Add CombatDeaths []int field to MatchResult to track combat density
per player. This enables monitoring of focus-fire combat across all
matches and helps verify that the zone forcing function is working.
Changes:
- Add CombatDeaths []int to MatchResult struct
- Add CombatDeaths []int to GameState for tracking during match
- Increment combat death count for each killer in executeCombat
- Populate combat_deaths in final match result
- Update tests to include CombatDeaths in MatchResult
Verified: 6-player match shows combat_deaths: [1,1,1,1,1,1] (each
player killed 1 bot in mutual combat).
Closes: bf-4fez
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Fix computeCombatTurns to count EventCombatDeath events instead of
EventBotDied with reason="combat" (which was never emitted, causing
CombatTurns to always be 0)
- Add CombatDeaths field to MapEngagementScore to track focus-fire kills
- Update engagement formula to weight combat deaths at 3.0 (same as
win_prob_crossings) to bias map evolution toward combat-dense maps
- Add countCombatDeaths helper function to count EventCombatDeath events
- Update log output to include combat_deaths metric
This implements bf-4nxs: the combat-density metric is now measured and
weighted in map engagement, which gates map curation/selection. Maps
with zero combat will have low engagement scores and be filtered out.
Closes: bf-4nxs
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 map_engagement_test.go with tests for:
- Win prob dependency in map engagement (lead changes counted)
- Critical moments dependency in engagement score
- Empty/nil replay handling
- Complete ComputeWinProbability + SetWinProbability flow
This confirms the existing implementation already correctly:
- Computes win probability via Monte Carlo rollout (100 iterations)
- Sets win_prob and critical_moments on replay before serialization
- Calculates map engagement score from win_prob_crossings and critical_moments
- Writes engagement score to maps table via UpdateMapEngagement
Task: bf-qps
