{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "cd16fdae", "metadata": {}, "outputs": [], "source": [ "from __future__ import annotations\n", "import os\n", "from datetime import date\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from pybaseball import statcast\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.cluster import KMeans\n", "from sklearn.neighbors import NearestNeighbors\n", "\n", "import plotly.express as px\n", "import plotly.graph_objects as go" ] }, { "cell_type": "code", "execution_count": 2, "id": "4c2c0387", "metadata": {}, "outputs": [], "source": [ "pd.set_option(\"display.max_columns\", 200)\n", "CACHE_DIR = Path(\"data/cache\")\n", "CACHE_DIR.mkdir(parents=True, exist_ok=True)\n", "ARTIFACTS_DIR = Path(\"artifacts\")\n", "ARTIFACTS_DIR.mkdir(parents=True, exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 3, "id": "11bb8296", "metadata": {}, "outputs": [], "source": [ "def default_window():\n", " \"\"\"Return a recent season window (approx opening day to today).\"\"\"\n", " today = date.today()\n", " start = date(today.year if today.month >= 3 else today.year - 1, 3, 1)\n", " return start.isoformat(), today.isoformat()\n", "\n", "\n", "def cache_path(start: str, end: str) -> Path:\n", " return CACHE_DIR / f\"statcast_{start}_{end}.parquet\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "cc0b9aee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('2025-03-01', '2025-10-30')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def load_statcast(start_date: str, end_date: str, force: bool = False) -> pd.DataFrame:\n", " \"\"\"Download Statcast data with simple caching to Parquet.\"\"\"\n", " cp = cache_path(start_date, end_date)\n", " if cp.exists() and not force:\n", " print(f\"Loading cached: {cp}\")\n", " return pd.read_parquet(cp)\n", " print(\"Downloading from Statcast (pybaseball)... window size affects duration.\")\n", " df = statcast(start_dt=start_date, end_dt=end_date)\n", " if \"pitch_type\" in df.columns:\n", " df = df[df[\"pitch_type\"].notna()]\n", " df.to_parquet(cp, index=False)\n", " print(f\"Cached to: {cp}\")\n", " return df\n", "\n", "\n", "start_date, end_date = default_window()\n", "start_date, end_date" ] }, { "cell_type": "code", "execution_count": 5, "id": "db1cf23c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading cached: data/cache/statcast_2025-03-01_2025-10-30.parquet\n" ] }, { "data": { "text/plain": [ "((752024, 118),\n", " pitch_type game_date release_speed release_pos_x release_pos_z \\\n", " 0 SL 2025-10-29 86.7 -2.63 5.58 \n", " 1 SL 2025-10-29 87.0 -2.55 5.6 \n", " 2 SL 2025-10-29 87.2 -2.58 5.59 \n", " \n", " player_name batter pitcher events description spin_dir \\\n", " 0 Hoffman, Jeff 606192 656546 strikeout swinging_strike \n", " 1 Hoffman, Jeff 606192 656546 None blocked_ball \n", " 2 Hoffman, Jeff 606192 656546 None ball \n", " \n", " spin_rate_deprecated break_angle_deprecated break_length_deprecated \\\n", " 0 \n", " 1 \n", " 2 \n", " \n", " zone des game_type stand p_throws \\\n", " 0 14 Teoscar Hernández strikes out swinging. W R R \n", " 1 14 Ball In Dirt W R R \n", " 2 14 Ball W R R \n", " \n", " home_team away_team type hit_location bb_type balls strikes game_year \\\n", " 0 LAD TOR S 2 None 2 2 2025 \n", " 1 LAD TOR B None 1 2 2025 \n", " 2 LAD TOR B None 0 2 2025 \n", " \n", " pfx_x pfx_z plate_x plate_z on_3b on_2b on_1b outs_when_up inning \\\n", " 0 0.25 0.13 0.63 1.1 669257 2 9 \n", " 1 0.22 0.07 1.22 0.09 669257 2 9 \n", " 2 0.13 0.2 0.82 1.34 669257 2 9 \n", " \n", " inning_topbot hc_x hc_y tfs_deprecated tfs_zulu_deprecated umpire \\\n", " 0 Bot \n", " 1 Bot \n", " 2 Bot \n", " \n", " sv_id vx0 vy0 vz0 ax ay az \\\n", " 0 7.310843 -126.234364 -4.940849 1.600881 19.561186 -30.09391 \n", " 1 8.567988 -126.485626 -7.272101 1.046798 20.184028 -30.388937 \n", " 2 7.933229 -126.959588 -4.65206 0.265795 19.239855 -29.350702 \n", " \n", " sz_top sz_bot hit_distance_sc launch_speed launch_angle \\\n", " 0 3.43 1.59 \n", " 1 3.35 1.59 \n", " 2 3.37 1.59 \n", " \n", " effective_speed release_spin_rate release_extension game_pk fielder_2 \\\n", " 0 87.8 2674 6.4 813022 672386 \n", " 1 87.6 2811 6.3 813022 672386 \n", " 2 88.4 2804 6.4 813022 672386 \n", " \n", " fielder_3 fielder_4 fielder_5 fielder_6 fielder_7 fielder_8 \\\n", " 0 665489 643396 676391 665926 664702 662139 \n", " 1 665489 643396 676391 665926 664702 662139 \n", " 2 665489 643396 676391 665926 664702 662139 \n", " \n", " fielder_9 release_pos_y estimated_ba_using_speedangle \\\n", " 0 680718 54.08 \n", " 1 680718 54.24 \n", " 2 680718 54.09 \n", " \n", " estimated_woba_using_speedangle woba_value woba_denom babip_value \\\n", " 0 0.0 0.0 1 0 \n", " 1 \n", " 2 \n", " \n", " iso_value launch_speed_angle at_bat_number pitch_number pitch_name \\\n", " 0 0 73 5 Slider \n", " 1 73 4 Slider \n", " 2 73 3 Slider \n", " \n", " home_score away_score bat_score fld_score post_away_score \\\n", " 0 1 6 1 6 6 \n", " 1 1 6 1 6 6 \n", " 2 1 6 1 6 6 \n", " \n", " post_home_score post_bat_score post_fld_score if_fielding_alignment \\\n", " 0 1 1 6 Standard \n", " 1 1 1 6 Standard \n", " 2 1 1 6 Standard \n", " \n", " of_fielding_alignment spin_axis delta_home_win_exp delta_run_exp \\\n", " 0 Standard 89 -0.001 -0.216 \n", " 1 Standard 25 0.0 0.033 \n", " 2 Standard 30 0.0 0.009 \n", " \n", " bat_speed swing_length estimated_slg_using_speedangle \\\n", " 0 66.1 8.5 \n", " 1 \n", " 2 \n", " \n", " delta_pitcher_run_exp hyper_speed home_score_diff bat_score_diff \\\n", " 0 0.216 -5 -5 \n", " 1 -0.033 -5 -5 \n", " 2 -0.009 -5 -5 \n", " \n", " home_win_exp bat_win_exp age_pit_legacy age_bat_legacy age_pit \\\n", " 0 0.001 0.001 32 32 32 \n", " 1 0.001 0.001 32 32 32 \n", " 2 0.001 0.001 32 32 32 \n", " \n", " age_bat n_thruorder_pitcher n_priorpa_thisgame_player_at_bat \\\n", " 0 33 1 3 \n", " 1 33 1 3 \n", " 2 33 1 3 \n", " \n", " pitcher_days_since_prev_game batter_days_since_prev_game \\\n", " 0 \n", " 1 \n", " 2 \n", " \n", " pitcher_days_until_next_game batter_days_until_next_game \\\n", " 0 \n", " 1 \n", " 2 \n", " \n", " api_break_z_with_gravity api_break_x_arm api_break_x_batter_in \\\n", " 0 2.84 -0.25 -0.25 \n", " 1 2.9 -0.22 -0.22 \n", " 2 2.73 -0.13 -0.13 \n", " \n", " arm_angle attack_angle attack_direction swing_path_tilt \\\n", " 0 21.292185 -28.318897 34.361479 \n", " 1 \n", " 2 \n", " \n", " intercept_ball_minus_batter_pos_x_inches \\\n", " 0 43.225687 \n", " 1 \n", " 2 \n", " \n", " intercept_ball_minus_batter_pos_y_inches \n", " 0 45.809537 \n", " 1 \n", " 2 )" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_raw = load_statcast(start_date, end_date, force=False)\n", "df_raw.shape, df_raw.head(3)" ] }, { "cell_type": "code", "execution_count": 6, "id": "55548f5d", "metadata": {}, "outputs": [], "source": [ "INCHES_PER_FOOT = 12.0\n", "\n", "def _safe_rate(num, den):\n", " return np.divide(\n", " num, den, out=np.full_like(num, np.nan, dtype=float), where=den > 0\n", " )\n", "\n", "\n", "def signed_arm_side(hb_in_raw: pd.Series, p_throws: pd.Series) -> pd.Series:\n", " \"\"\"\n", " Convert Statcast pfx_x (catcher-right positive) into 'arm-side positive' for both RHP and LHP.\n", " For RHP: arm-side = +pfx_x.\n", " For LHP: arm-side = -pfx_x (flip sign).\n", " \"\"\"\n", " handed = p_throws.fillna(\"R\").str.upper().str[0]\n", " sign = np.where(handed == \"R\", 1.0, -1.0)\n", " return -hb_in_raw * sign\n", "\n", "\n", "def engineer_pitch_features(df: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"\n", " Aggregates to (player_name, pitch_type, p_throws) with handedness-aware XY:\n", " X = hb_as_in (Arm-Side + / Glove-Side -), Y = ivb_in (Ride + / Drop -)\n", " Also computes CSW, Whiff, GB, Zone%.\n", " \"\"\"\n", " cols = [\n", " \"pitch_type\",\n", " \"player_name\",\n", " \"game_date\",\n", " \"events\",\n", " \"description\",\n", " \"p_throws\",\n", " \"stand\",\n", " \"release_pos_x\",\n", " \"release_pos_z\",\n", " \"pfx_x\",\n", " \"pfx_z\",\n", " \"release_speed\",\n", " \"release_spin_rate\",\n", " \"plate_x\",\n", " \"plate_z\",\n", " \"zone\",\n", " ]\n", " have = [c for c in cols if c in df.columns]\n", " df = df[have].copy()\n", "\n", " # outcomes\n", " df[\"is_called_strike\"] = (df[\"description\"] == \"called_strike\").astype(int)\n", " df[\"is_swing\"] = (\n", " df[\"description\"]\n", " .isin([\"swinging_strike\", \"swinging_strike_blocked\", \"foul\", \"hit_into_play\"])\n", " .astype(int)\n", " )\n", " df[\"is_whiff\"] = (\n", " df[\"description\"]\n", " .isin([\"swinging_strike\", \"swinging_strike_blocked\"])\n", " .astype(int)\n", " )\n", " df[\"is_ball\"] = (df[\"description\"] == \"ball\").astype(int)\n", " df[\"is_in_play\"] = (df[\"description\"] == \"hit_into_play\").astype(int)\n", " # quick GB proxy (refine later with launch angle)\n", " df[\"is_gb\"] = (\n", " df[\"events\"]\n", " .isin([\"groundout\", \"field_error\", \"single\", \"double\", \"triple\"])\n", " .astype(int)\n", " )\n", "\n", " # movement proxies in inches\n", " df[\"hb_in_raw\"] = df[\"pfx_x\"] * INCHES_PER_FOOT # + = break to catcher's right\n", " df[\"ivb_in\"] = df[\"pfx_z\"] * INCHES_PER_FOOT # + = ride, − = drop\n", " df[\"hb_as_in\"] = signed_arm_side(\n", " df[\"hb_in_raw\"], df.get(\"p_throws\")\n", " ) # + = arm-side, − = glove-side\n", "\n", " grp = df.groupby([\"player_name\", \"pitch_type\", \"p_throws\"], as_index=False)\n", " agg = grp.agg(\n", " n=(\"pitch_type\", \"size\"),\n", " velo=(\"release_speed\", \"mean\"),\n", " spin=(\"release_spin_rate\", \"mean\"),\n", " ivb_in=(\"ivb_in\", \"mean\"),\n", " hb_as_in=(\"hb_as_in\", \"mean\"),\n", " rel_height=(\"release_pos_z\", \"mean\"),\n", " rel_side=(\"release_pos_x\", \"mean\"),\n", " cs=(\"is_called_strike\", \"sum\"),\n", " swings=(\"is_swing\", \"sum\"),\n", " whiffs=(\"is_whiff\", \"sum\"),\n", " inplay=(\"is_in_play\", \"sum\"),\n", " gb=(\"is_gb\", \"sum\"),\n", " )\n", "\n", " agg[\"csw\"] = _safe_rate(agg[\"cs\"] + agg[\"whiffs\"], agg[\"n\"])\n", " agg[\"whiff_rate\"] = _safe_rate(agg[\"whiffs\"], agg[\"swings\"])\n", " agg[\"gb_rate\"] = _safe_rate(agg[\"gb\"], agg[\"inplay\"])\n", " # rough in-zone proxy\n", " agg[\"zone_pct\"] = _safe_rate(agg[\"cs\"] + agg[\"inplay\"], agg[\"n\"])\n", "\n", " keep = [\n", " \"player_name\",\n", " \"pitch_type\",\n", " \"p_throws\",\n", " \"n\",\n", " \"velo\",\n", " \"spin\",\n", " \"ivb_in\",\n", " \"hb_as_in\",\n", " \"rel_height\",\n", " \"rel_side\",\n", " \"csw\",\n", " \"whiff_rate\",\n", " \"gb_rate\",\n", " \"zone_pct\",\n", " ]\n", " out = agg[keep].dropna(subset=[\"velo\", \"ivb_in\", \"hb_as_in\"])\n", " return out" ] }, { "cell_type": "code", "execution_count": 7, "id": "bba4c7b2", "metadata": {}, "outputs": [], "source": [ "ARCH_FEATURES = [\n", " \"velo\",\n", " \"ivb_in\",\n", " \"hb_as_in\",\n", " \"rel_height\",\n", " \"rel_side\",\n", " \"spin\",\n", " \"csw\",\n", " \"whiff_rate\",\n", " \"gb_rate\",\n", " \"zone_pct\",\n", "]\n", "\n", "\n", "def fit_kmeans(df_feat: pd.DataFrame, k: int = 8, random_state: int = 42):\n", " df = df_feat.dropna(subset=ARCH_FEATURES).copy()\n", " X = df[ARCH_FEATURES].values\n", " scaler = StandardScaler()\n", " Xs = scaler.fit_transform(X)\n", " km = KMeans(n_clusters=k, n_init=20, random_state=random_state)\n", " labels = km.fit_predict(Xs)\n", " df[\"cluster\"] = labels\n", "\n", " nn = NearestNeighbors(n_neighbors=6, metric=\"euclidean\")\n", " nn.fit(Xs)\n", " return df, scaler, km, nn" ] }, { "cell_type": "code", "execution_count": 8, "id": "f3210171", "metadata": {}, "outputs": [], "source": [ "# --- Replacement: cluster tags using XY + functional flavor ---\n", "def _mag_label(v, q25, q75, small=\"Subtle\", mid=\"Moderate\", big=\"Heavy\"):\n", " if v >= q75:\n", " return big\n", " if v <= q25:\n", " return small\n", " return mid\n", "\n", "\n", "def _side_label(hb_as):\n", " return \"Arm-Side\" if hb_as >= 0 else \"Glove-Side\"\n", "\n", "\n", "def _vert_label(ivb):\n", " return \"Ride\" if ivb >= 0 else \"Drop\"\n", "\n", "\n", "def xy_cluster_tags(df_with_clusters: pd.DataFrame) -> dict[int, str]:\n", " \"\"\"\n", " Returns {cluster_id: label} like:\n", " Slider: Glove-Side • Moderate Sweep, Subtle Ride • Strike-Throwing\n", " Changeup: Arm-Side • Subtle Ride, Heavy Run • Grounder-First\n", " \"\"\"\n", " df = df_with_clusters.copy()\n", "\n", " q_abs_ivb25 = np.nanquantile(np.abs(df[\"ivb_in\"]), 0.25)\n", " q_abs_ivb75 = np.nanquantile(np.abs(df[\"ivb_in\"]), 0.75)\n", " q_abs_hb25 = np.nanquantile(np.abs(df[\"hb_as_in\"]), 0.25)\n", " q_abs_hb75 = np.nanquantile(np.abs(df[\"hb_as_in\"]), 0.75)\n", "\n", " q_wh75 = np.nanquantile(df[\"whiff_rate\"], 0.75)\n", " q_gb75 = np.nanquantile(df[\"gb_rate\"], 0.75)\n", " q_zn75 = np.nanquantile(df[\"zone_pct\"], 0.75)\n", "\n", " q_wh50 = np.nanquantile(df[\"whiff_rate\"], 0.50)\n", " q_gb50 = np.nanquantile(df[\"gb_rate\"], 0.50)\n", " q_zn50 = np.nanquantile(df[\"zone_pct\"], 0.50)\n", "\n", " tags = {}\n", " for c, sub in df.groupby(\"cluster\"):\n", " row = sub.mean(numeric_only=True)\n", " dom_pt = (\n", " sub[\"pitch_type\"].mode().iloc[0]\n", " if not sub[\"pitch_type\"].mode().empty\n", " else \"Pitch\"\n", " )\n", "\n", " side = _side_label(row[\"hb_as_in\"])\n", " vert = _vert_label(row[\"ivb_in\"])\n", "\n", " mag_side = _mag_label(\n", " abs(row[\"hb_as_in\"]), q_abs_hb25, q_abs_hb75\n", " ) # \"Subtle/Moderate/Heavy\"\n", " mag_vert = _mag_label(abs(row[\"ivb_in\"]), q_abs_ivb25, q_abs_ivb75)\n", "\n", " # Functional flavor\n", " flavor = []\n", " if row[\"whiff_rate\"] >= q_wh75:\n", " flavor.append(\"Whiff-First\")\n", " if row[\"gb_rate\"] >= q_gb75:\n", " flavor.append(\"Grounder-First\")\n", " if row[\"zone_pct\"] >= q_zn75:\n", " flavor.append(\"Strike-Throwing\")\n", " if not flavor:\n", " diffs = {\n", " \"Whiff-First\": row[\"whiff_rate\"] - q_wh50,\n", " \"Grounder-First\": row[\"gb_rate\"] - q_gb50,\n", " \"Strike-Throwing\": row[\"zone_pct\"] - q_zn50,\n", " }\n", " flavor.append(max(diffs, key=diffs.get))\n", "\n", " # Compose label: \": • , • \"\n", " # Examples:\n", " # \"Slider: Glove-Side • Heavy Sweep, Subtle Drop • Strike-Throwing\"\n", " # \"Changeup: Arm-Side • Moderate Run, Subtle Drop • Grounder-First\"\n", " side_noun = \"Sweep\" if side == \"Glove-Side\" else \"Run\"\n", " vert_noun = \"Ride\" if vert == \"Ride\" else \"Drop\"\n", " shape = f\"{side} • {mag_side} {side_noun}, {mag_vert} {vert_noun}\"\n", " tags[c] = f\"{dom_pt}: {shape} • \" + \" / \".join(flavor)\n", "\n", " return tags" ] }, { "cell_type": "code", "execution_count": 9, "id": "74c47e88", "metadata": {}, "outputs": [], "source": [ "def nearest_comps(\n", " row: pd.Series, df_fit: pd.DataFrame, scaler, nn, within_pitch_type=True, k=6\n", "):\n", " X_all = df_fit[ARCH_FEATURES].values\n", " xq = scaler.transform(row[ARCH_FEATURES].values.reshape(1, -1))\n", " dists, idxs = nn.kneighbors(xq, n_neighbors=k)\n", " comps = df_fit.iloc[idxs[0]].copy()\n", " if within_pitch_type:\n", " comps = comps[comps[\"pitch_type\"] == row[\"pitch_type\"]]\n", " return comps[\n", " [\n", " \"player_name\",\n", " \"pitch_type\",\n", " \"p_throws\",\n", " \"velo\",\n", " \"ivb_in\",\n", " \"hb_as_in\",\n", " \"whiff_rate\",\n", " \"gb_rate\",\n", " \"cluster_name\",\n", " ]\n", " ].head(k - 1)" ] }, { "cell_type": "code", "execution_count": 10, "id": "ebcbaea2", "metadata": {}, "outputs": [], "source": [ "def movement_scatter_xy(\n", " df: pd.DataFrame, color=\"pitch_type\", facet_by_handedness=False\n", "):\n", " df_plot = df.copy()\n", " if facet_by_handedness:\n", " fig = px.scatter(\n", " df_plot,\n", " x=\"hb_as_in\",\n", " y=\"ivb_in\",\n", " color=color,\n", " facet_col=\"p_throws\",\n", " hover_data=[\n", " \"player_name\",\n", " \"pitch_type\",\n", " \"p_throws\",\n", " \"velo\",\n", " \"whiff_rate\",\n", " \"gb_rate\",\n", " \"csw\",\n", " ],\n", " )\n", " else:\n", " fig = px.scatter(\n", " df_plot,\n", " x=\"hb_as_in\",\n", " y=\"ivb_in\",\n", " color=color,\n", " hover_data=[\n", " \"player_name\",\n", " \"pitch_type\",\n", " \"p_throws\",\n", " \"velo\",\n", " \"whiff_rate\",\n", " \"gb_rate\",\n", " \"csw\",\n", " ],\n", " )\n", "\n", " fig.update_layout(\n", " xaxis_title=\"Horizontal: Arm-Side (+) | Glove-Side (−)\",\n", " yaxis_title=\"Vertical: Ride (+) | Drop (−)\",\n", " legend_title_text=color,\n", " )\n", " # quadrant guides (for every subplot if faceted)\n", " for ax in fig.select_yaxes():\n", " fig.add_hline(\n", " y=0,\n", " line_dash=\"dot\",\n", " row=ax.anchor.split(\"y\")[-1] if facet_by_handedness else None,\n", " col=None,\n", " )\n", " for ax in fig.select_xaxes():\n", " fig.add_vline(\n", " x=0,\n", " line_dash=\"dot\",\n", " row=ax.anchor.split(\"x\")[-1] if facet_by_handedness else None,\n", " col=None,\n", " )\n", "\n", " # helpful annotations (single-plot case)\n", " if not facet_by_handedness:\n", " fig.add_annotation(\n", " x=1,\n", " y=0,\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " xanchor=\"left\",\n", " yanchor=\"top\",\n", " text=\"AS (+)\",\n", " showarrow=False,\n", " font=dict(size=10),\n", " )\n", " fig.add_annotation(\n", " x=0,\n", " y=0,\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " xanchor=\"right\",\n", " yanchor=\"top\",\n", " text=\"GS (−)\",\n", " showarrow=False,\n", " font=dict(size=10),\n", " )\n", " fig.add_annotation(\n", " x=0.5,\n", " y=1,\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " xanchor=\"center\",\n", " yanchor=\"bottom\",\n", " text=\"Ride (+)\",\n", " showarrow=False,\n", " font=dict(size=10),\n", " )\n", " fig.add_annotation(\n", " x=0.5,\n", " y=0,\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " xanchor=\"center\",\n", " yanchor=\"top\",\n", " text=\"Drop (−)\",\n", " showarrow=False,\n", " font=dict(size=10),\n", " )\n", " return fig\n", "\n", "\n", "def radar_quality(row: pd.Series):\n", " cats = [\"csw\", \"whiff_rate\", \"gb_rate\", \"zone_pct\"]\n", " vals = [row[c] for c in cats]\n", " fig = go.Figure(data=go.Scatterpolar(r=vals, theta=cats, fill=\"toself\"))\n", " fig.update_layout(\n", " polar=dict(radialaxis=dict(visible=True, range=[0, 1])), showlegend=False\n", " )\n", " return fig\n", "\n", "\n", "def xy_blurb(row: pd.Series) -> str:\n", " side = \"Arm-Side\" if row[\"hb_as_in\"] >= 0 else \"Glove-Side\"\n", " vert = \"Ride\" if row[\"ivb_in\"] >= 0 else \"Drop\"\n", " return (\n", " f\"{row['pitch_type']} ({row['p_throws']}): {side}, {vert}. \"\n", " f\"Velo {row['velo']:.1f} mph | AS {row['hb_as_in']:.1f}\\\" | \"\n", " f\"Ride {row['ivb_in']:.1f}\\\" | CSW {row['csw']:.2f} | \"\n", " f\"Whiff {row['whiff_rate']:.2f} | GB {row['gb_rate']:.2f} | Zone {row['zone_pct']:.2f}\"\n", " )" ] }, { "cell_type": "code", "execution_count": 11, "id": "a3d31501", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading cached: data/cache/statcast_2025-03-01_2025-10-30.parquet\n" ] } ], "source": [ "start_date, end_date = default_window()\n", "# For a fast first run, you can narrow the window, e.g.:\n", "# start_date, end_date = \"2024-04-01\", \"2024-04-07\"\n", "\n", "df_raw = load_statcast(start_date, end_date, force=False)\n", "df_feat = engineer_pitch_features(df_raw)\n", "\n", "df_fit, scaler, km, nn = fit_kmeans(df_feat, k=8)\n", "cluster_names = xy_cluster_tags(df_fit)\n", "df_fit[\"cluster_name\"] = df_fit[\"cluster\"].map(cluster_names)\n", "\n", "# Save artifacts\n", "df_feat.to_parquet(ARTIFACTS_DIR / \"pitch_features.parquet\", index=False)\n", "df_fit.to_parquet(ARTIFACTS_DIR / \"pitch_features_clusters.parquet\", index=False)" ] }, { "cell_type": "code", "execution_count": 48, "id": "a8b637de", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Williams, Devin'" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pitchers = sorted(df_fit[\"player_name\"].unique().tolist())\n", "SELECTED_PITCHER = pitchers[1041] if pitchers else None\n", "SELECTED_PITCHER" ] }, { "cell_type": "code", "execution_count": 49, "id": "cbcd0c40", "metadata": 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player_namepitch_typep_throwsnvelospinivb_inhb_as_inrel_heightrel_sidecswwhiff_rategb_ratezone_pctclustercluster_name
4547Williams, DevinCHR63683.7240572734.992126-4.71849119.2267925.147233-2.5115090.3207550.3567250.2500000.2798741SL: Glove-Side • Moderate Sweep, Subtle Drop •...
4548Williams, DevinFCR689.2666672411.83333313.26-1.285.521667-2.380.5000000.6666670.0000000.3333331SL: Glove-Side • Moderate Sweep, Subtle Drop •...
4549Williams, DevinFFR58494.1239732365.51027415.68054812.8344525.391387-2.3465920.2893840.3333330.3859650.2482887FF: Arm-Side • Moderate Run, Moderate Ride • S...
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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "FF (R): Arm-Side, Ride. Velo 94.1 mph | AS 12.8\" | Ride 15.7\" | CSW 0.29 | Whiff 0.33 | GB 0.39 | Zone 0.25\n" ] } ], "source": [ "if SELECTED_PITCHER:\n", " df_p = df_fit[df_fit[\"player_name\"] == SELECTED_PITCHER].sort_values(\"pitch_type\")\n", " try:\n", " display(df_p)\n", " except NameError:\n", " print(df_p)\n", "\n", " try:\n", " display(movement_scatter_xy(df_p, color=\"pitch_type\"))\n", " except NameError:\n", " movement_scatter_xy(df_p, color=\"pitch_type\").show()\n", "\n", " for _, row in df_p.iterrows():\n", " try:\n", " display(radar_quality(row))\n", " except NameError:\n", " radar_quality(row).show()\n", " print(xy_blurb(row))\n", "else:\n", " print(\"No pitchers found. Try a different date window.\")" ] }, { "cell_type": "code", "execution_count": 50, "id": "b4349e0e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nearest comps — CH (SL: Glove-Side • Moderate Sweep, Subtle Drop • Whiff-First):\n" ] }, { "data": { "text/html": [ "
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player_namepitch_typep_throwsveloivb_inhb_as_inwhiff_rategb_ratecluster_name
4547Williams, DevinCHR83.724057-4.71849119.2267920.3567250.250000SL: Glove-Side • Moderate Sweep, Subtle Drop •...
4685Yoho, CraigCHR77.122892-2.94939817.9985540.2894740.181818CH: Arm-Side • Moderate Run, Moderate Ride • W...
3773Seabold, ConnorCHR81.337931-2.69586216.1213790.2592590.285714CH: Arm-Side • Moderate Run, Moderate Ride • W...
4530Whitlock, GarrettCHR84.3065130.17747117.0170110.3092110.361702CH: Arm-Side • Moderate Run, Moderate Ride • W...
1768Henderson, LoganCHR81.8672225.48866718.7006670.2528740.250000CH: Arm-Side • Moderate Run, Moderate Ride • W...
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4548Williams, DevinFCR89.26666713.26-1.280.6666670.0SL: Glove-Side • Moderate Sweep, Subtle Drop •...
3116Overton, ConnorFCR88.6857148.640.3771430.5000000.0SL: Glove-Side • Moderate Sweep, Subtle Drop •...
471Boyer, LoganFCR90.7416674.76-7.160.8000000.0SL: Glove-Side • Moderate Sweep, Subtle Drop •...
2360Liranzo, JesúsFCR86.061.68-5.1840.5000000.0SL: Glove-Side • Moderate Sweep, Subtle Drop •...
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player_namepitch_typep_throwsveloivb_inhb_as_inwhiff_rategb_ratecluster_name
4549Williams, DevinFFR94.12397315.68054812.8344520.3333330.385965FF: Arm-Side • Moderate Run, Moderate Ride • S...
4090Strzelecki, PeterFFR93.96060615.1163646.8836360.3157890.333333FF: Arm-Side • Moderate Run, Moderate Ride • S...
14Abreu, BryanFFR97.28225516.6294536.5170150.3445690.304348FF: Arm-Side • Moderate Run, Moderate Ride • S...
2620McCullers Jr., LanceFFR91.78840611.8069577.4904350.3750000.444444FF: Arm-Side • Moderate Run, Moderate Ride • S...
4506Wheeler, ZackFFR96.00818914.72913310.0023120.2701690.253731FF: Arm-Side • Moderate Run, Moderate Ride • S...
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" ], "text/plain": [ " player_name pitch_type p_throws velo ivb_in \\\n", "4549 Williams, Devin FF R 94.123973 15.680548 \n", "4090 Strzelecki, Peter FF R 93.960606 15.116364 \n", "14 Abreu, Bryan FF R 97.282255 16.629453 \n", "2620 McCullers Jr., Lance FF R 91.788406 11.806957 \n", "4506 Wheeler, Zack FF R 96.008189 14.729133 \n", "\n", " hb_as_in whiff_rate gb_rate \\\n", "4549 12.834452 0.333333 0.385965 \n", "4090 6.883636 0.315789 0.333333 \n", "14 6.517015 0.344569 0.304348 \n", "2620 7.490435 0.375000 0.444444 \n", "4506 10.002312 0.270169 0.253731 \n", "\n", " cluster_name \n", "4549 FF: Arm-Side • Moderate Run, Moderate Ride • S... \n", "4090 FF: Arm-Side • Moderate Run, Moderate Ride • S... \n", "14 FF: Arm-Side • Moderate Run, Moderate Ride • S... \n", "2620 FF: Arm-Side • Moderate Run, Moderate Ride • S... \n", "4506 FF: Arm-Side • Moderate Run, Moderate Ride • S... " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if SELECTED_PITCHER:\n", " for _, row in df_p.iterrows():\n", " comps = nearest_comps(row, df_fit, scaler, nn, within_pitch_type=True, k=6)\n", " print(f\"\\nNearest comps — {row['pitch_type']} ({row['cluster_name']}):\")\n", " try:\n", " display(comps)\n", " except NameError:\n", " print(comps.to_string(index=False))" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }