Add HOT DOG indicator and its pond, backtested 2023-2025

backend/hot_dog.py compares the sportsbook underdog's last 5 games with the
favorite's (reaching into last season) on win %, points allowed, 3rd-down %,
giveaways per drive, and the heat map's defense rank. A certified Hot Dog
wins 3+ of the 5 and has a winning record.

scripts/backtest_hot_dogs.py writes hot_dog_history.csv (every underdog
game, 732) and hot_dog_backtest.csv. Certified dogs (86): 39.5% outright
vs 31.1% for all dogs, 50.0% ATS vs 48.3%. Chosen bet: spread (50.0%).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Az2f2NZQuPaekELAJsfFG8

backend/hot_dog.py

@@ -0,0 +1,162 @@
+"""
+HOT DOG INDICATOR -- is this underdog dangerous?
+
+The underdog is the sportsbook underdog (vig-removed moneyline below 50%).
+Its last 5 games before the matchup are compared with the favorite's last 5
+(reaching into last season when this one has fewer), on five stats:
+
+  win %                 higher wins
+  opponent scoring      points allowed per game, lower wins
+  3rd-down %            conversions / attempts, higher wins
+  turnover %            (INTs + fumbles lost) / drives, lower wins
+  defense rank          the heat map's defensive profile ranked 1-32, lower wins
+
+A CERTIFIED HOT DOG wins at least 3 of the 5 AND has a winning record (W > L).
+A team without 5 prior games in the pond (2023+) can't be certified.
+
+scripts/backtest_hot_dogs.py runs this rule over 2023-2025 (hot_dog_history.csv
+is the pond, hot_dog_backtest.csv the results) and picks the bet -- moneyline
+or spread -- that certified dogs hit more often.
+"""
+from __future__ import annotations
+
+from functools import lru_cache
+from statistics import mean
+
+import data
+import matchup
+
+WINDOW = 5
+MIN_STATS_WON = 3
+
+# (key, label, better) -- better is +1 when higher is better for the team, -1 when lower is.
+STATS = [
+    ("win_pct", "Win %", 1),
+    ("opp_ppg", "Opp points / game", -1),
+    ("third_down_pct", "3rd-down %", 1),
+    ("turnover_pct", "Turnovers / drive", -1),
+    ("def_rank", "Defense rank", -1),
+]
+DEF_PROFILE = ["opp_ppg", "opp_tds_pg", "opp_rz_td_pct", "opp_rush_ypg", "opp_rush_tds_pg",
+               "opp_pass_ypg", "opp_pass_tds_pg", "opp_comp_pg"]
+
+
+def window_stats(rows: list[dict], team: str, season: int, week: int, n: int = WINDOW) -> dict | None:
+    """The team's last n games strictly before (season, week), across seasons in the pond."""
+    prior = sorted((g for g in rows if g["team"] == team and (g["season"], g["week"]) < (season, week)),
+                   key=lambda g: (g["season"], g["week"]))[-n:]
+    if len(prior) < n:
+        return None
+    att = sum(g["third_down_att"] for g in prior)
+    drives = sum(g["drives"] for g in prior)
+    return {
+        "win_pct": sum(g["win"] for g in prior) / n,
+        "opp_ppg": sum(g["points_against"] for g in prior) / n,
+        "third_down_pct": sum(g["third_down_conv"] for g in prior) / att if att else 0.0,
+        "turnover_pct": sum(g["ints_thrown"] + g["fumbles_lost"] for g in prior) / drives if drives else 0.0,
+    }
+
+
+def defense_ranks(z: dict[str, dict[str, float]]) -> dict[str, int]:
+    """1 = best defense: the heat map's defensive z-scores, negated so allowing less ranks higher."""
+    composite = {t: -mean(z[t][s] for s in DEF_PROFILE) for t in z}
+    ordered = sorted(composite, key=composite.get, reverse=True)
+    return {t: i + 1 for i, t in enumerate(ordered)}
+
+
+def compare(dog: dict, fav: dict) -> tuple[list[dict], int]:
+    rows = []
+    for key, label, better in STATS:
+        dog_wins = (dog[key] - fav[key]) * better > 0
+        rows.append({"key": key, "label": label, "dog": round(dog[key], 3), "fav": round(fav[key], 3),
+                     "dogWins": dog_wins})
+    return rows, sum(r["dogWins"] for r in rows)
+
+
+def certify(stats_won: int, record: tuple[int, int, int]) -> bool:
+    return stats_won >= MIN_STATS_WON and record[0] > record[1]
+
+
+def home_win_prob(home_ml: float, away_ml: float) -> float:
+    """Vig-removed home win probability from American moneylines."""
+    imp = lambda ml: 100 / (ml + 100) if ml > 0 else -ml / (-ml + 100)  # noqa: E731
+    h, a = imp(home_ml), imp(away_ml)
+    return h / (h + a)
+
+
+def dog_spread(spread_line: float, dog_is_home: bool) -> float:
+    """nflverse spread_line is the home team's margin as favorite; the dog's handicap is its mirror."""
+    return -spread_line if dog_is_home else spread_line
+
+
+def evaluate(rows: list[dict], z, season: int, week: int, dog: str, fav: str,
+             dog_record: tuple[int, int, int]) -> dict:
+    d, f = window_stats(rows, dog, season, week), window_stats(rows, fav, season, week)
+    if d is None or f is None or z is None:
+        return {"certifiable": False, "stats": [], "statsWon": 0, "certified": False}
+    ranks = defense_ranks(z)
+    d["def_rank"], f["def_rank"] = ranks.get(dog, 32), ranks.get(fav, 32)
+    stats, won = compare(d, f)
+    return {"certifiable": True, "stats": stats, "statsWon": won, "certified": certify(won, dog_record)}
+
+
+# ---------------------------------------------------------------------------
+# Backtest results and this week's slate
+# ---------------------------------------------------------------------------
+
+@lru_cache(maxsize=1)
+def backtest() -> dict:
+    rows = data.load("hot_dog_backtest")
+    out: dict = {"groups": [r for r in rows if r["group"] != "chosen_bet"]}
+    chosen = next((r for r in rows if r["group"] == "chosen_bet"), None)
+    if chosen:
+        out["chosenBet"] = chosen["bet"]
+        out["certifiedHitRate"] = float(chosen["hit_rate"])
+    return out
+
+
+@lru_cache(maxsize=1)
+def slate() -> list[dict]:
+    import jimmy
+
+    games = {g["game_id"]: g for g in jimmy.next_game_by_team().values()}
+    m = matchup.model()
+    if not games or not m:
+        return []
+    odds = {o["game_id"]: o for o in data.load("matchup_game_odds")}
+    rows = matchup.pond_rows()
+    season = int(next(iter(games.values()))["season"])
+    week = int(next(iter(games.values()))["week"])
+    z = matchup.week_z(rows, season, week, m["k"], m["r"])
+    records = matchup.team_records()
+    bt = backtest()
+
+    out = []
+    for gid, g in sorted(games.items()):
+        o = odds.get(gid)
+        try:
+            hml, aml, spread = float(o["home_moneyline"]), float(o["away_moneyline"]), float(o["spread_line"])
+        except (TypeError, ValueError, KeyError):
+            continue
+        home, away = g["home_team"], g["away_team"]
+        dog_is_home = home_win_prob(hml, aml) < 0.5
+        dog, fav = (home, away) if dog_is_home else (away, home)
+        dog_rec = records.get(dog, (0, 0, 0))
+        result = evaluate(rows, z, season, week, dog, fav, dog_rec)
+        out.append({
+            "gameId": gid,
+            "underdog": dog,
+            "favorite": fav,
+            "dogRecord": "-".join(map(str, dog_rec)),
+            "favRecord": "-".join(map(str, records.get(fav, (0, 0, 0)))),
+            "dogWinning": dog_rec[0] > dog_rec[1],
+            **result,
+            "price": {
+                "moneyline": hml if dog_is_home else aml,
+                "spread": dog_spread(spread, dog_is_home),
+                "spreadOdds": float(o["home_spread_odds" if dog_is_home else "away_spread_odds"] or -110),
+                "source": "reference line (nflverse schedule), not a live FanDuel price",
+            },
+            "recommendedBet": bt.get("chosenBet"),
+        })
+    return out

backend/matchup.py

@@ -42,7 +42,8 @@ import data
 FIELDS = [
     "points_for", "points_against", "margin", "tds", "rz_trips", "rz_tds", "rush_att", "rush_yds",
     "rush_tds", "pass_tds", "dropbacks", "completions", "pass_yds", "sacks_taken", "ints_thrown",
-    "fumbles", "def_passes_defended", "def_tackles_solo", "def_tackles_total",
+    "fumbles", "fumbles_lost", "def_passes_defended", "def_tackles_solo", "def_tackles_total",
+    "third_down_att", "third_down_conv", "drives",
 ]
 
 # stat -> (numerator, denominator). "opp:" reads the opponent's row for that game; "G" counts games.

lake/gold/nfl/_index.csv

@@ -48,3 +48,5 @@ matchup_model,scripts/fit_matchup_model.py,one row per model parameter,38,2026-0
 matchup_backtest,scripts/fit_matchup_model.py,one row per backtest result,66,2026-09-25T21:14:22.388671+00:00,fitted on team_game_stats,ok
 pow_ledger,scripts/pow_ledger.py,one row per posted leg or game pick,30,2026-09-25T21:18:55.199474+00:00,Big Proppa board + lake box scores,ok
 team_game_stats,scripts/build_team_game_stats.py,"one row per team per regular-season game, 2023+",1698,2026-09-25T21:28:55.693042+00:00,nflverse pbp + schedules (derived),ok
+hot_dog_history,scripts/backtest_hot_dogs.py,"one row per underdog game, 2023-2025, with both teams' last-5 stats",732,2026-09-25T21:30:23.616435+00:00,team_game_stats + closing lines (derived),ok
+hot_dog_backtest,scripts/backtest_hot_dogs.py,one row per backtest group,8,2026-09-25T21:30:23.617036+00:00,hot_dog_history (derived),ok

lake/gold/nfl/hot_dog_backtest.csv

@@ -0,0 +1,9 @@
+group,games,outright_wins,outright_rate,covers,cover_games,cover_rate,bet,hit_rate,note
+certified hot dogs,86,34,0.395,42,84,0.500,,,
+all underdogs,732,228,0.311,347,718,0.483,,,
+underdogs with a winning record,200,66,0.330,86,195,0.441,,,
+underdogs winning 3+ of 5 (any record),202,75,0.371,99,198,0.500,,,
+certified hot dogs 2023,20,3,0.150,4,19,0.211,,,
+certified hot dogs 2024,32,12,0.375,18,31,0.581,,,
+certified hot dogs 2025,34,19,0.559,20,34,0.588,,,
+chosen_bet,86,,,,,,spread,0.500,"higher certified hit rate; POW counts tickets won, not cash"

lake/gold/nfl/hot_dog_history.csv

@@ -0,0 +1,733 @@
+season,week,game_id,underdog,favorite,dog_home,dog_moneyline,dog_spread,dog_record,dog_winning,stats_won,certified,dog_margin,won_outright,covered,dog_win_pct,fav_win_pct,dog_opp_ppg,fav_opp_ppg,dog_third_down_pct,fav_third_down_pct,dog_turnover_pct,fav_turnover_pct,dog_def_rank,fav_def_rank
+2023,6,2023_06_ARI_LA,ARI,LA,0,270.0,7.0,1-4-0,0,0,0,-17,0,0,0.2,0.4,27.2,21.6,0.417,0.437,0.098,0.098,30,12
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+2023,6,2023_06_CAR_MIA,CAR,MIA,0,700.0,14.0,0-5-0,0,0,0,-21,0,0,0.0,0.8,28.8,27.0,0.408,0.413,0.143,0.138,25,24
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+2023,6,2023_06_WAS_ATL,WAS,ATL,0,105.0,1.5,2-3-0,0,0,0,8,1,1,0.4,0.6,32.0,19.2,0.367,0.385,0.175,0.123,23,4
+2023,7,2023_07_ARI_SEA,ARI,SEA,0,350.0,9.5,1-5-0,0,1,0,-10,0,0,0.2,0.6,28.4,21.6,0.435,0.316,0.098,0.054,29,26
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+2023,7,2023_07_BUF_NE,NE,BUF,1,310.0,7.5,1-5-0,0,0,0,4,1,1,0.2,0.8,25.4,13.4,0.356,0.516,0.155,0.094,14,5
+2023,7,2023_07_CLE_IND,IND,CLE,1,150.0,3.5,3-3-0,0,2,0,-1,0,1,0.6,0.6,24.2,15.4,0.419,0.306,0.085,0.188,23,2
+2023,7,2023_07_DET_BAL,DET,BAL,0,136.0,3.0,5-1-0,1,2,0,-32,0,0,0.8,0.6,18.6,16.4,0.441,0.444,0.086,0.115,12,1
+2023,7,2023_07_GB_DEN,GB,DEN,0,-102.0,1.5,2-3-0,0,5,0,-2,0,0,0.4,0.2,22.6,36.6,0.409,0.382,0.105,0.175,17,32
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+2023,7,2023_07_LV_CHI,CHI,LV,1,120.0,2.5,1-5-0,0,2,0,18,1,1,0.2,0.4,27.6,23.0,0.439,0.35,0.167,0.212,30,20
+2023,7,2023_07_MIA_PHI,MIA,PHI,0,130.0,3.0,5-1-0,1,1,0,-14,0,0,0.8,0.8,24.4,20.8,0.44,0.541,0.119,0.16,27,19
+2023,7,2023_07_PIT_LA,PIT,LA,0,145.0,3.5,3-2-0,1,3,1,7,1,1,0.6,0.4,22.0,20.8,0.37,0.364,0.081,0.096,21,11
+2023,7,2023_07_SF_MIN,MIN,SF,1,260.0,7.0,2-4-0,0,0,0,5,1,1,0.4,0.8,23.0,16.0,0.322,0.434,0.173,0.036,22,7
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+2023,8,2023_08_ATL_TEN,TEN,ATL,1,110.0,2.5,2-4-0,0,1,0,5,1,1,0.4,0.4,20.2,19.8,0.373,0.426,0.062,0.218,9,4
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+2023,8,2023_08_CHI_LAC,CHI,LAC,0,340.0,9.5,2-5-0,0,2,0,-17,0,0,0.4,0.4,24.6,23.8,0.47,0.343,0.13,0.091,28,30
+2023,8,2023_08_CIN_SF,CIN,SF,0,170.0,4.0,3-3-0,0,0,0,14,1,1,0.6,0.6,20.6,15.8,0.379,0.491,0.093,0.093,15,10
+2023,8,2023_08_CLE_SEA,CLE,SEA,0,164.0,4.0,4-2-0,1,1,0,-4,0,push,0.6,0.8,22.4,17.6,0.297,0.377,0.179,0.088,5,22
+2023,8,2023_08_HOU_CAR,CAR,HOU,1,136.0,3.0,0-6-0,0,0,0,2,1,1,0.0,0.6,32.4,17.6,0.408,0.457,0.089,0.055,31,16
+2023,8,2023_08_JAX_PIT,PIT,JAX,1,114.0,2.5,4-2-0,1,2,0,-10,0,0,0.8,0.8,19.4,21.6,0.371,0.371,0.048,0.098,20,19
+2023,8,2023_08_KC_DEN,DEN,KC,1,270.0,7.0,2-5-0,0,0,0,15,1,1,0.4,1.0,33.0,15.0,0.4,0.554,0.145,0.135,32,2
+2023,8,2023_08_LA_DAL,LA,DAL,0,250.0,6.5,3-4-0,0,2,0,-23,0,0,0.4,0.6,19.6,20.0,0.382,0.479,0.077,0.093,11,7
+2023,8,2023_08_LV_DET,LV,DET,0,270.0,7.0,3-4-0,0,0,0,-12,0,0,0.4,0.8,21.4,18.8,0.323,0.397,0.212,0.054,23,18
+2023,8,2023_08_MIN_GB,GB,MIN,1,-108.0,1.0,2-4-0,0,3,0,-14,0,0,0.2,0.6,22.4,19.6,0.375,0.339,0.13,0.14,13,21
+2023,8,2023_08_NE_MIA,NE,MIA,0,300.0,7.5,2-5-0,0,2,0,-14,0,0,0.4,0.6,25.6,27.2,0.343,0.431,0.14,0.123,14,29
+2023,8,2023_08_NO_IND,IND,NO,1,110.0,2.0,3-4-0,0,2,0,-11,0,0,0.4,0.2,28.0,19.0,0.416,0.303,0.138,0.082,27,3
+2023,8,2023_08_NYJ_NYG,NYG,NYJ,1,136.0,3.0,2-5-0,0,2,0,-3,0,push,0.2,0.4,21.2,20.6,0.362,0.22,0.089,0.103,24,12
+2023,8,2023_08_PHI_WAS,WAS,PHI,1,245.0,7.0,3-4-0,0,1,0,-7,0,push,0.2,0.8,28.2,18.6,0.286,0.528,0.136,0.188,25,17
+2023,8,2023_08_TB_BUF,TB,BUF,0,370.0,10.0,3-3-0,0,2,0,-6,0,1,0.4,0.6,17.4,17.2,0.453,0.492,0.115,0.13,6,8
+2023,9,2023_09_ARI_CLE,ARI,CLE,0,525.0,13.0,1-7-0,0,2,0,-27,0,0,0.0,0.6,29.2,22.0,0.406,0.315,0.125,0.172,26,8
+2023,9,2023_09_BUF_CIN,BUF,CIN,0,105.0,1.5,5-3-0,1,2,0,-6,0,0,0.6,0.8,20.2,18.6,0.475,0.305,0.13,0.089,5,17
+2023,9,2023_09_CHI_NO,CHI,NO,0,340.0,9.0,2-6-0,0,1,0,-7,0,1,0.4,0.4,22.4,20.8,0.485,0.338,0.127,0.097,29,6
+2023,9,2023_09_DAL_PHI,DAL,PHI,0,145.0,3.5,5-2-0,1,3,1,-5,0,0,0.6,0.8,22.0,22.6,0.478,0.522,0.111,0.18,3,23
+2023,9,2023_09_IND_CAR,CAR,IND,1,110.0,1.5,1-6-0,0,3,0,-14,0,0,0.2,0.2,31.0,31.8,0.423,0.437,0.073,0.164,30,31
+2023,9,2023_09_LAC_NYJ,NYJ,LAC,1,142.0,3.0,4-3-0,1,3,1,-21,0,0,0.6,0.6,16.6,21.0,0.219,0.424,0.062,0.111,4,28
+2023,9,2023_09_LA_GB,LA,GB,0,160.0,3.5,3-5-0,0,4,0,-17,0,0,0.4,0.2,24.4,22.2,0.414,0.377,0.059,0.148,14,16
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+2025,17,2025_17_ARI_CIN,ARI,CIN,0,275.0,7.0,3-12-0,0,2,0,-23,0,0,0.0,0.4,31.6,24.8,0.393,0.423,0.094,0.113,26,31
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+2025,17,2025_17_CHI_SF,CHI,SF,0,170.0,3.5,11-4-0,1,1,0,-4,0,0,0.8,1.0,18.0,18.0,0.457,0.569,0.073,0.096,20,18
+2025,17,2025_17_DAL_WAS,WAS,DAL,1,380.0,8.5,4-11-0,0,2,0,-7,0,1,0.2,0.4,24.8,32.2,0.361,0.406,0.163,0.135,30,32
+2025,17,2025_17_DEN_KC,KC,DEN,1,700.0,13.5,6-9-0,0,1,0,-7,0,1,0.2,0.8,22.6,24.4,0.354,0.448,0.132,0.077,7,3
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+2025,17,2025_17_JAX_IND,IND,JAX,1,185.0,3.5,8-7-0,1,2,0,-6,0,0,0.0,1.0,29.0,17.2,0.421,0.397,0.096,0.097,19,14
+2025,17,2025_17_LA_ATL,ATL,LA,1,300.0,7.5,6-9-0,0,1,0,3,1,1,0.6,0.6,24.2,25.4,0.323,0.365,0.132,0.073,13,8
+2025,17,2025_17_NE_NYJ,NYJ,NE,1,625.0,12.5,3-12-0,0,0,0,-32,0,0,0.2,0.8,31.6,21.6,0.319,0.433,0.169,0.077,25,12
+2025,17,2025_17_NO_TEN,TEN,NO,1,100.0,1.5,3-12-0,0,1,0,-8,0,0,0.4,0.6,26.0,17.6,0.361,0.418,0.036,0.111,27,9
+2025,17,2025_17_NYG_LV,LV,NYG,1,136.0,3.0,2-13-0,0,3,0,-24,0,0,0.0,0.0,26.6,27.8,0.3,0.365,0.077,0.096,23,29
+2025,17,2025_17_PHI_BUF,PHI,BUF,0,142.0,3.0,10-5-0,1,3,1,1,1,1,0.4,0.8,17.6,23.0,0.458,0.453,0.148,0.118,5,16
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+2025,18,2025_18_DET_CHI,DET,CHI,0,150.0,3.5,8-8-0,0,1,0,3,1,1,0.2,0.6,30.8,20.8,0.422,0.449,0.109,0.038,21,24
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+2025,18,2025_18_KC_LV,LV,KC,1,185.0,3.5,2-14-0,0,0,0,2,1,1,0.0,0.0,28.6,22.6,0.304,0.333,0.111,0.1,23,7
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scripts/backtest_hot_dogs.py

@@ -0,0 +1,156 @@
+"""
+Backtest the HOT DOG indicator (backend/hot_dog.py) on 2023-2025.
+
+Every regular-season game where both teams have 5 prior games in the pond:
+the sportsbook underdog (closing moneylines, vig removed, from
+lake/bronze/nflverse/games.csv) is compared with the favorite exactly as the
+live indicator does, using only games before kickoff. Outcomes: did the dog
+win outright, and did it cover the closing spread (pushes excluded)?
+
+Writes:
+  lake/gold/nfl/hot_dog_history.csv   one row per underdog game (the new pond)
+  lake/gold/nfl/hot_dog_backtest.csv  hit rates by group, plus the chosen bet:
+                                      whichever of moneyline or spread certified
+                                      dogs hit more often (POW is hit rate)
+
+Run from the repo root after build_team_game_stats.py and fit_matchup_model.py:
+    backend/.venv/bin/python scripts/backtest_hot_dogs.py
+"""
+from __future__ import annotations
+
+import csv
+import sys
+import urllib.request
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(REPO / "backend"))
+sys.path.insert(0, str(REPO / "scripts"))
+
+import hot_dog  # noqa: E402
+import matchup  # noqa: E402
+from build_team_game_stats import BRONZE, GOLD, RELEASES, RELOCATED, download, register_chart  # noqa: E402
+
+SEASONS = (2023, 2024, 2025)
+
+
+def load_lines() -> dict[str, dict]:
+    path = BRONZE / "games.csv"
+    if not path.exists():
+        download(f"{RELEASES}/schedules/games.csv", path)
+    out = {}
+    with path.open(newline="", encoding="utf-8") as fh:
+        for r in csv.DictReader(fh):
+            if r["game_type"] != "REG" or int(r["season"]) not in SEASONS:
+                continue
+            try:
+                out[r["game_id"]] = {
+                    "home": RELOCATED.get(r["home_team"], r["home_team"]),
+                    "away": RELOCATED.get(r["away_team"], r["away_team"]),
+                    "home_ml": float(r["home_moneyline"]), "away_ml": float(r["away_moneyline"]),
+                    "spread": float(r["spread_line"]),
+                }
+            except ValueError:
+                continue
+    return out
+
+
+def record_before(rows: list[dict], team: str, season: int, week: int) -> tuple[int, int, int]:
+    w = l = t = 0
+    for g in rows:
+        if g["team"] == team and g["season"] == season and g["week"] < week:
+            w, l, t = w + (g["win"] == 1.0), l + (g["win"] == 0.0), t + (g["win"] == 0.5)
+    return w, l, t
+
+
+def rate(hits: list[bool]) -> tuple[int, int, str]:
+    n = len(hits)
+    return sum(hits), n, f"{sum(hits) / n:.3f}" if n else ""
+
+
+def main() -> None:
+    model = matchup.model()
+    rows = matchup.game_rows(list(csv.DictReader((GOLD / "team_game_stats.csv").open(encoding="utf-8"))))
+    by_key = {(g["game_id"], g["team"]): g for g in rows}
+    lines = load_lines()
+    z_cache: dict[tuple[int, int], dict | None] = {}
+
+    history = []
+    for gid, ln in sorted(lines.items()):
+        home_row = by_key.get((gid, ln["home"]))
+        if not home_row:
+            continue
+        season, week = home_row["season"], home_row["week"]
+        p_home = hot_dog.home_win_prob(ln["home_ml"], ln["away_ml"])
+        if p_home == 0.5:
+            continue
+        dog_is_home = p_home < 0.5
+        dog, fav = (ln["home"], ln["away"]) if dog_is_home else (ln["away"], ln["home"])
+        if (season, week) not in z_cache:
+            z_cache[(season, week)] = matchup.week_z(rows, season, week, model["k"], model["r"])
+        rec = record_before(rows, dog, season, week)
+        res = hot_dog.evaluate(rows, z_cache[(season, week)], season, week, dog, fav, rec)
+        if not res["certifiable"]:
+            continue
+        margin = by_key[(gid, dog)]["margin"]
+        spread = hot_dog.dog_spread(ln["spread"], dog_is_home)
+        row = {
+            "season": season, "week": week, "game_id": gid, "underdog": dog, "favorite": fav,
+            "dog_home": int(dog_is_home), "dog_moneyline": ln["home_ml"] if dog_is_home else ln["away_ml"],
+            "dog_spread": spread, "dog_record": "-".join(map(str, rec)), "dog_winning": int(rec[0] > rec[1]),
+            "stats_won": res["statsWon"], "certified": int(res["certified"]), "dog_margin": int(margin),
+            "won_outright": int(margin > 0),
+            "covered": "push" if margin + spread == 0 else int(margin + spread > 0),
+        }
+        for s in res["stats"]:
+            row[f"dog_{s['key']}"], row[f"fav_{s['key']}"] = s["dog"], s["fav"]
+        history.append(row)
+
+    groups = {
+        "certified hot dogs": [h for h in history if h["certified"]],
+        "all underdogs": history,
+        "underdogs with a winning record": [h for h in history if h["dog_winning"]],
+        "underdogs winning 3+ of 5 (any record)": [h for h in history if h["stats_won"] >= 3],
+    }
+    for s in SEASONS:
+        groups[f"certified hot dogs {s}"] = [h for h in history if h["certified"] and h["season"] == s]
+
+    report = []
+    for name, hs in groups.items():
+        ml_hits, ml_n, ml_rate = rate([bool(h["won_outright"]) for h in hs])
+        sp = [h["covered"] for h in hs if h["covered"] != "push"]
+        sp_hits, sp_n, sp_rate = rate([bool(c) for c in sp])
+        report.append({"group": name, "games": len(hs), "outright_wins": ml_hits, "outright_rate": ml_rate,
+                       "covers": sp_hits, "cover_games": sp_n, "cover_rate": sp_rate})
+
+    cert = report[0]
+    ml, sp = float(cert["outright_rate"] or 0), float(cert["cover_rate"] or 0)
+    bet, hit = ("moneyline", ml) if ml > sp else ("spread", sp)
+    report.append({"group": "chosen_bet", "bet": bet, "hit_rate": f"{hit:.3f}",
+                   "games": cert["games"], "note": "higher certified hit rate; POW counts tickets won, not cash"})
+
+    hist_cols = list(history[0].keys()) if history else []
+    with (GOLD / "hot_dog_history.csv").open("w", newline="", encoding="utf-8") as fh:
+        w = csv.DictWriter(fh, fieldnames=hist_cols)
+        w.writeheader()
+        w.writerows(history)
+    cols = ["group", "games", "outright_wins", "outright_rate", "covers", "cover_games", "cover_rate",
+            "bet", "hit_rate", "note"]
+    with (GOLD / "hot_dog_backtest.csv").open("w", newline="", encoding="utf-8") as fh:
+        w = csv.DictWriter(fh, fieldnames=cols)
+        w.writeheader()
+        for r in report:
+            w.writerow({c: r.get(c, "") for c in cols})
+
+    register_chart("hot_dog_history", "scripts/backtest_hot_dogs.py",
+                   "one row per underdog game, 2023-2025, with both teams' last-5 stats", len(history),
+                   "team_game_stats + closing lines (derived)")
+    register_chart("hot_dog_backtest", "scripts/backtest_hot_dogs.py", "one row per backtest group",
+                   len(report), "hot_dog_history (derived)")
+
+    for r in report:
+        print({k: v for k, v in r.items() if v != ""})
+
+
+if __name__ == "__main__":
+    main()