first (part 2: other directories)

2026-04-16 15:11:25 +03:00
parent 7814e0bc6b
commit 2f0b85a0c7
203 changed files with 59989 additions and 0 deletions
@@ -0,0 +1,94 @@
+
+import os
+import sys
+import psycopg2
+import pandas as pd
+from datetime import datetime
+
+# Database Connection
+DSN = os.getenv('DATABASE_URL', 'postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db')
+if '?' in DSN: DSN = DSN.split('?')[0]
+
+def diagnose():
+    try:
+        conn = psycopg2.connect(DSN)
+        cursor = conn.cursor()
+        
+        print("🔍 DIAGNOSTIC REPORT: AI Data Coverage")
+        print("=======================================")
+        
+        # 1. Total Football Matches (Finished)
+        cursor.execute("SELECT COUNT(*) FROM matches WHERE sport='football' AND score_home IS NOT NULL")
+        total_matches = cursor.fetchone()[0]
+        print(f"Total Finished Football Matches: {total_matches:,}")
+        
+        if total_matches == 0:
+            print("❌ No matches found!")
+            return
+
+        # 2. Stats Coverage (match_team_stats)
+        cursor.execute("SELECT COUNT(DISTINCT match_id) FROM match_team_stats")
+        stats_count = cursor.fetchone()[0]
+        print(f"Matches with Team Stats:       {stats_count:,} ({stats_count/total_matches*100:.1f}%)")
+        
+        # 3. Squad Coverage (match_player_participation)
+        cursor.execute("SELECT COUNT(DISTINCT match_id) FROM match_player_participation")
+        squad_count = cursor.fetchone()[0]
+        print(f"Matches with Lineups (Squad):  {squad_count:,} ({squad_count/total_matches*100:.1f}%)")
+        
+        # 4. Officials Coverage
+        cursor.execute("SELECT COUNT(DISTINCT match_id) FROM match_officials")
+        officials_count = cursor.fetchone()[0]
+        print(f"Matches with Officials:        {officials_count:,} ({officials_count/total_matches*100:.1f}%)")
+        
+        # 5. Overlap (Gold Standard Data)
+        cursor.execute("""
+            SELECT COUNT(m.id) 
+            FROM matches m
+            JOIN match_team_stats mts ON m.id = mts.match_id
+            JOIN match_player_participation mpp ON m.id = mpp.match_id
+            WHERE m.sport='football' AND m.score_home IS NOT NULL
+        """)
+        # Note: This join might be slow on huge DB without distinct on join inputs, but distinct count matches is better logic
+        # Rewrite for speed: check distinct IDs in intersection
+        
+        cursor.execute("""
+            SELECT COUNT(*) FROM (
+                SELECT id FROM matches WHERE sport='football' AND score_home IS NOT NULL
+                INTERSECT
+                SELECT match_id FROM match_team_stats
+                INTERSECT
+                SELECT match_id FROM match_player_participation
+                INTERSECT
+                SELECT match_id FROM match_officials
+            ) as overlap
+        """)
+        overlap_count = cursor.fetchone()[0]
+        print(f"Matches with ALL Data (Golden): {overlap_count:,} ({overlap_count/total_matches*100:.1f}%)")
+        
+        print("\n🔍 RECENT DATA QUALITY (Last 1000 Matches)")
+        print("==========================================")
+        # Check last 1000 matches specifically
+        cursor.execute("""
+            WITH recent AS (
+                SELECT id FROM matches 
+                WHERE sport='football' AND score_home IS NOT NULL 
+                ORDER BY mst_utc DESC LIMIT 1000
+            )
+            SELECT 
+                (SELECT COUNT(DISTINCT match_id) FROM match_team_stats WHERE match_id IN (SELECT id FROM recent)) as has_stats,
+                (SELECT COUNT(DISTINCT match_id) FROM match_player_participation WHERE match_id IN (SELECT id FROM recent)) as has_squad,
+                (SELECT COUNT(DISTINCT match_id) FROM match_officials WHERE match_id IN (SELECT id FROM recent)) as has_officials
+        """)
+        recent_stats = cursor.fetchone()
+        print(f"Has Stats:     {recent_stats[0]/10:.1f}%")
+        print(f"Has Lineups:   {recent_stats[1]/10:.1f}%")
+        print(f"Has Officials: {recent_stats[2]/10:.1f}%")
+
+    except Exception as e:
+        print(f"Error: {e}")
+    finally:
+        if conn: conn.close()
+
+if __name__ == "__main__":
+    diagnose()