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Fahri Can
2026-05-17 02:17:22 +03:00
parent 17ace9bd12
commit 94c7a4481a
53 changed files with 29602 additions and 7832 deletions
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ai-engine/core/engines/player_predictor.py
+161 -53
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@@ -24,32 +24,29 @@ class PlayerPrediction:
extract_training_data.py so that inference values match the
distribution the model was trained on (~3-36 range).
"""
home_squad_quality: float = 12.0 # training-scale composite (~3-36)
home_squad_quality: float = 12.0
away_squad_quality: float = 12.0
squad_diff: float = 0.0 # home - away (training scale)
squad_diff: float = 0.0
home_key_players: int = 0
away_key_players: int = 0
home_missing_impact: float = 0.0 # 0-1, how much weaker due to missing players
home_missing_impact: float = 0.0
away_missing_impact: float = 0.0
home_goals_form: int = 0 # Goals in last 5 matches
home_goals_form: int = 0
away_goals_form: int = 0
home_lineup_goals_per90: float = 0.0
away_lineup_goals_per90: float = 0.0
home_lineup_assists_per90: float = 0.0
away_lineup_assists_per90: float = 0.0
home_squad_continuity: float = 0.5
away_squad_continuity: float = 0.5
home_top_scorer_form: int = 0
away_top_scorer_form: int = 0
home_avg_player_exp: float = 0.0
away_avg_player_exp: float = 0.0
home_goals_diversity: float = 0.0
away_goals_diversity: float = 0.0
lineup_available: bool = False
confidence: float = 0.0
def to_dict(self) -> dict:
return {
"home_squad_quality": round(self.home_squad_quality, 1),
"away_squad_quality": round(self.away_squad_quality, 1),
"squad_diff": round(self.squad_diff, 1),
"home_key_players": self.home_key_players,
"away_key_players": self.away_key_players,
"home_missing_impact": round(self.home_missing_impact, 2),
"away_missing_impact": round(self.away_missing_impact, 2),
"home_goals_form": self.home_goals_form,
"away_goals_form": self.away_goals_form,
"lineup_available": self.lineup_available,
"confidence": round(self.confidence, 1)
}
class PlayerPredictorEngine:
@@ -90,8 +87,9 @@ class PlayerPredictorEngine:
"""
# Get squad features
home_analysis = None
away_analysis = None
if home_lineup and away_lineup:
# Use provided lineups (for live matches)
home_analysis = self.squad_engine.analyze_squad_from_list(
home_lineup, home_team_id
)
@@ -99,7 +97,6 @@ class PlayerPredictorEngine:
away_lineup, away_team_id
)
lineup_available = True
# Build features dict from analysis objects
features = {
"home_starting_11": home_analysis.starting_count or 11,
"home_goals_last_5": home_analysis.total_goals_last_5,
@@ -113,7 +110,6 @@ class PlayerPredictorEngine:
"away_forwards": away_analysis.forward_count or 2,
}
elif match_id:
# Try to get from database
try:
features = self.squad_engine.get_features(
match_id, home_team_id, away_team_id
@@ -132,58 +128,42 @@ class PlayerPredictorEngine:
home_team_id, away_team_id
)
lineup_available = False
# Extract features
home_goals = int(features.get("home_goals_last_5", 0))
away_goals = int(features.get("away_goals_last_5", 0))
home_key = int(features.get("home_key_players", 0))
away_key = int(features.get("away_key_players", 0))
home_assists = features.get("home_assists_last_5", 0)
away_assists = features.get("away_assists_last_5", 0)
home_starting = features.get("home_starting_11", 11)
away_starting = features.get("away_starting_11", 11)
home_fwd = features.get("home_forwards", 2)
away_fwd = features.get("away_forwards", 2)
# Calculate squad quality — MUST match extract_training_data.py formula
# Formula: starting_count * 0.3 + goals * 2.0 + assists * 1.0
# + key_players * 3.0 + fwd_count * 1.5
# Typical range: ~3 – 36 (model trained on this distribution)
home_quality = (
home_starting * 0.3 +
home_goals * 2.0 +
home_assists * 1.0 +
home_key * 3.0 +
home_fwd * 1.5
)
away_quality = (
away_starting * 0.3 +
away_goals * 2.0 +
away_assists * 1.0 +
away_key * 3.0 +
away_fwd * 1.5
)
# Squad difference
# Squad quality — matches V25 extract_training_data.py:579
home_quality = home_starting * 0.3 + home_key * 3.0 + home_fwd * 1.5
away_quality = away_starting * 0.3 + away_key * 3.0 + away_fwd * 1.5
squad_diff = home_quality - away_quality
# Missing player impact
# Priority: sidelined data (position-weighted) > lineup count (basic)
if sidelined_data:
home_impact, away_impact = self.sidelined_analyzer.analyze_match(sidelined_data)
home_missing = min(1.0, max(0.0, home_impact.impact_score))
away_missing = min(1.0, max(0.0, away_impact.impact_score))
sidelined_available = True
else:
# Fallback: basic lineup count method
expected_xi = 11
actual_home_xi = features.get("home_starting_11", 11)
actual_away_xi = features.get("away_starting_11", 11)
home_missing = (expected_xi - actual_home_xi) / expected_xi if actual_home_xi < expected_xi else 0
away_missing = (expected_xi - actual_away_xi) / expected_xi if actual_away_xi < expected_xi else 0
sidelined_available = False
# Confidence: more data sources = higher confidence
# Player-level features (matches extract_training_data.py:594-650)
player_feats = self._compute_player_level_features(
home_lineup or [], away_lineup or [],
home_team_id, away_team_id,
home_analysis, away_analysis,
)
confidence = 70.0 if lineup_available else 35.0
if home_goals + away_goals > 10:
confidence += 15
@@ -191,7 +171,7 @@ class PlayerPredictorEngine:
confidence += self.sidelined_analyzer.config.get("sidelined.confidence_boost", 10)
if not lineup_available:
confidence -= 5.0
return PlayerPrediction(
home_squad_quality=home_quality,
away_squad_quality=away_quality,
@@ -202,9 +182,137 @@ class PlayerPredictorEngine:
away_missing_impact=away_missing,
home_goals_form=home_goals,
away_goals_form=away_goals,
home_lineup_goals_per90=player_feats['home_lineup_goals_per90'],
away_lineup_goals_per90=player_feats['away_lineup_goals_per90'],
home_lineup_assists_per90=player_feats['home_lineup_assists_per90'],
away_lineup_assists_per90=player_feats['away_lineup_assists_per90'],
home_squad_continuity=player_feats['home_squad_continuity'],
away_squad_continuity=player_feats['away_squad_continuity'],
home_top_scorer_form=player_feats['home_top_scorer_form'],
away_top_scorer_form=player_feats['away_top_scorer_form'],
home_avg_player_exp=player_feats['home_avg_player_exp'],
away_avg_player_exp=player_feats['away_avg_player_exp'],
home_goals_diversity=player_feats['home_goals_diversity'],
away_goals_diversity=player_feats['away_goals_diversity'],
lineup_available=lineup_available,
confidence=max(5.0, confidence)
)
def _compute_player_level_features(
self,
home_lineup: List[str],
away_lineup: List[str],
home_team_id: str,
away_team_id: str,
home_analysis,
away_analysis,
) -> Dict[str, float]:
defaults = {
'home_lineup_goals_per90': 0.0, 'away_lineup_goals_per90': 0.0,
'home_lineup_assists_per90': 0.0, 'away_lineup_assists_per90': 0.0,
'home_squad_continuity': 0.5, 'away_squad_continuity': 0.5,
'home_top_scorer_form': 0, 'away_top_scorer_form': 0,
'home_avg_player_exp': 0.0, 'away_avg_player_exp': 0.0,
'home_goals_diversity': 0.0, 'away_goals_diversity': 0.0,
}
conn = self.squad_engine.get_conn()
if conn is None:
return defaults
try:
from psycopg2.extras import RealDictCursor
result = {}
for prefix, lineup, team_id in [
('home', home_lineup, home_team_id),
('away', away_lineup, away_team_id),
]:
if not lineup:
for k in ('lineup_goals_per90', 'lineup_assists_per90',
'squad_continuity', 'top_scorer_form',
'avg_player_exp', 'goals_diversity'):
result[f'{prefix}_{k}'] = defaults[f'{prefix}_{k}']
continue
g90, a90, total_exp = 0.0, 0.0, 0
best_scorer_total, best_scorer_id = 0, None
scorers_in_lineup = 0
with conn.cursor(cursor_factory=RealDictCursor) as cur:
for pid in lineup:
cur.execute("""
SELECT
COUNT(*) as starts,
COALESCE(SUM(CASE WHEN e.event_type = 'goal'
AND (e.event_subtype IS NULL OR e.event_subtype NOT ILIKE '%%penaltı kaçırma%%')
THEN 1 ELSE 0 END), 0) as goals,
COALESCE((SELECT COUNT(*) FROM match_player_events
WHERE assist_player_id = %s), 0) as assists
FROM match_player_participation mpp
LEFT JOIN match_player_events e
ON e.match_id = mpp.match_id AND e.player_id = mpp.player_id
WHERE mpp.player_id = %s AND mpp.is_starting = true
""", (pid, pid))
row = cur.fetchone()
if not row or not row['starts']:
continue
starts = row['starts']
goals = row['goals'] or 0
assists = row['assists'] or 0
g90 += goals / starts
a90 += assists / starts
total_exp += starts
if goals > 0:
scorers_in_lineup += 1
if goals > best_scorer_total:
best_scorer_total = goals
best_scorer_id = pid
n_st = len(lineup) or 1
# Top scorer recent form (goals in last 5 starts)
top_scorer_form = 0
if best_scorer_id:
cur.execute("""
SELECT COUNT(*) as goals
FROM match_player_events mpe
WHERE mpe.player_id = %s AND mpe.event_type = 'goal'
AND mpe.match_id IN (
SELECT match_id FROM match_player_participation
WHERE player_id = %s AND is_starting = true
ORDER BY match_id DESC LIMIT 5
)
""", (best_scorer_id, best_scorer_id))
tsf_row = cur.fetchone()
if tsf_row:
top_scorer_form = tsf_row['goals'] or 0
# Squad continuity (overlap with previous match lineup)
squad_continuity = 0.5
cur.execute("""
SELECT mpp.player_id
FROM match_player_participation mpp
JOIN matches m ON mpp.match_id = m.id
WHERE mpp.team_id = %s AND mpp.is_starting = true
AND m.status = 'FT'
ORDER BY m.mst_utc DESC
LIMIT 11
""", (team_id,))
prev_starters = {r['player_id'] for r in cur.fetchall()}
if prev_starters:
overlap = len(set(lineup) & prev_starters)
squad_continuity = overlap / n_st
result[f'{prefix}_lineup_goals_per90'] = round(g90, 3)
result[f'{prefix}_lineup_assists_per90'] = round(a90, 3)
result[f'{prefix}_squad_continuity'] = round(squad_continuity, 3)
result[f'{prefix}_top_scorer_form'] = top_scorer_form
result[f'{prefix}_avg_player_exp'] = round(total_exp / n_st, 1)
result[f'{prefix}_goals_diversity'] = round(scorers_in_lineup / n_st, 3)
return result
except Exception as e:
print(f"[PlayerPredictor] Player-level features failed: {e}")
return defaults
def get_1x2_modifier(self, prediction: PlayerPrediction) -> Dict[str, float]:
"""