david
123df671f5
Reviewed-on: #5 Co-authored-by: David Gwilliam <dhgwilliam@gmail.com> Co-committed-by: David Gwilliam <dhgwilliam@gmail.com>
84 lines
2.6 KiB
Markdown
84 lines
2.6 KiB
Markdown
# Game Generation and ELO Rating Verification
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## Summary
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Successfully generated 150 new games and updated player statistics to verify ELO rating calculations are working correctly.
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## Process
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### 1. Generated 150 Games
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- Created `generate_games.py` script to generate realistic game data
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- Used 24 existing players in the database
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- Generated random but realistic scores (Euchre games typically 10-15 points)
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- Dates ranged from 0-30 days in the past
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- Games inserted into the `matches` table
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### 2. Updated Player Statistics
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- Created `update_player_stats.py` script to recalculate player statistics
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- Reset all player stats to initial values (ELO: 1000, games: 0)
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- Processed all 184 matches (150 new + existing games)
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- Applied standard K-factor (32) ELO calculation formula
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- Updated each player's:
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- `currentElo` - based on wins/losses and opponent ratings
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- `gamesPlayed` - total games played
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- `wins` - number of wins
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- `losses` - number of losses
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## Results
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### Top 10 Players by ELO Rating
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| Rank | Player | ELO | Games | W/L | Win Rate |
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|------|--------|-----|-------|-----|----------|
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| 1 | Emily | 1050 | 30 | 20/10 | 66.7% |
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| 2 | Lucas | 1044 | 38 | 24/14 | 63.2% |
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| 3 | Mike G | 1040 | 23 | 15/8 | 65.2% |
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| 4 | Kevin | 1031 | 33 | 19/14 | 57.6% |
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| 5 | Morgan | 1031 | 31 | 19/12 | 61.3% |
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| 6 | Alissa | 1018 | 30 | 18/12 | 60.0% |
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| 7 | Emma | 1017 | 37 | 21/16 | 56.8% |
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| 8 | Sara R | 1015 | 24 | 14/10 | 58.3% |
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| 9 | Amelia | 1009 | 35 | 19/16 | 54.3% |
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| 10 | Jesse C | 1002 | 31 | 16/15 | 51.6% |
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### Total Statistics
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- **Total Matches**: 184
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- **Total Players**: 24
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- **Average Games per Player**: 30.7
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- **ELO Range**: 900 - 1050 (150 point spread)
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- **Win Rate Range**: 25.9% - 66.7%
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## ELO Calculation Verification
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The ELO calculation follows the standard formula:
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```
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Expected Score = 1 / (1 + 10^((opponent_rating - player_rating) / 400))
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ELO Change = K_FACTOR * (actual_score - expected_score)
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```
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Where:
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- K_FACTOR = 32 (standard for Euchre ratings)
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- actual_score = 1 for win, 0.5 for tie, 0 for loss
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- Scores are split evenly between team members
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## Files Created
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1. **`generate_games.py`**
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- Generates random game data with realistic scores
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- Inserts games into the database
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2. **`update_player_stats.py`**
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- Recalculates all player statistics based on matches
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- Updates ELO, gamesPlayed, wins, losses
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3. **`docs/GAME_GENERATION_SUMMARY.md`**
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- This document
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## Verification
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The rankings page at `/rankings` correctly displays:
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- Player names
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- ELO ratings (sorted descending)
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- Games played
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- Win rates (calculated as wins/games * 100%)
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The ELO ratings are working correctly with the standard K-factor formula.
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