Description
Build analysis tooling that consumes aggregated sweep data and generates actionable balance reports identifying overpowered/underpowered mechanics, dominant strategies, unused content, and parameter sensitivity. Extend existing analyze_ai_games.py functionality with statistical rigor and trend detection.
Acceptance Criteria
Priority
High
Dependencies
- Task 11.2.1 (Result Aggregation and Storage) - ✅ COMPLETED
- Task 9.4.1 (AI Tournament Analysis Script) - ✅ COMPLETED
Risks & Mitigations
- Risk: Statistical tests produce false positives
- Mitigation: Use appropriate significance thresholds and multiple comparison corrections
- Risk: Reports become too verbose
- Mitigation: Summary-first design with detailed breakdowns in appendices
Next Steps
- Define report structure and key metrics to surface
- Implement statistical analysis functions (win rate deltas, significance tests, trend detection)
- Add visualization generation (matplotlib/plotly for charts)
- Create test suite with synthetic sweep data
Reference
See .pm/tracker.md task 11.3.1 for full details.
Description
Build analysis tooling that consumes aggregated sweep data and generates actionable balance reports identifying overpowered/underpowered mechanics, dominant strategies, unused content, and parameter sensitivity. Extend existing
analyze_ai_games.pyfunctionality with statistical rigor and trend detection.Acceptance Criteria
scripts/analyze_balance.pyprocesses aggregated sweep results and produces HTML or Markdown balance reportsPriority
High
Dependencies
Risks & Mitigations
Next Steps
Reference
See
.pm/tracker.mdtask 11.3.1 for full details.