784 lines
31 KiB
Python
784 lines
31 KiB
Python
from web.database import query_db
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class StatsService:
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@staticmethod
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def get_team_stats_summary():
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"""
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Calculates aggregate statistics for matches where at least 2 roster members played together.
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Returns:
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{
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'map_stats': [{'map_name', 'count', 'wins', 'win_rate'}],
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'elo_stats': [{'range', 'count', 'wins', 'win_rate'}],
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'duration_stats': [{'range', 'count', 'wins', 'win_rate'}],
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'round_stats': [{'type', 'count', 'wins', 'win_rate'}]
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}
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"""
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# 1. Get Active Roster
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from web.services.web_service import WebService
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import json
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lineups = WebService.get_lineups()
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active_roster_ids = []
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if lineups:
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try:
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raw_ids = json.loads(lineups[0]['player_ids_json'])
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active_roster_ids = [str(uid) for uid in raw_ids]
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except:
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pass
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if not active_roster_ids:
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return {}
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# 2. Find matches with >= 2 roster members
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# We need match_id, map_name, scores, winner_team, duration, avg_elo
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# And we need to determine if "Our Team" won.
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placeholders = ','.join('?' for _ in active_roster_ids)
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# Step A: Get Candidate Match IDs (matches with >= 2 roster players)
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# Also get the team_id of our players in that match to determine win
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candidate_sql = f"""
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SELECT mp.match_id, MAX(mp.team_id) as our_team_id
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FROM fact_match_players mp
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WHERE CAST(mp.steam_id_64 AS TEXT) IN ({placeholders})
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GROUP BY mp.match_id
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HAVING COUNT(DISTINCT mp.steam_id_64) >= 2
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"""
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candidate_rows = query_db('l2', candidate_sql, active_roster_ids)
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if not candidate_rows:
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return {}
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candidate_map = {row['match_id']: row['our_team_id'] for row in candidate_rows}
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match_ids = list(candidate_map.keys())
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match_placeholders = ','.join('?' for _ in match_ids)
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# Step B: Get Match Details
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match_sql = f"""
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SELECT m.match_id, m.map_name, m.score_team1, m.score_team2, m.winner_team, m.duration,
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AVG(fmt.group_origin_elo) as avg_elo
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FROM fact_matches m
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LEFT JOIN fact_match_teams fmt ON m.match_id = fmt.match_id AND fmt.group_origin_elo > 0
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WHERE m.match_id IN ({match_placeholders})
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GROUP BY m.match_id
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"""
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match_rows = query_db('l2', match_sql, match_ids)
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# 3. Process Data
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# Buckets initialization
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map_stats = {}
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elo_ranges = ['<1000', '1000-1200', '1200-1400', '1400-1600', '1600-1800', '1800-2000', '2000+']
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elo_stats = {r: {'wins': 0, 'total': 0} for r in elo_ranges}
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dur_ranges = ['<30m', '30-45m', '45m+']
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dur_stats = {r: {'wins': 0, 'total': 0} for r in dur_ranges}
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round_types = ['Stomp (<15)', 'Normal', 'Close (>23)', 'Choke (24)']
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round_stats = {r: {'wins': 0, 'total': 0} for r in round_types}
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for m in match_rows:
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mid = m['match_id']
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# Determine Win
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# Use candidate_map to get our_team_id.
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# Note: winner_team is usually int (1 or 2) or string.
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# our_team_id from fact_match_players is usually int (1 or 2).
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# This logic assumes simple team ID matching.
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# If sophisticated "UID in Winning Group" logic is needed, we'd need more queries.
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# For aggregate stats, let's assume team_id matching is sufficient for 99% cases or fallback to simple check.
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# Actually, let's try to be consistent with get_matches logic if possible,
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# but getting group_uids for ALL matches is heavy.
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# Let's trust team_id for this summary.
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our_tid = candidate_map[mid]
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winner_tid = m['winner_team']
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# Type normalization
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try:
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is_win = (int(our_tid) == int(winner_tid)) if (our_tid and winner_tid) else False
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except:
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is_win = (str(our_tid) == str(winner_tid)) if (our_tid and winner_tid) else False
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# 1. Map Stats
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map_name = m['map_name'] or 'Unknown'
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if map_name not in map_stats:
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map_stats[map_name] = {'wins': 0, 'total': 0}
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map_stats[map_name]['total'] += 1
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if is_win: map_stats[map_name]['wins'] += 1
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# 2. ELO Stats
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elo = m['avg_elo']
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if elo:
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if elo < 1000: e_key = '<1000'
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elif elo < 1200: e_key = '1000-1200'
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elif elo < 1400: e_key = '1200-1400'
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elif elo < 1600: e_key = '1400-1600'
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elif elo < 1800: e_key = '1600-1800'
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elif elo < 2000: e_key = '1800-2000'
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else: e_key = '2000+'
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elo_stats[e_key]['total'] += 1
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if is_win: elo_stats[e_key]['wins'] += 1
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# 3. Duration Stats
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dur = m['duration'] # seconds
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if dur:
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dur_min = dur / 60
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if dur_min < 30: d_key = '<30m'
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elif dur_min < 45: d_key = '30-45m'
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else: d_key = '45m+'
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dur_stats[d_key]['total'] += 1
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if is_win: dur_stats[d_key]['wins'] += 1
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# 4. Round Stats
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s1 = m['score_team1'] or 0
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s2 = m['score_team2'] or 0
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total_rounds = s1 + s2
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if total_rounds == 24:
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r_key = 'Choke (24)'
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round_stats[r_key]['total'] += 1
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if is_win: round_stats[r_key]['wins'] += 1
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# Note: Close (>23) overlaps with Choke (24).
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# User requirement: Close > 23 counts ALL matches > 23, regardless of other categories.
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if total_rounds > 23:
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r_key = 'Close (>23)'
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round_stats[r_key]['total'] += 1
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if is_win: round_stats[r_key]['wins'] += 1
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if total_rounds < 15:
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r_key = 'Stomp (<15)'
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round_stats[r_key]['total'] += 1
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if is_win: round_stats[r_key]['wins'] += 1
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elif total_rounds <= 23: # Only Normal if NOT Stomp and NOT Close (<= 23 and >= 15)
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r_key = 'Normal'
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round_stats[r_key]['total'] += 1
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if is_win: round_stats[r_key]['wins'] += 1
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# 4. Format Results
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def fmt(stats_dict):
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res = []
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for k, v in stats_dict.items():
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rate = (v['wins'] / v['total'] * 100) if v['total'] > 0 else 0
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res.append({'label': k, 'count': v['total'], 'wins': v['wins'], 'win_rate': rate})
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return res
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# For maps, sort by count
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map_res = fmt(map_stats)
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map_res.sort(key=lambda x: x['count'], reverse=True)
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return {
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'map_stats': map_res,
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'elo_stats': fmt(elo_stats), # Keep order
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'duration_stats': fmt(dur_stats), # Keep order
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'round_stats': fmt(round_stats) # Keep order
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}
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@staticmethod
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def get_recent_matches(limit=5):
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sql = """
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SELECT m.match_id, m.start_time, m.map_name, m.score_team1, m.score_team2, m.winner_team,
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p.username as mvp_name
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FROM fact_matches m
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LEFT JOIN dim_players p ON m.mvp_uid = p.uid
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ORDER BY m.start_time DESC
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LIMIT ?
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"""
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return query_db('l2', sql, [limit])
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@staticmethod
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def get_matches(page=1, per_page=20, map_name=None, date_from=None, date_to=None):
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offset = (page - 1) * per_page
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args = []
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where_clauses = ["1=1"]
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if map_name:
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where_clauses.append("map_name = ?")
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args.append(map_name)
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if date_from:
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where_clauses.append("start_time >= ?")
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args.append(date_from)
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if date_to:
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where_clauses.append("start_time <= ?")
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args.append(date_to)
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where_str = " AND ".join(where_clauses)
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sql = f"""
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SELECT m.match_id, m.start_time, m.map_name, m.score_team1, m.score_team2, m.winner_team, m.duration
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FROM fact_matches m
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WHERE {where_str}
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ORDER BY m.start_time DESC
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LIMIT ? OFFSET ?
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"""
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args.extend([per_page, offset])
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matches = query_db('l2', sql, args)
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# Enrich matches with Avg ELO, Party info, and Our Team Result
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if matches:
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match_ids = [m['match_id'] for m in matches]
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placeholders = ','.join('?' for _ in match_ids)
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# Fetch ELO
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elo_sql = f"""
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SELECT match_id, AVG(group_origin_elo) as avg_elo
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FROM fact_match_teams
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WHERE match_id IN ({placeholders}) AND group_origin_elo > 0
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GROUP BY match_id
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"""
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elo_rows = query_db('l2', elo_sql, match_ids)
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elo_map = {row['match_id']: row['avg_elo'] for row in elo_rows}
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# Fetch Max Party Size
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party_sql = f"""
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SELECT match_id, MAX(cnt) as max_party
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FROM (
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SELECT match_id, match_team_id, COUNT(*) as cnt
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FROM fact_match_players
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WHERE match_id IN ({placeholders}) AND match_team_id > 0
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GROUP BY match_id, match_team_id
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)
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GROUP BY match_id
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"""
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party_rows = query_db('l2', party_sql, match_ids)
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party_map = {row['match_id']: row['max_party'] for row in party_rows}
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# --- New: Determine "Our Team" Result ---
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# Logic: Check if any player from `active_roster` played in these matches.
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# Use WebService to get the active roster
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from web.services.web_service import WebService
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import json
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lineups = WebService.get_lineups()
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active_roster_ids = []
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if lineups:
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try:
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# Load IDs and ensure they are all strings for DB comparison consistency
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raw_ids = json.loads(lineups[0]['player_ids_json'])
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active_roster_ids = [str(uid) for uid in raw_ids]
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except:
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pass
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# If no roster, we can't determine "Our Result"
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if not active_roster_ids:
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result_map = {}
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else:
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# 1. Get UIDs for Roster Members involved in these matches
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# We query fact_match_players to ensure we get the UIDs actually used in these matches
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roster_placeholders = ','.join('?' for _ in active_roster_ids)
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uid_sql = f"""
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SELECT DISTINCT steam_id_64, uid
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FROM fact_match_players
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WHERE match_id IN ({placeholders})
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AND CAST(steam_id_64 AS TEXT) IN ({roster_placeholders})
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"""
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combined_args_uid = match_ids + active_roster_ids
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uid_rows = query_db('l2', uid_sql, combined_args_uid)
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# Set of "Our UIDs" (as strings)
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our_uids = set()
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for r in uid_rows:
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if r['uid']:
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our_uids.add(str(r['uid']))
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# 2. Get Group UIDs and Winner info from fact_match_teams
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# We need to know which group contains our UIDs
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teams_sql = f"""
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SELECT fmt.match_id, fmt.group_id, fmt.group_uids, m.winner_team
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FROM fact_match_teams fmt
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JOIN fact_matches m ON fmt.match_id = m.match_id
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WHERE fmt.match_id IN ({placeholders})
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"""
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teams_rows = query_db('l2', teams_sql, match_ids)
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# 3. Determine Result per Match
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result_map = {}
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# Group data by match
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match_groups = {} # match_id -> {group_id: [uids...], winner: int}
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for r in teams_rows:
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mid = r['match_id']
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gid = r['group_id']
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uids_str = r['group_uids'] or ""
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# Split and clean UIDs
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uids = set(str(u).strip() for u in uids_str.split(',') if u.strip())
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if mid not in match_groups:
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match_groups[mid] = {'groups': {}, 'winner': r['winner_team']}
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match_groups[mid]['groups'][gid] = uids
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# Analyze
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for mid, data in match_groups.items():
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winner_gid = data['winner']
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groups = data['groups']
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our_in_winner = False
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our_in_loser = False
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# Check each group
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for gid, uids in groups.items():
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# Intersection of Our UIDs and Group UIDs
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common = our_uids.intersection(uids)
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if common:
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if gid == winner_gid:
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our_in_winner = True
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else:
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our_in_loser = True
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if our_in_winner and not our_in_loser:
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result_map[mid] = 'win'
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elif our_in_loser and not our_in_winner:
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result_map[mid] = 'loss'
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elif our_in_winner and our_in_loser:
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result_map[mid] = 'mixed'
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else:
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# Fallback: If UID matching failed (maybe missing UIDs), try old team_id method?
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# Or just leave it as None (safe)
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pass
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# Convert to dict to modify
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matches = [dict(m) for m in matches]
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for m in matches:
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m['avg_elo'] = elo_map.get(m['match_id'], 0)
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m['max_party'] = party_map.get(m['match_id'], 1)
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m['our_result'] = result_map.get(m['match_id'])
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# Convert to dict to modify
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matches = [dict(m) for m in matches]
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for m in matches:
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m['avg_elo'] = elo_map.get(m['match_id'], 0)
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m['max_party'] = party_map.get(m['match_id'], 1)
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m['our_result'] = result_map.get(m['match_id'])
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# Count total for pagination
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count_sql = f"SELECT COUNT(*) as cnt FROM fact_matches WHERE {where_str}"
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total = query_db('l2', count_sql, args[:-2], one=True)['cnt']
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return matches, total
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@staticmethod
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def get_match_detail(match_id):
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sql = "SELECT * FROM fact_matches WHERE match_id = ?"
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return query_db('l2', sql, [match_id], one=True)
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@staticmethod
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def get_match_players(match_id):
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sql = """
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SELECT mp.*, p.username, p.avatar_url
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FROM fact_match_players mp
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LEFT JOIN dim_players p ON mp.steam_id_64 = p.steam_id_64
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WHERE mp.match_id = ?
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ORDER BY mp.team_id, mp.rating DESC
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"""
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return query_db('l2', sql, [match_id])
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@staticmethod
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def get_match_rounds(match_id):
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sql = "SELECT * FROM fact_rounds WHERE match_id = ? ORDER BY round_num"
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return query_db('l2', sql, [match_id])
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@staticmethod
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def get_players(page=1, per_page=20, search=None, sort_by='rating_desc'):
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offset = (page - 1) * per_page
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args = []
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where_clauses = ["1=1"]
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if search:
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# Force case-insensitive search
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where_clauses.append("(LOWER(username) LIKE LOWER(?) OR steam_id_64 LIKE ?)")
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args.append(f"%{search}%")
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args.append(f"%{search}%")
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where_str = " AND ".join(where_clauses)
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# Sort mapping
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order_clause = "rating DESC" # Default logic (this query needs refinement as L2 dim_players doesn't store avg rating)
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# Wait, dim_players only has static info. We need aggregated stats.
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# Ideally, we should fetch from L3 for player list stats.
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# But StatsService is for L2.
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# For the Player List, we usually want L3 data (Career stats).
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# I will leave the detailed stats logic for FeatureService or do a join here if necessary.
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# For now, just listing players from dim_players.
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sql = f"""
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SELECT * FROM dim_players
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WHERE {where_str}
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LIMIT ? OFFSET ?
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"""
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args.extend([per_page, offset])
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players = query_db('l2', sql, args)
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total = query_db('l2', f"SELECT COUNT(*) as cnt FROM dim_players WHERE {where_str}", args[:-2], one=True)['cnt']
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return players, total
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@staticmethod
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def get_player_info(steam_id):
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sql = "SELECT * FROM dim_players WHERE steam_id_64 = ?"
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return query_db('l2', sql, [steam_id], one=True)
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@staticmethod
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def get_daily_match_counts(days=365):
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# Return list of {date: 'YYYY-MM-DD', count: N}
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sql = """
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SELECT date(start_time, 'unixepoch') as day, COUNT(*) as count
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FROM fact_matches
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WHERE start_time > strftime('%s', 'now', ?)
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GROUP BY day
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ORDER BY day
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"""
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# sqlite modifier for 'now' needs format like '-365 days'
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modifier = f'-{days} days'
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rows = query_db('l2', sql, [modifier])
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return rows
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@staticmethod
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def get_players_by_ids(steam_ids):
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if not steam_ids:
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return []
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placeholders = ','.join('?' for _ in steam_ids)
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sql = f"SELECT * FROM dim_players WHERE steam_id_64 IN ({placeholders})"
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return query_db('l2', sql, steam_ids)
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@staticmethod
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def get_player_basic_stats(steam_id):
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# Calculate stats from fact_match_players
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# Prefer calculating from sums (kills/deaths) for K/D accuracy
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# AVG(adr) is used as damage_total might be missing in some sources
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sql = """
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SELECT
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AVG(rating) as rating,
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SUM(kills) as total_kills,
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SUM(deaths) as total_deaths,
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AVG(kd_ratio) as avg_kd,
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AVG(kast) as kast,
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AVG(adr) as adr,
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COUNT(*) as matches_played
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FROM fact_match_players
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WHERE steam_id_64 = ?
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"""
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row = query_db('l2', sql, [steam_id], one=True)
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|
|
if row and row['matches_played'] > 0:
|
|
res = dict(row)
|
|
|
|
# Calculate K/D: Sum Kills / Sum Deaths
|
|
kills = res.get('total_kills') or 0
|
|
deaths = res.get('total_deaths') or 0
|
|
|
|
if deaths > 0:
|
|
res['kd'] = kills / deaths
|
|
else:
|
|
res['kd'] = kills # If 0 deaths, K/D is kills (or infinity, but kills is safer for display)
|
|
|
|
# Fallback to avg_kd if calculation failed (e.g. both 0) but avg_kd exists
|
|
if res['kd'] == 0 and res['avg_kd'] and res['avg_kd'] > 0:
|
|
res['kd'] = res['avg_kd']
|
|
|
|
# ADR validation
|
|
if res['adr'] is None:
|
|
res['adr'] = 0.0
|
|
|
|
return res
|
|
return None
|
|
|
|
@staticmethod
|
|
def get_shared_matches(steam_ids):
|
|
# Find matches where ALL steam_ids were present
|
|
if not steam_ids or len(steam_ids) < 1:
|
|
return []
|
|
|
|
placeholders = ','.join('?' for _ in steam_ids)
|
|
count = len(steam_ids)
|
|
|
|
# We need to know which team the players were on to determine win/loss
|
|
# Assuming they were on the SAME team for "shared experience"
|
|
# If count=1, it's just match history
|
|
|
|
# Query: Get matches where all steam_ids are present
|
|
# Also join to get team_id to check if they were on the same team (optional but better)
|
|
# For simplicity in v1: Just check presence in the match.
|
|
# AND check if the player won.
|
|
|
|
# We need to return: match_id, map_name, score, result (Win/Loss)
|
|
# "Result" is relative to the lineup.
|
|
# If they were on the winning team, it's a Win.
|
|
|
|
sql = f"""
|
|
SELECT m.match_id, m.start_time, m.map_name, m.score_team1, m.score_team2, m.winner_team,
|
|
MAX(mp.team_id) as player_team_id -- Just take one team_id (assuming same)
|
|
FROM fact_matches m
|
|
JOIN fact_match_players mp ON m.match_id = mp.match_id
|
|
WHERE mp.steam_id_64 IN ({placeholders})
|
|
GROUP BY m.match_id
|
|
HAVING COUNT(DISTINCT mp.steam_id_64) = ?
|
|
ORDER BY m.start_time DESC
|
|
"""
|
|
|
|
args = list(steam_ids)
|
|
args.append(count)
|
|
|
|
rows = query_db('l2', sql, args)
|
|
|
|
results = []
|
|
for r in rows:
|
|
# Determine if Win
|
|
# winner_team in DB is 'Team 1' or 'Team 2' usually, or the team name.
|
|
# fact_matches.winner_team stores the NAME of the winner? Or 'team1'/'team2'?
|
|
# Let's check how L2_Builder stores it. Usually it stores the name.
|
|
# But fact_match_players.team_id stores the name too.
|
|
|
|
# Logic: If m.winner_team == mp.team_id, then Win.
|
|
is_win = (r['winner_team'] == r['player_team_id'])
|
|
|
|
# If winner_team is NULL or empty, it's a draw?
|
|
if not r['winner_team']:
|
|
result_str = 'Draw'
|
|
elif is_win:
|
|
result_str = 'Win'
|
|
else:
|
|
result_str = 'Loss'
|
|
|
|
res = dict(r)
|
|
res['is_win'] = is_win # Boolean for styling
|
|
res['result_str'] = result_str # Text for display
|
|
results.append(res)
|
|
|
|
return results
|
|
|
|
@staticmethod
|
|
def get_player_trend(steam_id, limit=20):
|
|
# We need party_size: count of players with same match_team_id in the same match
|
|
# Using a correlated subquery for party_size
|
|
sql = """
|
|
SELECT * FROM (
|
|
SELECT
|
|
m.start_time,
|
|
mp.rating,
|
|
mp.kd_ratio,
|
|
mp.adr,
|
|
m.match_id,
|
|
m.map_name,
|
|
mp.is_win,
|
|
mp.match_team_id,
|
|
(SELECT COUNT(*)
|
|
FROM fact_match_players p2
|
|
WHERE p2.match_id = mp.match_id
|
|
AND p2.match_team_id = mp.match_team_id
|
|
AND p2.match_team_id > 0 -- Ensure we don't count 0 (solo default) as a massive party
|
|
) as party_size,
|
|
(
|
|
SELECT COUNT(*)
|
|
FROM fact_matches m2
|
|
WHERE m2.start_time <= m.start_time
|
|
) as match_index
|
|
FROM fact_match_players mp
|
|
JOIN fact_matches m ON mp.match_id = m.match_id
|
|
WHERE mp.steam_id_64 = ?
|
|
ORDER BY m.start_time DESC
|
|
LIMIT ?
|
|
) ORDER BY start_time ASC
|
|
"""
|
|
return query_db('l2', sql, [steam_id, limit])
|
|
|
|
@staticmethod
|
|
def get_roster_stats_distribution(target_steam_id):
|
|
"""
|
|
Calculates rank and distribution of the target player within the active roster.
|
|
Now covers all L3 Basic Features for Detailed Panel.
|
|
"""
|
|
from web.services.web_service import WebService
|
|
from web.services.feature_service import FeatureService
|
|
import json
|
|
import numpy as np
|
|
|
|
# 1. Get Active Roster IDs
|
|
lineups = WebService.get_lineups()
|
|
active_roster_ids = []
|
|
if lineups:
|
|
try:
|
|
raw_ids = json.loads(lineups[0]['player_ids_json'])
|
|
active_roster_ids = [str(uid) for uid in raw_ids]
|
|
except:
|
|
pass
|
|
|
|
if not active_roster_ids:
|
|
return None
|
|
|
|
# 2. Fetch L3 features for all roster members
|
|
# We need to use FeatureService to get the full L3 set (including detailed stats)
|
|
# Assuming L3 data is up to date.
|
|
|
|
placeholders = ','.join('?' for _ in active_roster_ids)
|
|
sql = f"SELECT * FROM dm_player_features WHERE steam_id_64 IN ({placeholders})"
|
|
rows = query_db('l3', sql, active_roster_ids)
|
|
|
|
if not rows:
|
|
return None
|
|
|
|
stats_map = {row['steam_id_64']: dict(row) for row in rows}
|
|
target_steam_id = str(target_steam_id)
|
|
|
|
# If target not in map (e.g. no L3 data), try to add empty default
|
|
if target_steam_id not in stats_map:
|
|
stats_map[target_steam_id] = {}
|
|
|
|
# 3. Calculate Distribution for ALL metrics
|
|
# Define metrics list (must match Detailed Panel keys)
|
|
metrics = [
|
|
'basic_avg_rating', 'basic_avg_kd', 'basic_avg_kast', 'basic_avg_rws', 'basic_avg_adr',
|
|
'basic_avg_headshot_kills', 'basic_headshot_rate', 'basic_avg_assisted_kill', 'basic_avg_awp_kill', 'basic_avg_jump_count',
|
|
'basic_avg_mvps', 'basic_avg_plants', 'basic_avg_defuses', 'basic_avg_flash_assists',
|
|
'basic_avg_first_kill', 'basic_avg_first_death', 'basic_first_kill_rate', 'basic_first_death_rate',
|
|
'basic_avg_kill_2', 'basic_avg_kill_3', 'basic_avg_kill_4', 'basic_avg_kill_5',
|
|
'basic_avg_perfect_kill', 'basic_avg_revenge_kill',
|
|
# L3 Advanced Dimensions
|
|
'sta_last_30_rating', 'sta_win_rating', 'sta_loss_rating', 'sta_rating_volatility', 'sta_time_rating_corr',
|
|
'bat_kd_diff_high_elo', 'bat_avg_duel_win_rate', 'bat_win_rate_vs_all',
|
|
'hps_clutch_win_rate_1v1', 'hps_clutch_win_rate_1v3_plus', 'hps_match_point_win_rate', 'hps_pressure_entry_rate', 'hps_comeback_kd_diff', 'hps_losing_streak_kd_diff',
|
|
'ptl_pistol_kills', 'ptl_pistol_win_rate', 'ptl_pistol_kd', 'ptl_pistol_util_efficiency',
|
|
'side_rating_ct', 'side_rating_t', 'side_first_kill_rate_ct', 'side_first_kill_rate_t', 'side_kd_diff_ct_t', 'side_hold_success_rate_ct', 'side_entry_success_rate_t',
|
|
'side_win_rate_ct', 'side_win_rate_t', 'side_kd_ct', 'side_kd_t',
|
|
'side_kast_ct', 'side_kast_t', 'side_rws_ct', 'side_rws_t',
|
|
'side_first_death_rate_ct', 'side_first_death_rate_t',
|
|
'side_multikill_rate_ct', 'side_multikill_rate_t',
|
|
'side_headshot_rate_ct', 'side_headshot_rate_t',
|
|
'side_defuses_ct', 'side_plants_t',
|
|
'util_avg_nade_dmg', 'util_avg_flash_time', 'util_avg_flash_enemy', 'util_usage_rate'
|
|
]
|
|
|
|
# Mapping for L2 legacy calls (if any) - mainly map 'rating' to 'basic_avg_rating' etc if needed
|
|
# But here we just use L3 columns directly.
|
|
|
|
result = {}
|
|
|
|
for m in metrics:
|
|
values = [p.get(m, 0) or 0 for p in stats_map.values()]
|
|
target_val = stats_map[target_steam_id].get(m, 0) or 0
|
|
|
|
if not values:
|
|
result[m] = None
|
|
continue
|
|
|
|
values.sort(reverse=True)
|
|
|
|
# Rank
|
|
try:
|
|
rank = values.index(target_val) + 1
|
|
except ValueError:
|
|
rank = len(values)
|
|
|
|
result[m] = {
|
|
'val': target_val,
|
|
'rank': rank,
|
|
'total': len(values),
|
|
'min': min(values),
|
|
'max': max(values),
|
|
'avg': sum(values) / len(values)
|
|
}
|
|
|
|
# Legacy mapping for top cards (rating, kd, adr, kast)
|
|
legacy_map = {
|
|
'basic_avg_rating': 'rating',
|
|
'basic_avg_kd': 'kd',
|
|
'basic_avg_adr': 'adr',
|
|
'basic_avg_kast': 'kast'
|
|
}
|
|
if m in legacy_map:
|
|
result[legacy_map[m]] = result[m]
|
|
|
|
return result
|
|
|
|
@staticmethod
|
|
def get_live_matches():
|
|
# Query matches started in last 2 hours with no winner
|
|
# Assuming we have a way to ingest live matches.
|
|
# For now, this query is 'formal' but will likely return empty on static dataset.
|
|
sql = """
|
|
SELECT m.match_id, m.map_name, m.score_team1, m.score_team2, m.start_time
|
|
FROM fact_matches m
|
|
WHERE m.winner_team IS NULL
|
|
AND m.start_time > strftime('%s', 'now', '-2 hours')
|
|
"""
|
|
return query_db('l2', sql)
|
|
|
|
@staticmethod
|
|
def get_head_to_head_stats(match_id):
|
|
"""
|
|
Returns a matrix of kills between players.
|
|
List of {attacker_steam_id, victim_steam_id, kills}
|
|
"""
|
|
sql = """
|
|
SELECT attacker_steam_id, victim_steam_id, COUNT(*) as kills
|
|
FROM fact_round_events
|
|
WHERE match_id = ? AND event_type = 'kill'
|
|
GROUP BY attacker_steam_id, victim_steam_id
|
|
"""
|
|
return query_db('l2', sql, [match_id])
|
|
|
|
@staticmethod
|
|
def get_match_round_details(match_id):
|
|
"""
|
|
Returns a detailed dictionary of rounds, events, and economy.
|
|
{
|
|
round_num: {
|
|
info: {winner_side, win_reason_desc, end_time_stamp...},
|
|
events: [ {event_type, event_time, attacker..., weapon...}, ... ],
|
|
economy: { steam_id: {main_weapon, equipment_value...}, ... }
|
|
}
|
|
}
|
|
"""
|
|
# 1. Base Round Info
|
|
rounds_sql = "SELECT * FROM fact_rounds WHERE match_id = ? ORDER BY round_num"
|
|
rounds_rows = query_db('l2', rounds_sql, [match_id])
|
|
|
|
if not rounds_rows:
|
|
return {}
|
|
|
|
# 2. Events
|
|
events_sql = """
|
|
SELECT * FROM fact_round_events
|
|
WHERE match_id = ?
|
|
ORDER BY round_num, event_time
|
|
"""
|
|
events_rows = query_db('l2', events_sql, [match_id])
|
|
|
|
# 3. Economy (if avail)
|
|
eco_sql = """
|
|
SELECT * FROM fact_round_player_economy
|
|
WHERE match_id = ?
|
|
"""
|
|
eco_rows = query_db('l2', eco_sql, [match_id])
|
|
|
|
# Structure Data
|
|
result = {}
|
|
|
|
# Initialize rounds
|
|
for r in rounds_rows:
|
|
r_num = r['round_num']
|
|
result[r_num] = {
|
|
'info': dict(r),
|
|
'events': [],
|
|
'economy': {}
|
|
}
|
|
|
|
# Group events
|
|
for e in events_rows:
|
|
r_num = e['round_num']
|
|
if r_num in result:
|
|
result[r_num]['events'].append(dict(e))
|
|
|
|
# Group economy
|
|
for eco in eco_rows:
|
|
r_num = eco['round_num']
|
|
sid = eco['steam_id_64']
|
|
if r_num in result:
|
|
result[r_num]['economy'][sid] = dict(eco)
|
|
|
|
return result
|
|
|