Update the map engagement scoring formula to match plan §14.6:
- score = win_prob_crossings * 3.0 + critical_moments * 2.0 +
resource_contest_turns * 1.5 + survival_turns * 0.5
New metrics computed from replay data:
- resource_contest_turns: turns where energy is contested by multiple players
- survival_turns: turns where all players have at least one bot alive
The old formula used map_coverage_pct, closeness, and turn_pct which
did not match the specification.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Add engine.CalculateMapEngagement() to compute map engagement scores from replay data (win_prob_crossings, critical_moments, map_coverage_pct, closeness, turn_pct)
- Add DBClient.UpdateMapEngagement() to update map engagement using rolling average
- Worker now calculates and writes map engagement scores after each match
- Add test to verify win_prob array is non-empty in produced replays
This implements the win probability Monte Carlo array storage in replay JSON
feature. The engine already called ComputeWinProbability() in MatchRunner.Run(),
so this commit adds the missing map engagement tracking.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
