fahricansecer
244d8f5366
Deploy Iddaai Backend / build-and-deploy (push) Successful in 6s
- extract_training_data.py: switch from top_leagues.json (23) to qualified_leagues.json (265) - update_implied_odds.py: new script to backfill implied odds from real market data - train_score_model.py: rewrite with v25 102-feature set + temporal split - single_match_orchestrator.py: integrate ML score model with heuristic fallback
308 lines
10 KiB
Python
308 lines
10 KiB
Python
"""
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Update Implied Odds in football_ai_features
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=============================================
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Populates implied_home, implied_draw, implied_away, implied_over25, implied_btts
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from real odds data in odd_categories + odd_selections tables.
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Also backfills form-based features (home_goals_avg_5, away_goals_avg_5, etc.)
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from recent match history.
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Usage:
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python3 scripts/update_implied_odds.py
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"""
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import os
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import sys
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import time
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import psycopg2
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from dotenv import load_dotenv
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load_dotenv()
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def get_conn():
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db_url = os.getenv("DATABASE_URL", "").split("?schema=")[0]
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return psycopg2.connect(db_url)
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def update_implied_odds(conn):
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"""Update implied probabilities from real odds data."""
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cur = conn.cursor()
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print("📊 Phase 1: Updating implied odds from real market data...")
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t0 = time.time()
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# Step 1: Build odds lookup from odd_categories + odd_selections
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print(" Loading odds data...")
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cur.execute("""
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SELECT oc.match_id, oc.name AS cat_name, os.name AS sel_name, os.odd_value
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FROM odd_selections os
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JOIN odd_categories oc ON os.odd_category_db_id = oc.db_id
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WHERE os.odd_value IS NOT NULL
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AND CAST(os.odd_value AS FLOAT) > 1.0
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""")
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odds_by_match = {}
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row_count = 0
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for match_id, cat_name, sel_name, odd_val in cur.fetchall():
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try:
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v = float(odd_val)
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if v <= 1.0:
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continue
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except (ValueError, TypeError):
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continue
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if match_id not in odds_by_match:
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odds_by_match[match_id] = {}
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cat_lower = (cat_name or "").lower().strip()
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sel_lower = (sel_name or "").lower().strip()
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# Match Result (1X2)
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if cat_lower == 'maç sonucu':
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if sel_name == '1':
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odds_by_match[match_id]['ms_h'] = v
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elif sel_name in ('0', 'X'):
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odds_by_match[match_id]['ms_d'] = v
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elif sel_name == '2':
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odds_by_match[match_id]['ms_a'] = v
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# Over/Under 2.5
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elif cat_lower == '2,5 alt/üst':
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if 'üst' in sel_lower:
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odds_by_match[match_id]['ou25_o'] = v
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elif 'alt' in sel_lower:
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odds_by_match[match_id]['ou25_u'] = v
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# BTTS
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elif cat_lower == 'karşılıklı gol':
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if 'var' in sel_lower:
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odds_by_match[match_id]['btts_y'] = v
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elif 'yok' in sel_lower:
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odds_by_match[match_id]['btts_n'] = v
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row_count += 1
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print(f" Loaded odds for {len(odds_by_match)} matches ({row_count} selections) in {time.time()-t0:.1f}s")
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# Step 2: Calculate implied probabilities and update
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print(" Calculating implied probabilities...")
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# Get all match_ids in football_ai_features
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cur.execute("SELECT match_id FROM football_ai_features")
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feature_match_ids = {row[0] for row in cur.fetchall()}
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updated = 0
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batch_size = 500
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updates = []
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for match_id in feature_match_ids:
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odds = odds_by_match.get(match_id, {})
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if not odds:
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continue
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# Implied MS probabilities (vig-free normalization)
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ms_h = odds.get('ms_h', 0)
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ms_d = odds.get('ms_d', 0)
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ms_a = odds.get('ms_a', 0)
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implied_home = 0.33
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implied_draw = 0.33
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implied_away = 0.33
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if ms_h > 1.0 and ms_d > 1.0 and ms_a > 1.0:
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raw_sum = (1 / ms_h) + (1 / ms_d) + (1 / ms_a)
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if raw_sum > 0:
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implied_home = round((1 / ms_h) / raw_sum, 4)
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implied_draw = round((1 / ms_d) / raw_sum, 4)
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implied_away = round((1 / ms_a) / raw_sum, 4)
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# Implied OU25
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ou25_o = odds.get('ou25_o', 0)
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ou25_u = odds.get('ou25_u', 0)
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implied_over25 = 0.50
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if ou25_o > 1.0 and ou25_u > 1.0:
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raw_sum = (1 / ou25_o) + (1 / ou25_u)
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if raw_sum > 0:
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implied_over25 = round((1 / ou25_o) / raw_sum, 4)
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# Implied BTTS
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btts_y = odds.get('btts_y', 0)
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btts_n = odds.get('btts_n', 0)
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implied_btts = 0.50
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if btts_y > 1.0 and btts_n > 1.0:
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raw_sum = (1 / btts_y) + (1 / btts_n)
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if raw_sum > 0:
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implied_btts = round((1 / btts_y) / raw_sum, 4)
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# Only update if we have real data (not all defaults)
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has_real_data = (ms_h > 1.0 or ou25_o > 1.0 or btts_y > 1.0)
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if not has_real_data:
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continue
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updates.append((
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implied_home, implied_draw, implied_away,
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implied_over25, implied_btts, match_id
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))
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if len(updates) >= batch_size:
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cur.executemany("""
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UPDATE football_ai_features
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SET implied_home = %s,
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implied_draw = %s,
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implied_away = %s,
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implied_over25 = %s,
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implied_btts = %s
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WHERE match_id = %s
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""", updates)
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updated += len(updates)
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updates = []
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# Final batch
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if updates:
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cur.executemany("""
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UPDATE football_ai_features
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SET implied_home = %s,
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implied_draw = %s,
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implied_away = %s,
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implied_over25 = %s,
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implied_btts = %s
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WHERE match_id = %s
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""", updates)
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updated += len(updates)
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conn.commit()
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print(f" ✅ Updated implied odds for {updated} matches in {time.time()-t0:.1f}s")
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return updated
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def update_form_features(conn):
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"""Backfill form-based features (goals avg, clean sheet rate) from match history."""
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cur = conn.cursor()
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print("\n📊 Phase 2: Updating form-based features...")
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t0 = time.time()
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# Load all finished football matches ordered by time
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print(" Loading match history...")
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cur.execute("""
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SELECT id, home_team_id, away_team_id, score_home, score_away, mst_utc
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FROM matches
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WHERE status = 'FT'
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AND score_home IS NOT NULL
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AND sport = 'football'
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ORDER BY mst_utc ASC
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""")
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matches = cur.fetchall()
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print(f" Loaded {len(matches)} finished matches")
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# Build team history incrementally
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from collections import defaultdict
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team_history = defaultdict(list) # team_id -> [(goals_scored, goals_conceded)]
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# Get all feature match IDs
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cur.execute("SELECT match_id FROM football_ai_features")
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feature_match_ids = {row[0] for row in cur.fetchall()}
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updated = 0
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batch_size = 500
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updates = []
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for match_id, home_id, away_id, score_home, score_away, mst_utc in matches:
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# Calculate features BEFORE updating history (pre-match features)
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if match_id in feature_match_ids:
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h_hist = team_history[home_id][-5:] # last 5
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a_hist = team_history[away_id][-5:]
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# Home team form
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if h_hist:
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h_goals_avg = sum(g for g, _ in h_hist) / len(h_hist)
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h_conceded_avg = sum(c for _, c in h_hist) / len(h_hist)
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h_cs_rate = sum(1 for _, c in h_hist if c == 0) / len(h_hist)
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h_scoring_rate = sum(1 for g, _ in h_hist if g > 0) / len(h_hist)
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else:
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h_goals_avg, h_conceded_avg = 1.3, 1.2
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h_cs_rate, h_scoring_rate = 0.25, 0.75
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# Away team form
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if a_hist:
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a_goals_avg = sum(g for g, _ in a_hist) / len(a_hist)
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a_conceded_avg = sum(c for _, c in a_hist) / len(a_hist)
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a_cs_rate = sum(1 for _, c in a_hist if c == 0) / len(a_hist)
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a_scoring_rate = sum(1 for g, _ in a_hist if g > 0) / len(a_hist)
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else:
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a_goals_avg, a_conceded_avg = 1.3, 1.2
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a_cs_rate, a_scoring_rate = 0.25, 0.75
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updates.append((
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round(h_goals_avg, 3), round(h_conceded_avg, 3),
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round(h_cs_rate, 3), round(h_scoring_rate, 3),
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round(a_goals_avg, 3), round(a_conceded_avg, 3),
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round(a_cs_rate, 3), round(a_scoring_rate, 3),
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match_id
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))
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if len(updates) >= batch_size:
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cur.executemany("""
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UPDATE football_ai_features
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SET home_goals_avg_5 = %s,
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home_conceded_avg_5 = %s,
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home_clean_sheet_rate = %s,
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home_scoring_rate = %s,
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away_goals_avg_5 = %s,
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away_conceded_avg_5 = %s,
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away_clean_sheet_rate = %s,
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away_scoring_rate = %s
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WHERE match_id = %s
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""", updates)
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updated += len(updates)
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updates = []
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# Update history AFTER feature extraction (maintains pre-match invariant)
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team_history[home_id].append((score_home, score_away))
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team_history[away_id].append((score_away, score_home))
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# Final batch
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if updates:
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cur.executemany("""
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UPDATE football_ai_features
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SET home_goals_avg_5 = %s,
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home_conceded_avg_5 = %s,
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home_clean_sheet_rate = %s,
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home_scoring_rate = %s,
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away_goals_avg_5 = %s,
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away_conceded_avg_5 = %s,
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away_clean_sheet_rate = %s,
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away_scoring_rate = %s
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WHERE match_id = %s
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""", updates)
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updated += len(updates)
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conn.commit()
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print(f" ✅ Updated form features for {updated} matches in {time.time()-t0:.1f}s")
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return updated
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def main():
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print("🚀 Football AI Features — Implied Odds & Form Backfill")
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print("=" * 60)
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conn = get_conn()
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try:
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odds_updated = update_implied_odds(conn)
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form_updated = update_form_features(conn)
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print(f"\n✅ DONE!")
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print(f" Implied odds updated: {odds_updated} matches")
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print(f" Form features updated: {form_updated} matches")
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finally:
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conn.close()
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if __name__ == "__main__":
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main()
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