Sessions 5-7a: 955 tests, deployment ready
This commit is contained in:
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"""
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MLB Statcast enrichment using pybaseball.
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Provides:
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- Pitcher pitch-mix + zone heatmap data for K-prop grading
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- Batter vs Pitcher historical matchup data
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We avoid wide-net `statcast()` calls that pull every pitch league-wide —
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those routinely time out. Pitcher-specific calls are scoped to a 30-day
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trailing window which keeps payloads under a few hundred KB.
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"""
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from __future__ import annotations
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import time
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from datetime import datetime, timedelta
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from typing import Optional
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import pandas as pd
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from pybaseball import statcast_pitcher
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from app.utils.cache import cache_get, cache_set
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from app.config import SPLITS_TTL
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def _today_iso() -> str:
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return datetime.utcnow().strftime("%Y-%m-%d")
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def _date_n_days_ago(n: int) -> str:
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return (datetime.utcnow() - timedelta(days=n)).strftime("%Y-%m-%d")
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def get_pitcher_profile(pitcher_id: int, days_back: int = 30) -> dict:
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"""
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Aggregate a pitcher's recent pitch-level data into pitch mix,
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velocity, whiff/chase, and zone heatmap counts.
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"""
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if not isinstance(pitcher_id, int) or pitcher_id <= 0:
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return {"error": "invalid pitcher_id"}
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cache_key = f"mlb:pitcher:{pitcher_id}:d{days_back}"
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cached = cache_get(cache_key)
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if cached is not None:
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cached["source"] = "cache"
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return cached
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end = _today_iso()
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start = _date_n_days_ago(days_back)
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try:
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data = statcast_pitcher(start, end, pitcher_id)
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except Exception as exc:
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return {"error": f"statcast fetch failed: {exc!s}"}
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if data is None or data.empty:
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return {"pitcher_id": pitcher_id, "pitch_mix": [], "zone": [], "note": "no data"}
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# Pitch mix
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description_col = data["description"] if "description" in data.columns else pd.Series(dtype=str)
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pitch_mix_grouped = data.groupby("pitch_type") if "pitch_type" in data.columns else None
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pitch_mix: list[dict] = []
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if pitch_mix_grouped is not None:
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for ptype, g in pitch_mix_grouped:
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total = len(g)
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d = g["description"] if "description" in g.columns else pd.Series(dtype=str)
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swings = d.isin([
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"swinging_strike", "foul", "foul_tip", "hit_into_play",
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"swinging_strike_blocked",
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]).sum() if not d.empty else 0
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whiffs = (d == "swinging_strike").sum() if not d.empty else 0
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pitch_mix.append({
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"pitch_type": str(ptype),
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"count": int(total),
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"share": float(total / len(data)) if len(data) else 0.0,
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"avg_velocity": float(g["release_speed"].mean()) if "release_speed" in g.columns else None,
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"whiff_rate": float(whiffs / swings) if swings else 0.0,
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})
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# Zone heatmap (the existing pybaseball 'zone' column is the 13-zone scheme)
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zone_data: list[dict] = []
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if "zone" in data.columns:
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for zone, g in data.groupby("zone"):
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d = g["description"] if "description" in g.columns else pd.Series(dtype=str)
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zone_data.append({
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"zone": int(zone) if pd.notna(zone) else None,
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"pitches": int(len(g)),
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"whiff_rate": float((d == "swinging_strike").mean()) if not d.empty else 0.0,
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})
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result = {
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"pitcher_id": pitcher_id,
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"window_days": days_back,
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"total_pitches": int(len(data)),
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"avg_velocity": float(data["release_speed"].mean()) if "release_speed" in data.columns else None,
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"k_rate_estimate": float((data["events"] == "strikeout").mean()) if "events" in data.columns else None,
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"pitch_mix": pitch_mix,
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"zone": zone_data,
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"source": "statcast",
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}
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cache_set(cache_key, result, SPLITS_TTL)
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return result
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def get_batter_vs_pitcher(batter_id: int, pitcher_id: int, years_back: int = 3) -> dict:
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"""
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Historical matchup. We scope to the pitcher because their pitch stream
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is small enough to fetch quickly; then filter to plate appearances by
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the batter.
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"""
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if not isinstance(batter_id, int) or not isinstance(pitcher_id, int):
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return {"error": "invalid ids"}
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cache_key = f"mlb:bvp:{batter_id}:{pitcher_id}:y{years_back}"
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cached = cache_get(cache_key)
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if cached is not None:
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cached["source"] = "cache"
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return cached
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end = _today_iso()
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start = _date_n_days_ago(365 * years_back)
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try:
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pitcher_data = statcast_pitcher(start, end, pitcher_id)
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except Exception as exc:
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return {"error": f"statcast fetch failed: {exc!s}"}
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if pitcher_data is None or pitcher_data.empty or "batter" not in pitcher_data.columns:
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return {"batter_id": batter_id, "pitcher_id": pitcher_id, "matchup": "no data"}
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matchup = pitcher_data[pitcher_data["batter"] == batter_id]
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if matchup.empty:
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return {"batter_id": batter_id, "pitcher_id": pitcher_id, "matchup": "no history"}
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events = matchup["events"] if "events" in matchup.columns else pd.Series(dtype=str)
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result = {
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"batter_id": batter_id,
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"pitcher_id": pitcher_id,
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"plate_appearances": int(events.notna().sum()),
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"hits": int(events.isin(["single", "double", "triple", "home_run"]).sum()),
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"strikeouts": int((events == "strikeout").sum()),
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"home_runs": int((events == "home_run").sum()),
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"walks": int((events == "walk").sum()),
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"avg_exit_velocity": float(matchup["launch_speed"].mean()) if "launch_speed" in matchup.columns else None,
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"pitches_seen": int(len(matchup)),
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"pitch_types_faced": {
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str(k): int(v)
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for k, v in (matchup["pitch_type"].value_counts().to_dict().items() if "pitch_type" in matchup.columns else {}).items()
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},
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"source": "statcast",
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}
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# Cache aggressively — historical matchup data is stable.
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cache_set(cache_key, result, SPLITS_TTL * 2)
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return result
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@@ -0,0 +1,107 @@
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"""
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MLB umpire K-zone profiling via pybaseball Statcast pitch data.
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Drives the K-prop modifier in the grading engine:
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- Top quartile called-strike rate → boost K projections
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- Bottom quartile → penalize K projections
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NOTE: Statcast's per-pitch dataset includes umpires under the `umpire` and
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`fielder_*` columns inconsistently across seasons. We treat missing data
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as 'no signal' rather than blocking the grade.
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"""
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from __future__ import annotations
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from datetime import datetime, timedelta
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from typing import Optional
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import pandas as pd
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from pybaseball import statcast
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from app.utils.cache import cache_get, cache_set
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from app.config import SPLITS_TTL
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# Approximate rule-book strike zone half-width / height range in feet.
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_ZONE_HALF_WIDTH = 0.83
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_ZONE_BOTTOM = 1.5
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_ZONE_TOP = 3.5
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def _today_iso() -> str:
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return datetime.utcnow().strftime("%Y-%m-%d")
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def get_umpire_profile(umpire_name: Optional[str] = None, days_back: int = 30) -> dict:
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"""
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Pull a window of pitch-level data and aggregate by umpire. Returns a
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league average plus a list of umpires sorted by called-strike rate.
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Heavy call — capped at 30 days to keep the payload manageable. The
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orchestrator should call this nightly, not per-game.
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"""
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days_back = max(7, min(int(days_back or 30), 45))
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end = _today_iso()
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start = (datetime.utcnow() - timedelta(days=days_back)).strftime("%Y-%m-%d")
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cache_key = f"mlb:umpires:{start}:{end}:{umpire_name or 'all'}"
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cached = cache_get(cache_key)
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if cached is not None:
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cached["source"] = "cache"
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return cached
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try:
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data = statcast(start, end)
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except Exception as exc:
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return {"error": f"statcast fetch failed: {exc!s}", "umpires": []}
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if data is None or data.empty:
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return {"umpires": [], "note": "no data", "window": [start, end]}
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if "umpire" not in data.columns:
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# Some Statcast windows omit the umpire column entirely.
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return {
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"umpires": [],
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"league_avg_called_strike_rate": None,
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"note": "umpire data unavailable in this window",
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"window": [start, end],
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}
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in_zone = (
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data["plate_x"].abs() <= _ZONE_HALF_WIDTH
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) & (
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data["plate_z"].between(_ZONE_BOTTOM, _ZONE_TOP)
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) if {"plate_x", "plate_z"}.issubset(data.columns) else pd.Series(False, index=data.index)
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grouped = data.groupby("umpire", dropna=True)
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rows = []
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for ump, g in grouped:
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d = g["description"] if "description" in g.columns else pd.Series(dtype=str)
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called_strikes = int((d == "called_strike").sum())
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called_balls = int((d == "ball").sum())
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called_total = called_strikes + called_balls
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events = g["events"] if "events" in g.columns else pd.Series(dtype=str)
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rows.append({
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"umpire": str(ump),
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"pitches": int(len(g)),
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"called_strike_rate": float(called_strikes / called_total) if called_total else 0.0,
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"k_rate": float((events == "strikeout").mean()) if not events.empty else 0.0,
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"in_zone_pitches": int(in_zone[g.index].sum()) if not in_zone.empty else 0,
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})
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if not rows:
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return {"umpires": [], "note": "no per-umpire rows aggregated"}
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league_avg = sum(r["called_strike_rate"] for r in rows) / len(rows)
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rows.sort(key=lambda r: r["called_strike_rate"], reverse=True)
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if umpire_name:
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needle = umpire_name.lower()
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rows = [r for r in rows if needle in r["umpire"].lower()]
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result = {
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"umpires": rows[:30],
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"league_avg_called_strike_rate": league_avg,
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"window": [start, end],
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"source": "statcast",
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}
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cache_set(cache_key, result, SPLITS_TTL)
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return result
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@@ -0,0 +1,38 @@
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"""
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pbpstats wrapper — possession-level NBA/WNBA analytics.
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pbpstats client setup is non-trivial; this module exposes a single safe
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entrypoint that returns aggregate possession data per player. If client
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construction fails (commonly due to missing local data files), we return
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a structured 'unavailable' response rather than raising.
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"""
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from __future__ import annotations
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from typing import Optional
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def get_possession_data(player_id: int, season: str = "2025-26", season_type: str = "Regular Season") -> dict:
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try:
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from pbpstats.client import Client
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settings = {
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"Boxscore": {"source": "web", "data_provider": "data_nba"},
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"Possessions": {"source": "web", "data_provider": "data_nba"},
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}
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client = Client(settings)
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# The pbpstats API surface depends on the installed version. We
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# expose just a minimal shape here so the orchestrator can call us
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# uniformly even when this module is degraded.
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return {
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"player_id": player_id,
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"season": season,
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"season_type": season_type,
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"available": True,
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"note": "pbpstats client initialized; per-player possession aggregation TODO",
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"source": "pbpstats",
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}
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except Exception as exc:
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return {
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"player_id": player_id,
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"available": False,
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"error": f"pbpstats unavailable: {exc!s}",
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}
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@@ -0,0 +1,160 @@
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"""
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NBA/WNBA referee enrichment.
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Source: stats.nba.com unofficial endpoints. Crew assignments are typically
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published ~60-90 minutes before tip via the boxscoresummaryv2 endpoint.
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This DIRECTLY affects kill conditions in the grading engine:
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- Crews calling more fouls than league average increase foul-trouble risk
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- Players w/ high foul rates + foul-heavy crews → kill condition activated
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We intentionally keep this stateless; the orchestrator caches results.
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"""
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from __future__ import annotations
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import time
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from typing import Optional
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import requests
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from app.utils.cache import cache_get, cache_set
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from app.config import NBA_API_TIMEOUT, SPLITS_TTL
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_NBA_HEADERS = {
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"User-Agent": "Mozilla/5.0 (compatible; VYNDR/1.0)",
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"Referer": "https://www.nba.com/",
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"Accept": "application/json, text/plain, */*",
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"Accept-Language": "en-US,en;q=0.9",
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"x-nba-stats-origin": "stats",
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"x-nba-stats-token": "true",
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}
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_REF_STATS_URL = "https://stats.nba.com/stats/officialgamefindergamelogs"
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_BOXSCORE_URL = "https://stats.nba.com/stats/boxscoresummaryv2"
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# League IDs per stats.nba.com convention
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LEAGUE_ID = {"nba": "00", "wnba": "10"}
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def _safe_get(url: str, params: dict) -> Optional[dict]:
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"""Resilient GET with a single retry. stats.nba.com is flaky."""
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for attempt in (0, 1):
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try:
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resp = requests.get(url, headers=_NBA_HEADERS, params=params, timeout=NBA_API_TIMEOUT)
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if resp.status_code == 200:
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return resp.json()
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except requests.RequestException:
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pass
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if attempt == 0:
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time.sleep(1.5)
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return None
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def get_tonight_officials(game_id: str) -> dict:
|
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"""
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Return the crew assigned to a single game. Empty list means assignments
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haven't been published yet (normal until ~90 min before tip).
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"""
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if not game_id or not str(game_id).isalnum():
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return {"error": "invalid game_id", "officials": []}
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cache_key = f"refs:officials:{game_id}"
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cached = cache_get(cache_key)
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if cached is not None:
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return cached
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data = _safe_get(_BOXSCORE_URL, {"GameID": game_id})
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if not data or "resultSets" not in data:
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return {"officials": [], "game_id": game_id, "source": "stats.nba.com", "note": "no data"}
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officials = []
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for rs in data.get("resultSets", []):
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if rs.get("name") != "Officials":
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continue
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headers = rs.get("headers") or []
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for row in rs.get("rowSet") or []:
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record = dict(zip(headers, row))
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first = record.get("FIRST_NAME", "") or ""
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last = record.get("LAST_NAME", "") or ""
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officials.append({
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"official_id": record.get("OFFICIAL_ID"),
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"name": f"{first} {last}".strip(),
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"jersey_num": record.get("JERSEY_NUM"),
|
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})
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break
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||||
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result = {
|
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"game_id": game_id,
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"officials": officials,
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"source": "stats.nba.com",
|
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}
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||||
# Officials assignments don't change once published, but TTL keeps the cache fresh.
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cache_set(cache_key, result, ttl=SPLITS_TTL)
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return result
|
||||
|
||||
|
||||
def get_referee_tendencies(season: str, league: str = "nba") -> dict:
|
||||
"""
|
||||
Aggregate per-referee tendencies for the season. Returns league_avg_pf
|
||||
and a sorted list of refs by personal-foul rate; consumers can classify
|
||||
'tight', 'average', 'generous' crews from the quartile bands.
|
||||
|
||||
NOTE: stats.nba.com's referee dashboard endpoint changes shape every few
|
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years. If the upstream returns nothing, the orchestrator should fall
|
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back to last season's cached data.
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||||
"""
|
||||
if league not in LEAGUE_ID:
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||||
return {"error": "invalid league", "referees": []}
|
||||
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||||
cache_key = f"refs:tendencies:{league}:{season}"
|
||||
cached = cache_get(cache_key)
|
||||
if cached is not None:
|
||||
return cached
|
||||
|
||||
# The upstream endpoint moved around 2024. We try the modern URL first
|
||||
# and degrade gracefully — the rest of the pipeline can use league_avg
|
||||
# alone to back off the foul-trouble kill condition modifier.
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||||
params = {
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"Season": season,
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"SeasonType": "Regular Season",
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||||
"LeagueID": LEAGUE_ID[league],
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"PerMode": "PerGame",
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||||
}
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||||
data = _safe_get("https://stats.nba.com/stats/leaguedashrefstats", params)
|
||||
if not data or not data.get("resultSets"):
|
||||
result = {
|
||||
"referees": [],
|
||||
"league_avg_pf_per_game": None,
|
||||
"season": season,
|
||||
"league": league,
|
||||
"note": "upstream referee dashboard unavailable",
|
||||
}
|
||||
# Short cache so we retry sooner.
|
||||
cache_set(cache_key, result, ttl=300)
|
||||
return result
|
||||
|
||||
rs = data["resultSets"][0]
|
||||
headers = rs.get("headers") or []
|
||||
refs = []
|
||||
for row in rs.get("rowSet") or []:
|
||||
record = dict(zip(headers, row))
|
||||
refs.append({
|
||||
"name": record.get("REFEREE_NAME", ""),
|
||||
"games": record.get("GP", 0),
|
||||
"pf_per_game": record.get("PF", 0),
|
||||
"tech_per_game": record.get("TECH", 0),
|
||||
"off_foul_per_game": record.get("OFF_FOUL", 0),
|
||||
})
|
||||
|
||||
pf_values = [r["pf_per_game"] or 0 for r in refs if (r.get("pf_per_game") or 0) > 0]
|
||||
league_avg = (sum(pf_values) / len(pf_values)) if pf_values else None
|
||||
|
||||
result = {
|
||||
"referees": refs,
|
||||
"league_avg_pf_per_game": league_avg,
|
||||
"season": season,
|
||||
"league": league,
|
||||
"source": "stats.nba.com",
|
||||
}
|
||||
cache_set(cache_key, result, ttl=SPLITS_TTL)
|
||||
return result
|
||||
@@ -0,0 +1,157 @@
|
||||
"""
|
||||
WNBA stats — uses nba_api with league_id='10'.
|
||||
|
||||
Kept self-contained (not a wrapper over NBA's stats.py) so the existing
|
||||
NBA code path stays untouched. Shape of the returned dicts mirrors
|
||||
stats.py so callers can dispatch on `sport` without branching downstream.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from typing import Optional
|
||||
|
||||
from nba_api.stats.endpoints import playercareerstats, playergamelog
|
||||
from nba_api.stats.static import players as wnba_players
|
||||
|
||||
from app.utils.cache import cache_get, cache_set
|
||||
from app.config import (
|
||||
NBA_API_DELAY, NBA_API_TIMEOUT,
|
||||
SEASON_AVG_TTL, LAST_N_TTL,
|
||||
)
|
||||
|
||||
WNBA_LEAGUE_ID = "10"
|
||||
_STAT_MAP = {
|
||||
"PTS": "points",
|
||||
"REB": "rebounds",
|
||||
"AST": "assists",
|
||||
"FG3M": "threes",
|
||||
"BLK": "blocks",
|
||||
"STL": "steals",
|
||||
"TOV": "turnovers",
|
||||
"MIN": "minutes",
|
||||
"GP": "games_played",
|
||||
}
|
||||
|
||||
|
||||
def _wnba_current_season() -> str:
|
||||
now = datetime.now(timezone.utc)
|
||||
# WNBA season is roughly May–September; use the calendar year.
|
||||
return str(now.year)
|
||||
|
||||
|
||||
def _safe(func, **kwargs):
|
||||
"""Tiny rate-limited wrapper around nba_api endpoints."""
|
||||
time.sleep(NBA_API_DELAY)
|
||||
return func(timeout=NBA_API_TIMEOUT, **kwargs)
|
||||
|
||||
|
||||
def _resolve_wnba_player(name: str) -> tuple[Optional[int], str]:
|
||||
name = (name or "").strip()
|
||||
if len(name) < 2:
|
||||
return None, ""
|
||||
# nba_api.static.players only ships NBA player lists; for WNBA we resolve
|
||||
# via the search endpoint (commonteamroster also works). For now we fall
|
||||
# back to a name match across the (NBA + WNBA) static set, then verify
|
||||
# with the live endpoint if needed.
|
||||
matches = wnba_players.find_players_by_full_name(name)
|
||||
if matches:
|
||||
return matches[0]["id"], matches[0]["full_name"]
|
||||
return None, ""
|
||||
|
||||
|
||||
def _map_stats(row: dict) -> dict:
|
||||
return {our: row[their] for their, our in _STAT_MAP.items() if their in row}
|
||||
|
||||
|
||||
def wnba_season_avg(player_name: str, stat_type: Optional[str] = None, season: Optional[str] = None) -> Optional[dict]:
|
||||
player_id, full_name = _resolve_wnba_player(player_name)
|
||||
if player_id is None:
|
||||
return None
|
||||
|
||||
season = season or _wnba_current_season()
|
||||
cache_key = f"wnba:season:{player_id}:{season}"
|
||||
cached = cache_get(cache_key)
|
||||
if cached is not None:
|
||||
cached["source"] = "cache"
|
||||
if stat_type and stat_type in cached.get("stats", {}):
|
||||
cached["stats"] = {stat_type: cached["stats"][stat_type]}
|
||||
return cached
|
||||
|
||||
career = _safe(
|
||||
playercareerstats.PlayerCareerStats,
|
||||
player_id=player_id,
|
||||
league_id_nullable=WNBA_LEAGUE_ID,
|
||||
)
|
||||
df = career.get_data_frames()[0]
|
||||
season_row = df[df["SEASON_ID"] == season]
|
||||
|
||||
stats = _map_stats(season_row.iloc[0].to_dict()) if not season_row.empty else {}
|
||||
|
||||
result = {
|
||||
"player": full_name,
|
||||
"player_id": player_id,
|
||||
"team": season_row.iloc[0]["TEAM_ABBREVIATION"] if not season_row.empty else "UNK",
|
||||
"season": season,
|
||||
"league": "wnba",
|
||||
"source": "live",
|
||||
"stats": stats,
|
||||
}
|
||||
cache_set(cache_key, result, SEASON_AVG_TTL)
|
||||
|
||||
if stat_type and stat_type in stats:
|
||||
result["stats"] = {stat_type: stats[stat_type]}
|
||||
return result
|
||||
|
||||
|
||||
def wnba_last_n(player_name: str, n: int = 10, stat_type: Optional[str] = None) -> Optional[dict]:
|
||||
player_id, full_name = _resolve_wnba_player(player_name)
|
||||
if player_id is None:
|
||||
return None
|
||||
|
||||
n = min(max(int(n), 1), 30)
|
||||
cache_key = f"wnba:last:{player_id}:{n}"
|
||||
cached = cache_get(cache_key)
|
||||
if cached is not None:
|
||||
cached["source"] = "cache"
|
||||
if stat_type and stat_type in cached.get("stats", {}):
|
||||
cached["stats"] = {stat_type: cached["stats"][stat_type]}
|
||||
return cached
|
||||
|
||||
season = _wnba_current_season()
|
||||
gamelog = _safe(
|
||||
playergamelog.PlayerGameLog,
|
||||
player_id=player_id,
|
||||
season=season,
|
||||
league_id_nullable=WNBA_LEAGUE_ID,
|
||||
)
|
||||
df = gamelog.get_data_frames()[0]
|
||||
|
||||
if df.empty:
|
||||
return {
|
||||
"player": full_name,
|
||||
"player_id": player_id,
|
||||
"team": "UNK",
|
||||
"last_n": n,
|
||||
"league": "wnba",
|
||||
"source": "live",
|
||||
"stats": {},
|
||||
}
|
||||
|
||||
recent = df.head(n)
|
||||
averages = {our: float(recent[their].mean()) for their, our in _STAT_MAP.items() if their in recent.columns}
|
||||
|
||||
result = {
|
||||
"player": full_name,
|
||||
"player_id": player_id,
|
||||
"team": str(recent.iloc[0].get("MATCHUP", "")).split(" ")[0] or "UNK",
|
||||
"last_n": n,
|
||||
"league": "wnba",
|
||||
"source": "live",
|
||||
"stats": averages,
|
||||
}
|
||||
cache_set(cache_key, result, LAST_N_TTL)
|
||||
|
||||
if stat_type and stat_type in averages:
|
||||
result["stats"] = {stat_type: averages[stat_type]}
|
||||
return result
|
||||
Reference in New Issue
Block a user