add pattern analysis feature with web dashboard and CLI
New /patterns page with 9 analyses: chair win bias, bet rank correlations, hand type distributions, pot size buckets, streaks, hourly patterns, and recent-vs-overall comparison. Also adds a standalone analyze.py CLI script for terminal output.
This commit is contained in:
196
app/db.py
196
app/db.py
@@ -571,6 +571,202 @@ def get_analytics(period: str = "all") -> dict:
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}
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@_with_lock
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def get_pattern_analysis() -> dict:
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"""Run all pattern analysis queries and return a single dict."""
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client = get_client()
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# 1. Chair win bias
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result = client.query(
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"SELECT winner, count() AS cnt FROM games GROUP BY winner ORDER BY winner"
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)
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chair_wins = {}
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total_games = 0
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for row in result.result_rows:
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chair = config.CHAIRS.get(row[0], "?")
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chair_wins[chair] = row[1]
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total_games += row[1]
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chair_bias = {"total_games": total_games}
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for ch in ("A", "B", "C"):
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wins = chair_wins.get(ch, 0)
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pct = round(wins / total_games * 100, 2) if total_games else 0
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chair_bias[ch] = {"wins": wins, "pct": pct}
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# 2. Bet rank analysis — how often the highest/mid/lowest bet chair wins
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rank_result = client.query("""
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SELECT
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countIf(winner_bet >= greatest(bet_a, bet_b, bet_c)) AS high,
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countIf(winner_bet > least(bet_a, bet_b, bet_c)
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AND winner_bet < greatest(bet_a, bet_b, bet_c)) AS mid,
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countIf(winner_bet <= least(bet_a, bet_b, bet_c)) AS low
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FROM (
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SELECT bet_a, bet_b, bet_c,
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multiIf(winner = 3, bet_a, winner = 2, bet_b, bet_c) AS winner_bet
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FROM games WHERE bet_a + bet_b + bet_c > 0
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)
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""")
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bet_rank = {"high": 0, "mid": 0, "low": 0}
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if rank_result.result_rows:
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r = rank_result.result_rows[0]
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bet_rank = {"high": r[0], "mid": r[1], "low": r[2]}
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# 3. Per-chair bet rank — when chair X has max bet, how often does X win?
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pcr = client.query("""
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SELECT
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countIf(bet_a >= greatest(bet_a, bet_b, bet_c) AND bet_a > 0) AS a_high,
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countIf(bet_a >= greatest(bet_a, bet_b, bet_c) AND bet_a > 0 AND winner = 3) AS a_win,
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countIf(bet_b >= greatest(bet_a, bet_b, bet_c) AND bet_b > 0) AS b_high,
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countIf(bet_b >= greatest(bet_a, bet_b, bet_c) AND bet_b > 0 AND winner = 2) AS b_win,
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countIf(bet_c >= greatest(bet_a, bet_b, bet_c) AND bet_c > 0) AS c_high,
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countIf(bet_c >= greatest(bet_a, bet_b, bet_c) AND bet_c > 0 AND winner = 1) AS c_win
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FROM games WHERE bet_a + bet_b + bet_c > 0
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""")
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per_chair_rank = {}
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if pcr.result_rows:
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r = pcr.result_rows[0]
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for i, ch in enumerate(("A", "B", "C")):
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has = r[i * 2]
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wins = r[i * 2 + 1]
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per_chair_rank[ch] = {
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"has_highest": has, "wins": wins,
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"win_pct": round(wins / has * 100, 2) if has else 0,
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}
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# 4. Hand type distribution by chair (all dealt hands, not just winners)
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ht_result = client.query("""
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SELECT 'A' AS chair, hand_type_a AS ht, count() AS cnt
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FROM games WHERE hand_type_a > 0 GROUP BY ht
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UNION ALL
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SELECT 'B', hand_type_b, count()
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FROM games WHERE hand_type_b > 0 GROUP BY hand_type_b
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UNION ALL
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SELECT 'C', hand_type_c, count()
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FROM games WHERE hand_type_c > 0 GROUP BY hand_type_c
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ORDER BY chair, ht
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""")
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hand_types_by_chair = {"A": {}, "B": {}, "C": {}}
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for row in ht_result.result_rows:
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ch = row[0]
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type_name = config.HAND_TYPES.get(row[1], f"Type {row[1]}")
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hand_types_by_chair[ch][type_name] = row[2]
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# 5. Hand type win rates (winning hand type distribution)
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htw = client.query("""
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SELECT hand_type, count() AS cnt FROM (
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SELECT multiIf(winner = 3, hand_type_a, winner = 2, hand_type_b, hand_type_c) AS hand_type
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FROM games
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) WHERE hand_type > 0
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GROUP BY hand_type ORDER BY hand_type
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""")
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hand_type_wins = {}
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for row in htw.result_rows:
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type_name = config.HAND_TYPES.get(row[0], f"Type {row[0]}")
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hand_type_wins[type_name] = row[1]
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# 6. Pot size buckets — win rates by pot quartile
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qr = client.query("""
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SELECT
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quantile(0.25)(total_pot) AS q1,
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quantile(0.5)(total_pot) AS q2,
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quantile(0.75)(total_pot) AS q3
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FROM games
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""")
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pot_buckets = {}
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if qr.result_rows:
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q1, q2, q3 = int(qr.result_rows[0][0]), int(qr.result_rows[0][1]), int(qr.result_rows[0][2])
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br = client.query(f"""
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SELECT
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multiIf(
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total_pot <= {q1}, 'small',
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total_pot <= {q2}, 'medium',
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total_pot <= {q3}, 'large',
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'whale'
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) AS bucket,
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winner, count() AS cnt
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FROM games GROUP BY bucket, winner
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""")
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for row in br.result_rows:
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bucket, chair_id, cnt = row[0], row[1], row[2]
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chair = config.CHAIRS.get(chair_id, "?")
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if bucket not in pot_buckets:
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pot_buckets[bucket] = {"A": 0, "B": 0, "C": 0, "total": 0}
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pot_buckets[bucket][chair] = cnt
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pot_buckets[bucket]["total"] += cnt
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pot_buckets["_ranges"] = {
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"small": f"0–{q1}", "medium": f"{q1+1}–{q2}",
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"large": f"{q2+1}–{q3}", "whale": f">{q3}",
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}
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# 7. Streak analysis — compute in Python from ordered winners
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streak_result = client.query(
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"SELECT winner FROM games ORDER BY game_no ASC"
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)
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winners_list = [config.CHAIRS.get(r[0], "?") for r in streak_result.result_rows]
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streaks = {}
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for ch in ("A", "B", "C"):
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max_s = cur = 0
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for w in winners_list:
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if w == ch:
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cur += 1
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max_s = max(max_s, cur)
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else:
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cur = 0
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# current streak from the end
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current = 0
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for w in reversed(winners_list):
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if w == ch:
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current += 1
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else:
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break
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streaks[ch] = {"max_streak": max_s, "current_streak": current}
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# 8. Hourly patterns — win rates by hour of day
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hr_result = client.query("""
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SELECT toHour(created_at) AS hr, winner, count() AS cnt
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FROM games GROUP BY hr, winner ORDER BY hr, winner
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""")
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hourly = {}
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for row in hr_result.result_rows:
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h = str(row[0])
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chair = config.CHAIRS.get(row[1], "?")
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if h not in hourly:
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hourly[h] = {"A": 0, "B": 0, "C": 0, "total": 0}
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hourly[h][chair] = row[2]
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hourly[h]["total"] += row[2]
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# 9. Recent (last 100) vs overall
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recent = client.query("""
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SELECT winner, count() AS cnt FROM (
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SELECT winner FROM games ORDER BY game_no DESC LIMIT 100
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) GROUP BY winner
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""")
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recent_dist = {"A": 0, "B": 0, "C": 0}
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recent_total = 0
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for row in recent.result_rows:
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chair = config.CHAIRS.get(row[0], "?")
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if chair in recent_dist:
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recent_dist[chair] = row[1]
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recent_total += row[1]
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return {
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"chair_bias": chair_bias,
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"bet_rank": bet_rank,
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"per_chair_rank": per_chair_rank,
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"hand_types_by_chair": hand_types_by_chair,
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"hand_type_wins": hand_type_wins,
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"pot_buckets": pot_buckets,
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"streaks": streaks,
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"hourly": hourly,
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"recent_vs_all": {
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"recent": {"dist": recent_dist, "total": recent_total},
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"all": {
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"dist": {ch: chair_bias[ch]["wins"] for ch in ("A", "B", "C")},
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"total": total_games,
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},
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},
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}
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@_with_lock
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def get_hot_cold_players(n: int = 5) -> dict:
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"""
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