Convert scripts to use Typer and add requirements.txt

- Replaced argparse with Typer for CLI argument parsing in both scripts
- Updated function signatures and calls accordingly in build_season_schedule.py and compute_ratings.py
- Added requirements.txt listing dependencies including typer[all], pandas, numpy, etc.

build_season_schedule.py

@@ -291,7 +291,6 @@ def main(
     def match_opponent(text: str) -> Optional[TeamRec]:
         return best_match_team(text, by_slug, by_norm)
 
-    # Group by game_id if available; otherwise fallback on (date + unordered pair + raw score text if present)
     buckets: Dict[str, dict] = {}
     fallback_rows = 0
 
@@ -336,7 +335,6 @@ def main(
     if fallback_rows:
         logging.info(f"Used fallback dedupe for {fallback_rows} rows without game_id.")
 
-    # Merge perspectives into a single home/away row
     out_rows = []
     time_cache: Dict[str, Optional[str]] = {}
 
@@ -345,11 +343,9 @@ def main(
         date = p[0]["date"]
         game_id = bucket.get("game_id", "")
 
-        # Identify home/away perspectives
         p_home = next((x for x in p if x["is_away"] is False), None)
         p_away = next((x for x in p if x["is_away"] is True), None)
 
-        # Team identities
         home_team = (p_home["team"] if p_home else (p_away["opp"] if p_away else None))
         away_team = (p_away["team"] if p_away else (p_home["opp"] if p_home else None))
 
@@ -358,7 +354,6 @@ def main(
                 return rec.slug, rec.instance_id, rec.team_id, rec.name
             return fallback_slug, "", "", fallback_slug.replace("-", " ").title()
 
-        # Prefer runs from the explicit perspective (home if available; otherwise away)
         home_runs = away_runs = None
         if p_home and isinstance(p_home["team_runs"], int) and isinstance(p_home["opp_runs"], int):
             home_runs = p_home["team_runs"]
@@ -367,7 +362,6 @@ def main(
             away_runs = p_away["team_runs"]
             home_runs = p_away["opp_runs"]
 
-        # Fallback: single perspective present but numbers known → place by is_away
         if (home_runs is None or away_runs is None) and p:
             one = p[0]
             if isinstance(one["team_runs"], int) and isinstance(one["opp_runs"], int):
@@ -378,7 +372,6 @@ def main(
                     home_runs = one["team_runs"]; away_runs = one["opp_runs"]
                     home_team = one["team"]; away_team = one["opp"] if one["opp"] else away_team
 
-        # Pack final team identifiers (fallback slug = guess from perspectives)
         guess_home_fallback = (p_home["team"].slug if p_home and p_home["team"] else
                                p_away["opp"].slug if p_away and p_away["opp"] else
                                p[0]["pair"][0])
@@ -389,7 +382,6 @@ def main(
         home_slug, home_inst, home_id, home_name = pack_team(home_team, guess_home_fallback)
         away_slug, away_inst, away_id, away_name = pack_team(away_team, guess_away_fallback)
 
-        # Winner/loser
         winner_slug = winner_inst = winner_id = loser_slug = loser_inst = loser_id = ""
         if isinstance(home_runs, int) and isinstance(away_runs, int):
             if home_runs > away_runs:
@@ -399,12 +391,10 @@ def main(
                 winner_slug, winner_inst, winner_id = away_slug, away_inst, away_id
                 loser_slug, loser_inst, loser_id = home_slug, home_inst, home_id
 
-        # Meta from perspectives
         loc = (p_home["location"] if p_home else "") or (p_away["location"] if p_away else "")
         status = (p_home["status"] if p_home else "") or (p_away["status"] if p_away else "")
         source_urls = sorted({x["source_url"] for x in p})
 
-        # -------- NEW: fetch game start time from game page --------
         time_local = ""
         if fetch_time and game_id:
             if game_id in time_cache:

compute_ratings.py

@@ -141,7 +141,6 @@ def elo_once(df: pd.DataFrame, K: float, H: float, mcap: float, init: dict[str,f
 def elo(df: pd.DataFrame, K=24.0, H=30.0, mcap=2.0, shuffles=20, seed=42) -> pd.Series:
     teams = sorted(set(df["HomeTeam"]).union(df["AwayTeam"]))
     base = {t: 1500.0 for t in teams}
-    # baseline in chronological order (Date may be NaT; sort is stable)
     df0 = df.sort_values(["Date"]).reset_index(drop=True)
     r_first = elo_once(df0, K, H, mcap, base)
     rng = np.random.default_rng(seed)

requirements.txt

@@ -1,5 +1,5 @@
-typer[all]==0.16.1
-pandas==2.3.2
-numpy==2.3.2
-beautifulsoup4==4.13.5
-requests==2.32.5
+typer[all]
+pandas
+numpy
+beautifulsoup4
+requests