add seat recommendations, bet advisor, and live whale/public trends

Prediction hero now shows ranked TOP PICK and 2ND PICK with EV per unit
bet (P(win)*2.9 - 1). Bet Size Advisor panel shows Kelly criterion
fraction (capped 25%), best chair with confidence, and historical bet
rank insight (how often lowest/highest-bet chair wins).

Live Market Sentiment section tracks whale trend (top 5 bettors by amount)
and public trend (total pool distribution) in real-time via WebSocket,
mirroring the live dashboard. Notes highlight agreement/divergence between
model pick and crowd favorite.

Historical crowd analysis cards show how often the most-bet, mid-bet, and
least-bet chairs actually won across all games.

Round result flash now includes whale/public pick accuracy alongside the
model prediction result. user_bet WebSocket events are tracked to build
per-round bettor profiles for whale analysis.

app/db.py

@@ -1290,6 +1290,24 @@ def get_prediction_analysis() -> dict:
     suits = _suit_distribution(cards_data)
     winning_cards = _winning_card_patterns(cards_data)
 
+    # Bet rank analysis — how often the winning chair had high/mid/low bet
+    rank_result = client.query("""
+        SELECT
+            countIf(winner_bet >= greatest(bet_a, bet_b, bet_c)) AS high,
+            countIf(winner_bet > least(bet_a, bet_b, bet_c)
+                AND winner_bet < greatest(bet_a, bet_b, bet_c)) AS mid,
+            countIf(winner_bet <= least(bet_a, bet_b, bet_c)) AS low
+        FROM (
+            SELECT bet_a, bet_b, bet_c,
+                multiIf(winner = 3, bet_a, winner = 2, bet_b, bet_c) AS winner_bet
+            FROM games WHERE bet_a + bet_b + bet_c > 0
+        )
+    """)
+    bet_rank = {"high": 0, "mid": 0, "low": 0}
+    if rank_result.result_rows:
+        r = rank_result.result_rows[0]
+        bet_rank = {"high": r[0], "mid": r[1], "low": r[2]}
+
     return {
         "total_games": len(winners),
         "last_winners": winners[-10:] if len(winners) >= 10 else winners,
@@ -1302,6 +1320,7 @@ def get_prediction_analysis() -> dict:
         "chi_squared": chi_squared,
         "runs_test": runs_test,
         "backtest": backtest,
+        "bet_rank": bet_rank,
         "card_values": card_values,
         "face_cards": face_cards,
         "suits": suits,