from web.database import query_db, execute_db from flask import current_app, url_for import os class StatsService: @staticmethod def resolve_avatar_url(steam_id, avatar_url): """ Resolves avatar URL with priority: 1. Local File (web/static/avatars/{steam_id}.jpg/png) - User override 2. DB Value (avatar_url) """ try: # Check local file first (User Request: "directly associate if exists") base = os.path.join(current_app.root_path, 'static', 'avatars') for ext in ('.jpg', '.png', '.jpeg'): fname = f"{steam_id}{ext}" fpath = os.path.join(base, fname) if os.path.exists(fpath): return url_for('static', filename=f'avatars/{fname}') # Fallback to DB value if valid if avatar_url and str(avatar_url).strip(): return avatar_url return None except Exception: return avatar_url @staticmethod def get_team_stats_summary(): """ Calculates aggregate statistics for matches where at least 2 roster members played together. Returns: { 'map_stats': [{'map_name', 'count', 'wins', 'win_rate'}], 'elo_stats': [{'range', 'count', 'wins', 'win_rate'}], 'duration_stats': [{'range', 'count', 'wins', 'win_rate'}], 'round_stats': [{'type', 'count', 'wins', 'win_rate'}] } """ # 1. Get Active Roster from web.services.web_service import WebService import json 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 {} # 2. Find matches with >= 2 roster members # We need match_id, map_name, scores, winner_team, duration, avg_elo # And we need to determine if "Our Team" won. placeholders = ','.join('?' for _ in active_roster_ids) # Step A: Get Candidate Match IDs (matches with >= 2 roster players) # Also get the team_id of our players in that match to determine win candidate_sql = f""" SELECT mp.match_id, MAX(mp.team_id) as our_team_id FROM fact_match_players mp WHERE CAST(mp.steam_id_64 AS TEXT) IN ({placeholders}) GROUP BY mp.match_id HAVING COUNT(DISTINCT mp.steam_id_64) >= 2 """ candidate_rows = query_db('l2', candidate_sql, active_roster_ids) if not candidate_rows: return {} candidate_map = {row['match_id']: row['our_team_id'] for row in candidate_rows} match_ids = list(candidate_map.keys()) match_placeholders = ','.join('?' for _ in match_ids) # Step B: Get Match Details match_sql = f""" SELECT m.match_id, m.map_name, m.score_team1, m.score_team2, m.winner_team, m.duration, AVG(fmt.group_origin_elo) as avg_elo FROM fact_matches m LEFT JOIN fact_match_teams fmt ON m.match_id = fmt.match_id AND fmt.group_origin_elo > 0 WHERE m.match_id IN ({match_placeholders}) GROUP BY m.match_id """ match_rows = query_db('l2', match_sql, match_ids) # 3. Process Data # Buckets initialization map_stats = {} elo_ranges = ['<1000', '1000-1200', '1200-1400', '1400-1600', '1600-1800', '1800-2000', '2000+'] elo_stats = {r: {'wins': 0, 'total': 0} for r in elo_ranges} dur_ranges = ['<30m', '30-45m', '45m+'] dur_stats = {r: {'wins': 0, 'total': 0} for r in dur_ranges} round_types = ['Stomp (<15)', 'Normal', 'Close (>23)', 'Choke (24)'] round_stats = {r: {'wins': 0, 'total': 0} for r in round_types} for m in match_rows: mid = m['match_id'] # Determine Win # Use candidate_map to get our_team_id. # Note: winner_team is usually int (1 or 2) or string. # our_team_id from fact_match_players is usually int (1 or 2). # This logic assumes simple team ID matching. # If sophisticated "UID in Winning Group" logic is needed, we'd need more queries. # For aggregate stats, let's assume team_id matching is sufficient for 99% cases or fallback to simple check. # Actually, let's try to be consistent with get_matches logic if possible, # but getting group_uids for ALL matches is heavy. # Let's trust team_id for this summary. our_tid = candidate_map[mid] winner_tid = m['winner_team'] # Type normalization try: is_win = (int(our_tid) == int(winner_tid)) if (our_tid and winner_tid) else False except: is_win = (str(our_tid) == str(winner_tid)) if (our_tid and winner_tid) else False # 1. Map Stats map_name = m['map_name'] or 'Unknown' if map_name not in map_stats: map_stats[map_name] = {'wins': 0, 'total': 0} map_stats[map_name]['total'] += 1 if is_win: map_stats[map_name]['wins'] += 1 # 2. ELO Stats elo = m['avg_elo'] if elo: if elo < 1000: e_key = '<1000' elif elo < 1200: e_key = '1000-1200' elif elo < 1400: e_key = '1200-1400' elif elo < 1600: e_key = '1400-1600' elif elo < 1800: e_key = '1600-1800' elif elo < 2000: e_key = '1800-2000' else: e_key = '2000+' elo_stats[e_key]['total'] += 1 if is_win: elo_stats[e_key]['wins'] += 1 # 3. Duration Stats dur = m['duration'] # seconds if dur: dur_min = dur / 60 if dur_min < 30: d_key = '<30m' elif dur_min < 45: d_key = '30-45m' else: d_key = '45m+' dur_stats[d_key]['total'] += 1 if is_win: dur_stats[d_key]['wins'] += 1 # 4. Round Stats s1 = m['score_team1'] or 0 s2 = m['score_team2'] or 0 total_rounds = s1 + s2 if total_rounds == 24: r_key = 'Choke (24)' round_stats[r_key]['total'] += 1 if is_win: round_stats[r_key]['wins'] += 1 # Note: Close (>23) overlaps with Choke (24). # User requirement: Close > 23 counts ALL matches > 23, regardless of other categories. if total_rounds > 23: r_key = 'Close (>23)' round_stats[r_key]['total'] += 1 if is_win: round_stats[r_key]['wins'] += 1 if total_rounds < 15: r_key = 'Stomp (<15)' round_stats[r_key]['total'] += 1 if is_win: round_stats[r_key]['wins'] += 1 elif total_rounds <= 23: # Only Normal if NOT Stomp and NOT Close (<= 23 and >= 15) r_key = 'Normal' round_stats[r_key]['total'] += 1 if is_win: round_stats[r_key]['wins'] += 1 # 4. Format Results def fmt(stats_dict): res = [] for k, v in stats_dict.items(): rate = (v['wins'] / v['total'] * 100) if v['total'] > 0 else 0 res.append({'label': k, 'count': v['total'], 'wins': v['wins'], 'win_rate': rate}) return res # For maps, sort by count map_res = fmt(map_stats) map_res.sort(key=lambda x: x['count'], reverse=True) return { 'map_stats': map_res, 'elo_stats': fmt(elo_stats), # Keep order 'duration_stats': fmt(dur_stats), # Keep order 'round_stats': fmt(round_stats) # Keep order } @staticmethod def get_recent_matches(limit=5): sql = """ SELECT m.match_id, m.start_time, m.map_name, m.score_team1, m.score_team2, m.winner_team, p.username as mvp_name FROM fact_matches m LEFT JOIN dim_players p ON m.mvp_uid = p.uid ORDER BY m.start_time DESC LIMIT ? """ return query_db('l2', sql, [limit]) @staticmethod def get_matches(page=1, per_page=20, map_name=None, date_from=None, date_to=None): offset = (page - 1) * per_page args = [] where_clauses = ["1=1"] if map_name: where_clauses.append("map_name = ?") args.append(map_name) if date_from: where_clauses.append("start_time >= ?") args.append(date_from) if date_to: where_clauses.append("start_time <= ?") args.append(date_to) where_str = " AND ".join(where_clauses) sql = f""" SELECT m.match_id, m.start_time, m.map_name, m.score_team1, m.score_team2, m.winner_team, m.duration FROM fact_matches m WHERE {where_str} ORDER BY m.start_time DESC LIMIT ? OFFSET ? """ args.extend([per_page, offset]) matches = query_db('l2', sql, args) # Enrich matches with Avg ELO, Party info, and Our Team Result if matches: match_ids = [m['match_id'] for m in matches] placeholders = ','.join('?' for _ in match_ids) # Fetch ELO elo_sql = f""" SELECT match_id, AVG(group_origin_elo) as avg_elo FROM fact_match_teams WHERE match_id IN ({placeholders}) AND group_origin_elo > 0 GROUP BY match_id """ elo_rows = query_db('l2', elo_sql, match_ids) elo_map = {row['match_id']: row['avg_elo'] for row in elo_rows} # Fetch Max Party Size party_sql = f""" SELECT match_id, MAX(cnt) as max_party FROM ( SELECT match_id, match_team_id, COUNT(*) as cnt FROM fact_match_players WHERE match_id IN ({placeholders}) AND match_team_id > 0 GROUP BY match_id, match_team_id ) GROUP BY match_id """ party_rows = query_db('l2', party_sql, match_ids) party_map = {row['match_id']: row['max_party'] for row in party_rows} # --- New: Determine "Our Team" Result --- # Logic: Check if any player from `active_roster` played in these matches. # Use WebService to get the active roster from web.services.web_service import WebService import json lineups = WebService.get_lineups() active_roster_ids = [] if lineups: try: # Load IDs and ensure they are all strings for DB comparison consistency raw_ids = json.loads(lineups[0]['player_ids_json']) active_roster_ids = [str(uid) for uid in raw_ids] except: pass # If no roster, we can't determine "Our Result" if not active_roster_ids: result_map = {} else: # 1. Get UIDs for Roster Members involved in these matches # We query fact_match_players to ensure we get the UIDs actually used in these matches roster_placeholders = ','.join('?' for _ in active_roster_ids) uid_sql = f""" SELECT DISTINCT steam_id_64, uid FROM fact_match_players WHERE match_id IN ({placeholders}) AND CAST(steam_id_64 AS TEXT) IN ({roster_placeholders}) """ combined_args_uid = match_ids + active_roster_ids uid_rows = query_db('l2', uid_sql, combined_args_uid) # Set of "Our UIDs" (as strings) our_uids = set() for r in uid_rows: if r['uid']: our_uids.add(str(r['uid'])) # 2. Get Group UIDs and Winner info from fact_match_teams # We need to know which group contains our UIDs teams_sql = f""" SELECT fmt.match_id, fmt.group_id, fmt.group_uids, m.winner_team FROM fact_match_teams fmt JOIN fact_matches m ON fmt.match_id = m.match_id WHERE fmt.match_id IN ({placeholders}) """ teams_rows = query_db('l2', teams_sql, match_ids) # 3. Determine Result per Match result_map = {} # Group data by match match_groups = {} # match_id -> {group_id: [uids...], winner: int} for r in teams_rows: mid = r['match_id'] gid = r['group_id'] uids_str = r['group_uids'] or "" # Split and clean UIDs uids = set(str(u).strip() for u in uids_str.split(',') if u.strip()) if mid not in match_groups: match_groups[mid] = {'groups': {}, 'winner': r['winner_team']} match_groups[mid]['groups'][gid] = uids # Analyze for mid, data in match_groups.items(): winner_gid = data['winner'] groups = data['groups'] our_in_winner = False our_in_loser = False # Check each group for gid, uids in groups.items(): # Intersection of Our UIDs and Group UIDs common = our_uids.intersection(uids) if common: if gid == winner_gid: our_in_winner = True else: our_in_loser = True if our_in_winner and not our_in_loser: result_map[mid] = 'win' elif our_in_loser and not our_in_winner: result_map[mid] = 'loss' elif our_in_winner and our_in_loser: result_map[mid] = 'mixed' else: # Fallback: If UID matching failed (maybe missing UIDs), try old team_id method? # Or just leave it as None (safe) pass # Convert to dict to modify matches = [dict(m) for m in matches] for m in matches: m['avg_elo'] = elo_map.get(m['match_id'], 0) m['max_party'] = party_map.get(m['match_id'], 1) m['our_result'] = result_map.get(m['match_id']) # Convert to dict to modify matches = [dict(m) for m in matches] for m in matches: m['avg_elo'] = elo_map.get(m['match_id'], 0) m['max_party'] = party_map.get(m['match_id'], 1) m['our_result'] = result_map.get(m['match_id']) # Count total for pagination count_sql = f"SELECT COUNT(*) as cnt FROM fact_matches WHERE {where_str}" total = query_db('l2', count_sql, args[:-2], one=True)['cnt'] return matches, total @staticmethod def get_match_detail(match_id): sql = "SELECT * FROM fact_matches WHERE match_id = ?" return query_db('l2', sql, [match_id], one=True) @staticmethod def get_match_players(match_id): sql = """ SELECT mp.*, p.username, p.avatar_url FROM fact_match_players mp LEFT JOIN dim_players p ON mp.steam_id_64 = p.steam_id_64 WHERE mp.match_id = ? ORDER BY mp.team_id, mp.rating DESC """ rows = query_db('l2', sql, [match_id]) result = [] for r in rows or []: d = dict(r) d['avatar_url'] = StatsService.resolve_avatar_url(d.get('steam_id_64'), d.get('avatar_url')) result.append(d) return result @staticmethod def get_match_rounds(match_id): sql = "SELECT * FROM fact_rounds WHERE match_id = ? ORDER BY round_num" return query_db('l2', sql, [match_id]) @staticmethod def get_players(page=1, per_page=20, search=None, sort_by='rating_desc'): offset = (page - 1) * per_page args = [] where_clauses = ["1=1"] if search: # Force case-insensitive search where_clauses.append("(LOWER(username) LIKE LOWER(?) OR steam_id_64 LIKE ?)") args.append(f"%{search}%") args.append(f"%{search}%") where_str = " AND ".join(where_clauses) # Sort mapping order_clause = "rating DESC" # Default logic (this query needs refinement as L2 dim_players doesn't store avg rating) # Wait, dim_players only has static info. We need aggregated stats. # Ideally, we should fetch from L3 for player list stats. # But StatsService is for L2. # For the Player List, we usually want L3 data (Career stats). # I will leave the detailed stats logic for FeatureService or do a join here if necessary. # For now, just listing players from dim_players. sql = f""" SELECT * FROM dim_players WHERE {where_str} LIMIT ? OFFSET ? """ args.extend([per_page, offset]) rows = query_db('l2', sql, args) players = [] for r in rows or []: d = dict(r) d['avatar_url'] = StatsService.resolve_avatar_url(d.get('steam_id_64'), d.get('avatar_url')) players.append(d) total = query_db('l2', f"SELECT COUNT(*) as cnt FROM dim_players WHERE {where_str}", args[:-2], one=True)['cnt'] return players, total @staticmethod def get_player_info(steam_id): sql = "SELECT * FROM dim_players WHERE steam_id_64 = ?" r = query_db('l2', sql, [steam_id], one=True) if not r: return None d = dict(r) d['avatar_url'] = StatsService.resolve_avatar_url(steam_id, d.get('avatar_url')) return d @staticmethod def get_daily_match_counts(days=365): # Return list of {date: 'YYYY-MM-DD', count: N} sql = """ SELECT date(start_time, 'unixepoch') as day, COUNT(*) as count FROM fact_matches WHERE start_time > strftime('%s', 'now', ?) GROUP BY day ORDER BY day """ # sqlite modifier for 'now' needs format like '-365 days' modifier = f'-{days} days' rows = query_db('l2', sql, [modifier]) return rows @staticmethod def get_players_by_ids(steam_ids): if not steam_ids: return [] placeholders = ','.join('?' for _ in steam_ids) sql = f"SELECT * FROM dim_players WHERE steam_id_64 IN ({placeholders})" rows = query_db('l2', sql, steam_ids) result = [] for r in rows or []: d = dict(r) d['avatar_url'] = StatsService.resolve_avatar_url(d.get('steam_id_64'), d.get('avatar_url')) result.append(d) return result @staticmethod def get_player_basic_stats(steam_id): l3 = query_db( "l3", """ SELECT total_matches as matches_played, core_avg_rating as rating, core_avg_kd as kd, core_avg_kast as kast, core_avg_adr as adr FROM dm_player_features WHERE steam_id_64 = ? """, [steam_id], one=True, ) if l3 and (l3["matches_played"] or 0) > 0: return dict(l3) sql = """ SELECT AVG(rating) as rating, SUM(kills) as total_kills, SUM(deaths) as total_deaths, AVG(kd_ratio) as avg_kd, AVG(kast) as kast, AVG(adr) as adr, COUNT(*) as matches_played FROM fact_match_players WHERE steam_id_64 = ? """ row = query_db("l2", sql, [steam_id], one=True) if row and row["matches_played"] > 0: res = dict(row) 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 res["kd"] == 0 and res["avg_kd"] and res["avg_kd"] > 0: res["kd"] = res["avg_kd"] 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): l3_sql = """ SELECT * FROM ( SELECT match_date as start_time, rating, kd_ratio, adr, kast, match_id, map_name, is_win, match_sequence as match_index FROM dm_player_match_history WHERE steam_id_64 = ? ORDER BY match_date DESC LIMIT ? ) ORDER BY start_time ASC """ l3_rows = query_db("l3", l3_sql, [steam_id, limit]) if l3_rows: return l3_rows 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 ) 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_recent_performance_stats(steam_id): """ Calculates Avg Rating and Rating Variance for: - Last 5, 10, 15 matches - Last 5, 10, 15 days """ def avg_var(nums): if not nums: return 0.0, 0.0 n = len(nums) avg = sum(nums) / n var = sum((x - avg) ** 2 for x in nums) / n return avg, var rows = query_db( "l3", """ SELECT match_date as t, rating FROM dm_player_match_history WHERE steam_id_64 = ? ORDER BY match_date DESC """, [steam_id], ) if not rows: rows = query_db( "l2", """ SELECT m.start_time as t, mp.rating 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 """, [steam_id], ) if not rows: return {} matches = [{"time": r["t"], "rating": float(r["rating"] or 0)} for r in rows] stats = {} for n in [5, 10, 15]: subset = matches[:n] ratings = [m["rating"] for m in subset] avg, var = avg_var(ratings) stats[f"last_{n}_matches"] = {"avg": avg, "var": var, "count": len(ratings)} import time now = time.time() for d in [5, 10, 15]: cutoff = now - (d * 24 * 3600) subset = [m for m in matches if (m["time"] or 0) >= cutoff] ratings = [m["rating"] for m in subset] avg, var = avg_var(ratings) stats[f"last_{d}_days"] = {"avg": avg, "var": var, "count": len(ratings)} return stats @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 # 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 placeholders = ",".join("?" for _ in active_roster_ids) rows = query_db("l3", f"SELECT * FROM dm_player_features WHERE steam_id_64 IN ({placeholders})", active_roster_ids) if not rows: return None stats_map = {str(row["steam_id_64"]): FeatureService._normalize_features(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] = {} metrics = [ # TIER 1: CORE # Basic Performance "core_avg_rating", "core_avg_rating2", "core_avg_kd", "core_avg_adr", "core_avg_kast", "core_avg_rws", "core_avg_hs_kills", "core_hs_rate", "core_total_kills", "core_total_deaths", "core_total_assists", "core_avg_assists", "core_kpr", "core_dpr", "core_survival_rate", # Match Stats "core_win_rate", "core_wins", "core_losses", "core_avg_match_duration", "core_avg_mvps", "core_mvp_rate", "core_avg_elo_change", "core_total_elo_gained", # Weapon Stats "core_avg_awp_kills", "core_awp_usage_rate", "core_avg_knife_kills", "core_avg_zeus_kills", "core_zeus_buy_rate", "core_top_weapon_kills", "core_top_weapon_hs_rate", "core_weapon_diversity", "core_rifle_hs_rate", "core_pistol_hs_rate", "core_smg_kills_total", # Objective Stats "core_avg_plants", "core_avg_defuses", "core_avg_flash_assists", "core_plant_success_rate", "core_defuse_success_rate", "core_objective_impact", # TIER 2: TACTICAL # Opening Impact "tac_avg_fk", "tac_avg_fd", "tac_fk_rate", "tac_fd_rate", "tac_fk_success_rate", "tac_entry_kill_rate", "tac_entry_death_rate", "tac_opening_duel_winrate", # Multi-Kill "tac_avg_2k", "tac_avg_3k", "tac_avg_4k", "tac_avg_5k", "tac_multikill_rate", "tac_ace_count", # Clutch Performance "tac_clutch_1v1_attempts", "tac_clutch_1v1_wins", "tac_clutch_1v1_rate", "tac_clutch_1v2_attempts", "tac_clutch_1v2_wins", "tac_clutch_1v2_rate", "tac_clutch_1v3_plus_attempts", "tac_clutch_1v3_plus_wins", "tac_clutch_1v3_plus_rate", "tac_clutch_impact_score", # Utility Mastery "tac_util_flash_per_round", "tac_util_smoke_per_round", "tac_util_molotov_per_round", "tac_util_he_per_round", "tac_util_usage_rate", "tac_util_nade_dmg_per_round", "tac_util_nade_dmg_per_nade", "tac_util_flash_time_per_round", "tac_util_flash_enemies_per_round", "tac_util_flash_efficiency", "tac_util_smoke_timing_score", "tac_util_impact_score", # Economy Efficiency "tac_eco_dmg_per_1k", "tac_eco_kpr_eco_rounds", "tac_eco_kd_eco_rounds", "tac_eco_kpr_force_rounds", "tac_eco_kpr_full_rounds", "tac_eco_save_discipline", "tac_eco_force_success_rate", "tac_eco_efficiency_score", # TIER 3: INTELLIGENCE # High IQ Kills "int_wallbang_kills", "int_wallbang_rate", "int_smoke_kills", "int_smoke_kill_rate", "int_blind_kills", "int_blind_kill_rate", "int_noscope_kills", "int_noscope_rate", "int_high_iq_score", # Timing Analysis "int_timing_early_kills", "int_timing_mid_kills", "int_timing_late_kills", "int_timing_early_kill_share", "int_timing_mid_kill_share", "int_timing_late_kill_share", "int_timing_avg_kill_time", "int_timing_early_deaths", "int_timing_early_death_rate", "int_timing_aggression_index", "int_timing_patience_score", "int_timing_first_contact_time", # Pressure Performance "int_pressure_comeback_kd", "int_pressure_comeback_rating", "int_pressure_losing_streak_kd", "int_pressure_matchpoint_kpr", "int_pressure_matchpoint_rating", "int_pressure_clutch_composure", "int_pressure_entry_in_loss", "int_pressure_performance_index", "int_pressure_big_moment_score", "int_pressure_tilt_resistance", # Position Mastery "int_pos_site_a_control_rate", "int_pos_site_b_control_rate", "int_pos_mid_control_rate", "int_pos_position_diversity", "int_pos_rotation_speed", "int_pos_map_coverage", "int_pos_lurk_tendency", "int_pos_site_anchor_score", "int_pos_entry_route_diversity", "int_pos_retake_positioning", "int_pos_postplant_positioning", "int_pos_spatial_iq_score", "int_pos_avg_distance_from_teammates", # Trade Network "int_trade_kill_count", "int_trade_kill_rate", "int_trade_response_time", "int_trade_given_count", "int_trade_given_rate", "int_trade_balance", "int_trade_efficiency", "int_teamwork_score", # TIER 4: META # Stability "meta_rating_volatility", "meta_recent_form_rating", "meta_win_rating", "meta_loss_rating", "meta_rating_consistency", "meta_time_rating_correlation", "meta_map_stability", "meta_elo_tier_stability", # Side Preference "meta_side_ct_rating", "meta_side_t_rating", "meta_side_ct_kd", "meta_side_t_kd", "meta_side_ct_win_rate", "meta_side_t_win_rate", "meta_side_ct_fk_rate", "meta_side_t_fk_rate", "meta_side_ct_kast", "meta_side_t_kast", "meta_side_rating_diff", "meta_side_kd_diff", "meta_side_balance_score", # Opponent Adaptation "meta_opp_vs_lower_elo_rating", "meta_opp_vs_similar_elo_rating", "meta_opp_vs_higher_elo_rating", "meta_opp_vs_lower_elo_kd", "meta_opp_vs_similar_elo_kd", "meta_opp_vs_higher_elo_kd", "meta_opp_elo_adaptation", "meta_opp_stomping_score", "meta_opp_upset_score", "meta_opp_consistency_across_elos", "meta_opp_rank_resistance", "meta_opp_smurf_detection", # Map Specialization "meta_map_best_rating", "meta_map_worst_rating", "meta_map_diversity", "meta_map_pool_size", "meta_map_specialist_score", "meta_map_versatility", "meta_map_comfort_zone_rate", "meta_map_adaptation", # Session Pattern "meta_session_avg_matches_per_day", "meta_session_longest_streak", "meta_session_weekend_rating", "meta_session_weekday_rating", "meta_session_morning_rating", "meta_session_afternoon_rating", "meta_session_evening_rating", "meta_session_night_rating", # TIER 5: COMPOSITE "score_aim", "score_clutch", "score_pistol", "score_defense", "score_utility", "score_stability", "score_economy", "score_pace", "score_overall", "tier_percentile", # Legacy Mappings (keep for compatibility if needed, or remove if fully migrated) "basic_avg_rating", "basic_avg_kd", "basic_avg_adr", "basic_avg_kast", "basic_avg_rws", ] lower_is_better = [] result = {} for m in metrics: values = [] non_numeric = False for p in stats_map.values(): raw = (p or {}).get(m) if raw is None: raw = 0 try: values.append(float(raw)) except Exception: non_numeric = True break raw_target = (stats_map.get(target_steam_id) or {}).get(m) if raw_target is None: raw_target = 0 try: target_val = float(raw_target) except Exception: non_numeric = True target_val = 0 if non_numeric: result[m] = None continue if not values: result[m] = None continue # Sort: Reverse (High to Low) by default, unless in lower_is_better is_reverse = m not in lower_is_better values.sort(reverse=is_reverse) # 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), 'inverted': not is_reverse # Flag for frontend to invert bar } 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