feat: game importance factor in hype scoring

Adds calculate_game_importance() that boosts Priority for high-stakes
regular-season matchups based on season progress (sharp ramp after game
55), playoff bubble proximity (wildcard rank ~17-19 = max relevance),
and divisional/conference rivalry (1.4x/1.2x multipliers). Max bonus
150 pts applied to both LIVE and PRE games; playoff and FINAL games
are unaffected. Extends standings schema with division, conference,
games_played, and wildcard_sequence fields fetched from the NHL API.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/games.py

@@ -52,7 +52,7 @@ def parse_games(scoreboard_data):
                 "Intermission": game["clock"]["inIntermission"]
                 if game_state == "LIVE"
                 else "N/A",
-                "Priority": calculate_game_priority(game) + get_comeback_bonus(game),
+                "Priority": calculate_game_priority(game) + get_comeback_bonus(game) + calculate_game_importance(game),
                 "Start Time": get_start_time(game),
                 "Home Record": format_record(game["homeTeam"]["record"])
                 if game["gameState"] in ["PRE", "FUT"]
@@ -290,20 +290,94 @@ def get_team_standings(team_name):
     cursor = conn.cursor()
     cursor.execute(
         """
-        SELECT league_sequence, league_l10_sequence
+        SELECT league_sequence, league_l10_sequence,
+               division_abbrev, conference_abbrev,
+               games_played, wildcard_sequence
         FROM standings
         WHERE team_common_name = ?
-    """,
+        """,
         (team_name,),
     )
     result = cursor.fetchone()
     conn.close()
+    if result:
+        return {
+            "league_sequence": result[0],
+            "league_l10_sequence": result[1],
+            "division_abbrev": result[2],
+            "conference_abbrev": result[3],
+            "games_played": result[4],
+            "wildcard_sequence": result[5],
+        }
     return {
-        "league_sequence": result[0] if result else 0,
-        "league_l10_sequence": result[1] if result else 0,
+        "league_sequence": 0,
+        "league_l10_sequence": 0,
+        "division_abbrev": None,
+        "conference_abbrev": None,
+        "games_played": 0,
+        "wildcard_sequence": 32,
     }
 
 
+def calculate_game_importance(game):
+    # Playoff games already have elevated priorities; don't double-count
+    if game.get("gameType", 2) != 2:
+        return 0
+    # FINAL/OFF games must sort below LIVE and PRE games
+    if game["gameState"] in ("FINAL", "OFF"):
+        return 0
+
+    home_st = get_team_standings(game["homeTeam"]["name"]["default"])
+    away_st = get_team_standings(game["awayTeam"]["name"]["default"])
+
+    # Season weight — near-zero before game 30, sharp ramp 55-70, max at 82
+    avg_gp = (home_st["games_played"] + away_st["games_played"]) / 2
+    if avg_gp <= 30:
+        season_weight = 0.05
+    else:
+        t = (avg_gp - 30) / (82 - 30)
+        season_weight = min(t ** 1.8, 1.0)
+
+    # Playoff relevance — peaks for bubble teams (wildcard rank ~17-19)
+    best_wc = min(home_st["wildcard_sequence"] or 32, away_st["wildcard_sequence"] or 32)
+    if best_wc <= 12:
+        playoff_relevance = 0.60
+    elif best_wc <= 16:
+        playoff_relevance = 0.85
+    elif best_wc <= 19:
+        playoff_relevance = 1.00
+    elif best_wc <= 23:
+        playoff_relevance = 0.65
+    else:
+        playoff_relevance = 0.15
+
+    # Division/conference rivalry multiplier
+    home_div = home_st["division_abbrev"]
+    away_div = away_st["division_abbrev"]
+    home_conf = home_st["conference_abbrev"]
+    away_conf = away_st["conference_abbrev"]
+    if home_div and away_div and home_div == away_div:
+        rivalry_multiplier = 1.4
+    elif home_conf and away_conf and home_conf == away_conf:
+        rivalry_multiplier = 1.2
+    else:
+        rivalry_multiplier = 1.0
+
+    raw = season_weight * playoff_relevance * rivalry_multiplier
+    importance = int((raw / 1.4) * 150)
+
+    logger.debug(
+        "importance components — season_weight: %.3f, playoff_relevance: %.2f, "
+        "rivalry: %.1f, importance: %s",
+        season_weight,
+        playoff_relevance,
+        rivalry_multiplier,
+        importance,
+    )
+
+    return max(0, min(importance, 150))
+
+
 def utc_to_eastern(utc_time):
     utc_datetime = datetime.strptime(utc_time, "%Y-%m-%dT%H:%M:%SZ")
     eastern_datetime = utc_datetime.replace(tzinfo=timezone.utc).astimezone(EASTERN)