat_runs == 6 else 0,
+                    "is_dot_ball"      : 1 if (bat_runs  == 0 and
+                                               wide_runs == 0 and
+                                               noball    == 0) else 0,
+                    "is_legal_delivery": is_legal,
+                    "over_phase"       : phase,
+                })
+
+    return match_row, delivery_rows
+
+
+def build_dataframes_from_json(json_dir: Path,
+                                seasons_to_add: list,
+                                start_match_id: int) -> tuple:
+    json_files = sorted(json_dir.glob("*.json"))
+    print(f"Scanning {len(json_files)} JSON files "
+          f"for seasons {seasons_to_add}...")
+
+    match_rows    = []
+    delivery_rows = []
+    next_id       = start_match_id
+    skipped       = 0
+
+    for fp in json_files:
+        try:
+            with open(fp, encoding="utf-8") as f:
+                peek = json.load(f)
+            season = normalize_season(
+                peek.get("info", {}).get("season", "")
+            )
+        except Exception:
+            skipped += 1
+            continue
+
+        if season not in seasons_to_add:
+            continue
+
+        match_row, deliveries = parse_json_match(fp, next_id)
+        if match_row is None:
+            skipped += 1
+            continue
+
+        match_rows.append(match_row)
+        delivery_rows.extend(deliveries)
+        next_id += 1
+
+    new_matches    = pd.DataFrame(match_rows)
+    new_deliveries = pd.DataFrame(delivery_rows)
+
+    if not new_matches.empty:
+        print(f"  Parsed          : {len(new_matches)} matches, "
+              f"{len(new_deliveries):,} deliveries")
+        print(f"  toss_winner nulls : "
+              f"{new_matches['toss_winner'].isna().sum()}")
+        print(f"  winner nulls      : "
+              f"{new_matches['winner'].isna().sum()}")
+    print(f"  Skipped         : {skipped} files")
+
+    return new_matches, new_deliveries
+
+
+def append_new_seasons(seasons_to_add: list = None,
+                        force: list = None):
+    matches_path    = RAW / "matches.csv"
+    deliveries_path = RAW / "deliveries.csv"
+
+    existing_matches    = pd.read_csv(matches_path)
+    existing_deliveries = pd.read_csv(
+        deliveries_path, low_memory=False
+    )
+
+    # ── Normalise ID column ────────────────────────────────────────────
+    if "id" in existing_matches.columns \
+            and "match_id" not in existing_matches.columns:
+        existing_matches = existing_matches.rename(
+            columns={"id": "match_id"}
+        )
+        existing_matches.to_csv(matches_path, index=False)
+        print("Renamed 'id' → 'match_id' in matches.csv")
+
+    if "id" in existing_deliveries.columns \
+            and "match_id" not in existing_deliveries.columns:
+        existing_deliveries = existing_deliveries.rename(
+            columns={"id": "match_id"}
+        )
+        existing_deliveries.to_csv(deliveries_path, index=False)
+        print("Renamed 'id' → 'match_id' in deliveries.csv")
+
+    existing_seasons = get_existing_seasons(matches_path)
+    print(f"Existing seasons : {sorted(existing_seasons)}")
+
+    current_year = datetime.date.today().year
+    if seasons_to_add is None:
+        seasons_to_add = list(range(2025, current_year + 1))
+
+    # ── Force refresh ──────────────────────────────────────────────────
+    if force:
+        print(f"Force-refreshing : {force}")
+        existing_matches["_season_norm"] = (
+            existing_matches["season"].apply(normalize_season)
+        )
+        forced_match_ids = set(
+            existing_matches.loc[
+                existing_matches["_season_norm"].isin(force),
+                "match_id"
+            ].tolist()
+        )
+        existing_matches = existing_matches[
+            ~existing_matches["_season_norm"].isin(force)
+        ].drop(columns=["_season_norm"]).copy()
+
+        existing_deliveries = existing_deliveries[
+            ~existing_deliveries["match_id"].isin(forced_match_ids)
+        ].copy()
+
+        existing_seasons -= set(force)
+        print(f"  Removed {len(forced_match_ids)} matches "
+              f"and their deliveries.")
+
+    seasons_to_add = [
+        s for s in seasons_to_add if s not in existing_seasons
+    ]
+
+    if not seasons_to_add:
+        print("All requested seasons already present.")
+        print("To refresh: python update_dataset.py 2026 --force")
+        return
+
+    print(f"Seasons to add   : {seasons_to_add}")
+
+    json_dir = download_cricsheet_json()
+    next_id  = int(existing_matches["match_id"].max()) + 1
+
+    new_matches, new_deliveries = build_dataframes_from_json(
+        json_dir, seasons_to_add, next_id
+    )
+
+    if new_matches.empty:
+        print(f"No matches found for {seasons_to_add}.")
+        return
+
+    # ── Align columns ──────────────────────────────────────────────────
+    for col in existing_matches.columns:
+        if col not in new_matches.columns:
+            new_matches[col] = np.nan
+    new_matches = new_matches[existing_matches.columns]
+
+    for col in existing_deliveries.columns:
+        if col not in new_deliveries.columns:
+            new_deliveries[col] = np.nan
+    new_deliveries = new_deliveries[existing_deliveries.columns]
+
+    assert new_matches["match_id"].notna().all(), \
+        "Null match_id in new matches after alignment — abort"
+
+    # ── Concat and save ───────────────────────────────────────────────
+    combined_matches = pd.concat(
+        [existing_matches, new_matches], ignore_index=True
+    )
+    combined_deliveries = pd.concat(
+        [existing_deliveries, new_deliveries], ignore_index=True
+    )
+
+    combined_matches.to_csv(matches_path,    index=False)
+    combined_deliveries.to_csv(deliveries_path, index=False)
+
+    print(f"\nDone:")
+    print(f"  matches.csv    : {len(combined_matches):,} rows "
+          f"(added {len(new_matches)})")
+    print(f"  deliveries.csv : {len(combined_deliveries):,} rows "
+          f"(added {len(new_deliveries):,})")
+    print(f"  New seasons    : "
+          f"{sorted(new_matches['season'].unique())}")
+    print(f"  toss_winner nulls : "
+          f"{new_matches['toss_winner'].isna().sum()}")
+    print(f"  winner nulls      : "
+          f"{new_matches['winner'].isna().sum()}")
+
+
+# ── CLI ───────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    import sys
+
+    args  = [a for a in sys.argv[1:] if not a.startswith("--")]
+    flags = [a for a in sys.argv[1:] if     a.startswith("--")]
+
+    force_refresh = [int(a) for a in args] if "--force" in flags else None
+    seasons       = [int(a) for a in args] if "--force" not in flags else None
+
+    append_new_seasons(
+        seasons_to_add=seasons,
+        force=force_refresh,
+    )

waitress_server.py

@@ -0,0 +1,15 @@
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).parent
+DASHBOARD = ROOT / "dashboard"
+sys.path.insert(0, str(ROOT))
+sys.path.insert(0, str(DASHBOARD))
+
+from waitress import serve
+from dashboard.app import server
+
+if __name__ == "__main__":
+    print("Starting IPL Analytics Platform...")
+    print("Open http://0.0.0.0:8050 in your browser")
+    serve(server, host="0.0.0.0", port=8050, threads=4)