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fad7dff 29442f6 fad7dff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | 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
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} |