Here’s a comprehensive, step-by-step guide to for machine learning, replay analysis, or build order mining. 1. Understanding SC2 Data Sources You have three primary sources of game data:

import numpy as np state_data = [] timeline = np.arange(0, replay.real_length.seconds, 5)

build_order_vector = [] for second in [60, 120, 180, 240, 300]: units_at_time = [e for e in replay.events if e.second <= second and e.name == 'UnitBornEvent'] build_order_vector.append(len([u for u in units_at_time if 'Zergling' in u.unit_type_name])) Goal: Predict race & opening from first 3 minutes. Extraction Code import sc2reader import pandas as pd replay = sc2reader.load_file("replay.SC2Replay")

for player in replay.players: print(f"player.name (player.race) – MMR: player.mmr") Extract unit events, upgrades, resources, and positions:

Example save:

data = [] for event in replay.events: if event.name in ['UnitBornEvent', 'UpgradeCompleteEvent'] and event.second <= 180: data.append( 'time': event.second, 'type': event.name, 'unit': getattr(event, 'unit_type_name', None), 'upgrade': getattr(event, 'upgrade_type_name', None), 'player_race': event.player.play_race, 'winner': 1 if event.player == replay.winner else 0 )

| Source | Format | Use Case | |--------|--------|----------| | | Binary / MPQ archive | Full game state reconstruction, player actions, timings | | Live game state (via API) | JSON (via SC2API) | Real-time bot development, decision-making models | | Match history (Blizzard API) | JSON | Win rates, map stats, ladder ranking |

import pandas as pd actions = [] for event in replay.events: if hasattr(event, 'second'): actions.append( 'time_sec': event.second, 'event_type': event.name, 'player': getattr(event, 'player', None), 'unit_type': getattr(event, 'unit_type_name', None), 'position': getattr(event, 'location', None) ) df = pd.DataFrame(actions) Create a time-aligned representation: every 5 seconds, record game state (supply, workers, army, buildings, resources).

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