✨ feat(cli): migrate build_season_schedule and compute_ratings to typer CLI
- add typer-based CLI to build_season_schedule.py for structured option handling - refactor compute_ratings.py to remove argparse and support typer CLI - improve typing and option descriptions in compute_ratings.py main function - add .gitignore entry for __pycache__ - add requirements.txt with dependencies for the project
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3
.gitignore
vendored
3
.gitignore
vendored
@@ -1,2 +1,3 @@
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/*.csv
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/*.numbers
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/*.numbers
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**/__pycache__
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@@ -30,6 +30,7 @@ from urllib.parse import urlencode
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import requests
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from bs4 import BeautifulSoup
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from dateutil import parser as dtp
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import typer
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# ----------------- logging -----------------
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logging.basicConfig(
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@@ -264,16 +265,14 @@ def fetch_game_time(game_id: str, session: requests.Session) -> Optional[str]:
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return None
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# ----------------- build & merge -----------------
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def main():
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ap = argparse.ArgumentParser(description="Build a deduped season schedule with IDs, winners/losers, runs, and times.")
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ap.add_argument("--subseason", required=True, help="Subseason ID, e.g. 942425")
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ap.add_argument("--teams", required=True, help="Path to teams.json (array with team_id, team_slug, instance_id, teamName)")
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ap.add_argument("--out", default="season_schedule.csv", help="Output CSV path")
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ap.add_argument("--fetch-time", action="store_true", help="Fetch game time from /game/show/<id>")
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ap.add_argument("--sleep", type=float, default=0.35, help="Delay between requests (seconds)")
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args = ap.parse_args()
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by_instance, by_slug, by_norm = load_teams(args.teams)
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def main(
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subseason: str = typer.Option(..., help="Subseason ID, e.g. 942425"),
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teams: str = typer.Option(..., help="Path to teams.json (array with team_id, team_slug, instance_id, teamName)"),
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out: str = typer.Option("season_schedule.csv", help="Output CSV path"),
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fetch_time: bool = typer.Option(False, help="Fetch game time from /game/show/<id>"),
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sleep: float = typer.Option(0.35, help="Delay between requests (seconds)")
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):
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by_instance, by_slug, by_norm = load_teams(teams)
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instance_ids = sorted(by_instance.keys())
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session = requests.Session()
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@@ -283,8 +282,8 @@ def main():
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raw: List[dict] = []
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for i, iid in enumerate(instance_ids, 1):
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logging.info(f"[{i}/{len(instance_ids)}] Fetching schedule for instance {iid}")
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raw.extend(parse_printable(iid, args.subseason, session=session))
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time.sleep(args.sleep) # be polite
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raw.extend(parse_printable(iid, subseason, session=session))
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time.sleep(sleep) # be polite
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def rec_from_instance(iid: str) -> Optional[TeamRec]:
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return by_instance.get(iid)
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@@ -407,7 +406,7 @@ def main():
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# -------- NEW: fetch game start time from game page --------
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time_local = ""
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if args.fetch_time and game_id:
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if fetch_time and game_id:
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if game_id in time_cache:
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tval = time_cache[game_id]
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else:
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@@ -415,8 +414,7 @@ def main():
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tval = fetch_game_time(game_id, session=session)
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time_cache[game_id] = tval
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if tval is None:
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# small backoff to be nice if many misses
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time.sleep(min(args.sleep * 2, 1.0))
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time.sleep(min(sleep * 2, 1.0))
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if tval:
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time_local = tval
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@@ -452,13 +450,13 @@ def main():
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"loser_slug","loser_instance","loser_id",
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"location","status","game_id","source_urls",
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]
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with open(args.out, "w", newline="", encoding="utf-8") as f:
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with open(out, "w", newline="", encoding="utf-8") as f:
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w = csv.DictWriter(f, fieldnames=fieldnames)
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w.writeheader()
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for r in out_rows:
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w.writerow(r)
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logging.info(f"Wrote {len(out_rows)} games → {args.out}")
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logging.info(f"Wrote {len(out_rows)} games → {out}")
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if __name__ == "__main__":
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main()
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typer.run(main)
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@@ -19,43 +19,27 @@ Defaults:
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"""
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from __future__ import annotations
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import argparse
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import math
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import numpy as np
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import pandas as pd
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import typer
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def parse_args():
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p = argparse.ArgumentParser(description="Power ratings from season_schedule.csv")
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p.add_argument("--in", dest="inp", required=True, help="Input CSV (season_schedule.csv)")
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p.add_argument("--out", dest="out", required=True, help="Output ratings CSV")
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p.add_argument("--team-id", choices=["names","slugs"], default="names",
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help="Use team names or slugs as identifiers (default: names)")
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p.add_argument("--final-status", default=None,
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help="Only include games where status == this value (e.g., 'final'). If omitted, any row with scores is included.")
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# Tunables
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p.add_argument("--pyexp", type=float, default=1.83, help="Pythagorean exponent")
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p.add_argument("--massey-cap", type=float, default=8.0, help="Cap for run margins in Massey")
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p.add_argument("--no-massey-home-adj", action="store_true",
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help="Disable subtracting estimated home-field runs in Massey")
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p.add_argument("--elo-k", type=float, default=24.0, help="Elo K-factor")
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p.add_argument("--elo-home", type=float, default=30.0, help="Elo home bonus (points)")
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p.add_argument("--elo-mcap", type=float, default=2.0, help="Cap for margin factor ln(|m|+1)")
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p.add_argument("--elo-shuffles", type=int, default=20, help="Random shuffles to average Elo")
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p.add_argument("--elo-seed", type=int, default=42, help="RNG seed for shuffles")
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return p.parse_args()
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def load_games(a) -> pd.DataFrame:
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df = pd.read_csv(a.inp)
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def load_games(
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inp: str,
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team_id: str = "names",
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final_status: str | None = None,
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) -> pd.DataFrame:
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df = pd.read_csv(inp)
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# Choose identifiers
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home_id_col = "home_name" if a.team_id == "names" else "home_slug"
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away_id_col = "away_name" if a.team_id == "names" else "away_slug"
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home_id_col = "home_name" if team_id == "names" else "home_slug"
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away_id_col = "away_name" if team_id == "names" else "away_slug"
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for c in [home_id_col, away_id_col, "home_runs", "away_runs"]:
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if c not in df.columns:
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raise ValueError(f"Missing required column: {c}")
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# Optional status filter (helps exclude postponed/canceled)
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if a.final_status is not None and "status" in df.columns:
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df = df[df["status"].astype(str).str.lower() == str(a.final_status).lower()]
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if final_status is not None and "status" in df.columns:
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df = df[df["status"].astype(str).str.lower() == str(final_status).lower()]
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# Keep only games with numeric scores
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df = df.copy()
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@@ -173,52 +157,71 @@ def zscore(s: pd.Series) -> pd.Series:
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mu, sd = s.mean(), s.std(ddof=0)
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return pd.Series(0.0, index=s.index) if (sd == 0 or np.isnan(sd)) else (s - mu) / sd
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def main():
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a = parse_args()
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games = load_games(a)
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def main(
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inp: str = typer.Option(..., help="Input CSV (season_schedule.csv)"),
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out: str = typer.Option(..., help="Output ratings CSV"),
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team_id: str = typer.Option(
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"names",
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help="Use team names or slugs as identifiers (default: names)",
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show_default=True,
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case_sensitive=False,
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prompt=False,
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),
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final_status: str | None = typer.Option(None, help="Only include games where status == this value (e.g., 'final'). If omitted, any row with scores is included."),
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pyexp: float = typer.Option(1.83, help="Pythagorean exponent"),
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massey_cap: float = typer.Option(8.0, help="Cap for run margins in Massey"),
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no_massey_home_adj: bool = typer.Option(False, help="Disable subtracting estimated home-field runs in Massey"),
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elo_k: float = typer.Option(24.0, help="Elo K-factor"),
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elo_home: float = typer.Option(30.0, help="Elo home bonus (points)"),
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elo_mcap: float = typer.Option(2.0, help="Cap for margin factor ln(|m|+1)"),
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elo_shuffles: int = typer.Option(20, help="Random shuffles to average Elo"),
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elo_seed: int = typer.Option(42, help="RNG seed for shuffles")
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):
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team_id = team_id.lower()
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# Load games
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games = load_games(inp, team_id=team_id, final_status=final_status)
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# Aggregates
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team = aggregate_team_stats(games)
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team["PythagoreanWinPct"] = pythagorean(team["RS"], team["RA"], a.pyexp)
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team["PythagoreanWinPct"] = pythagorean(team["RS"], team["RA"], pyexp)
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# Ratings
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massey_r, h_runs = massey(games, cap=a.massey_cap, subtract_home=(not a.no_massey_home_adj))
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sos = (
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games.assign(OppTeam=np.where(True, games["AwayTeam"], games["AwayTeam"])) # placeholder
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)
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# Strength of schedule: avg opponent Massey rating faced
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massey_r, h_runs = massey(games, cap=massey_cap, subtract_home=not no_massey_home_adj)
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# Strength of schedule
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opps = {t: [] for t in massey_r.index}
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for _, r in games.iterrows():
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opps[r["HomeTeam"]].append(r["AwayTeam"])
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opps[r["AwayTeam"]].append(r["HomeTeam"])
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sos_series = pd.Series({t: (float(massey_r[opps[t]].mean()) if opps[t] else 0.0) for t in opps})
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elo_r = elo(games, K=a.elo_k, H=a.elo_home, mcap=a.elo_mcap, shuffles=a.elo_shuffles, seed=a.elo_seed)
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elo_r = elo(games, K=elo_k, H=elo_home, mcap=elo_mcap, shuffles=elo_shuffles, seed=elo_seed)
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# Merge
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out = team.set_index("Team")
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out["MasseyRating"] = massey_r
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out["EloRating"] = elo_r
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out["StrengthOfSchedule"] = sos_series
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out_df = team.set_index("Team")
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out_df["MasseyRating"] = massey_r
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out_df["EloRating"] = elo_r
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out_df["StrengthOfSchedule"] = sos_series
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# Composite
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Z_r, Z_e, Z_p = zscore(out["MasseyRating"]), zscore(out["EloRating"]), zscore(out["PythagoreanWinPct"])
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out["CompositeRating"] = 0.45*Z_r + 0.35*Z_e + 0.20*Z_p
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Z_r, Z_e, Z_p = zscore(out_df["MasseyRating"]), zscore(out_df["EloRating"]), zscore(out_df["PythagoreanWinPct"])
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out_df["CompositeRating"] = 0.45*Z_r + 0.35*Z_e + 0.20*Z_p
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out = out.reset_index()
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out = out[[
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out_df = out_df.reset_index()
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out_df = out_df[[
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"Team","GP","W","L","T","WinPct","RS","RA","RunDiff",
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"PythagoreanWinPct","MasseyRating","EloRating","StrengthOfSchedule","CompositeRating"
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]].sort_values("CompositeRating", ascending=False)
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# Round for readability
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for c in ["WinPct","PythagoreanWinPct","MasseyRating","EloRating","StrengthOfSchedule","CompositeRating"]:
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out[c] = out[c].astype(float).round(5)
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out_df[c] = out_df[c].astype(float).round(5)
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out.to_csv(a.out, index=False)
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out_df.to_csv(out, index=False)
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print(f"Done. Estimated home-field (runs) used in Massey: {h_runs:.3f}")
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print(f"Teams ranked: {len(out)} | Games processed: {len(games)}")
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print(f"Output -> {a.out}")
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print(f"Teams ranked: {len(out_df)} | Games processed: {len(games)}")
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print(f"Output -> {out}")
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if __name__ == "__main__":
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main()
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typer.run(main)
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5
requirements.txt
Normal file
5
requirements.txt
Normal file
@@ -0,0 +1,5 @@
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typer[all]==0.16.1
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pandas==2.3.2
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numpy==2.3.2
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beautifulsoup4==4.13.5
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requests==2.32.5
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