Match Predictor

This document explains the working of the Match Predictor tool, which uses advanced analytics and machine learning to predict match outcomes and provide strategic insights.

Overview

The Match Predictor tool analyzes scouting data to predict match outcomes and evaluate team performance. It combines traditional statistical analysis with machine learning to provide accurate predictions and insights.

Core Features

1. Data Collection and Management

• Supports CSV and JSON data formats
• QR code scanner integration for rapid data collection
• Data merging and format conversion capabilities
• Automatic backup system for data safety

2. Basic Score Calculations

The total score $S_{total}$ for a team is calculated as:

Autonomous Score

The autonomous score $S_{auton}$ is calculated as:

Teleop Score

The teleop score $S_{teleop}$ is calculated as:

Endgame Score

The endgame score $S_{endgame}$ is calculated as:

Defense Value

The defense value $S_{defense}$ is a binary score:

3. Machine Learning Prediction

The tool uses an XGBoost model for advanced match prediction, considering:

• Historical team performance
• Feature importance analysis
• Cross-validation for accuracy
• Probability-based predictions

Model Features

• auton_total: Autonomous performance
• teleop_total: Teleoperated performance
• endgame_total: Endgame performance
• defense_value: Defense capability

The model provides win probabilities for each alliance based on these metrics.

4. Team Analysis

Performance Metrics

• Match count and consistency rating
• Average scores by phase (autonomous, teleop, endgame)
• Climbing success percentage
• Defense participation rate

External Data Integration

• Statbotics API integration for historical statistics
• The Blue Alliance API for team information
• Win/loss records and EPA (Estimated Prediction Accuracy)

5. Alliance Selection Tools

The tool provides several alliance selection features:

• Best teams by category (autonomous, teleop, endgame)
• Team compatibility analysis
• Versatility rankings
• Defense capability assessment

6. Data Visualization

The tool generates various visualizations:

• Team performance breakdowns
• Consistency ratings
• Alliance comparisons
• Historical trend analysis

Example visualization:

7. Advanced Features

Sentiment Analysis

• Analyzes scout comments using natural language processing
• Classifies feedback as positive or negative
• Provides sentiment-based team insights

Team Filtering

• Custom filtering criteria
• Match count thresholds
• Score-based filtering
• Defense capability filtering

Team Search

• Multi-criteria search capabilities
• Performance-based filtering
• Match history analysis
• Detailed team profiles

Usage Examples

Basic Match Prediction

Red Alliance: Teams 1234, 5678, 9012
Blue Alliance: Teams 2345, 6789, 0123

Prediction Results:
- Red Alliance: 75% win probability
- Blue Alliance: 25% win probability

Alliance Selection

Top Autonomous Teams:
1. Team A (avg: 15 pts)
2. Team B (avg: 14 pts)
3. Team C (avg: 13 pts)

Best Overall Performance:
1. Team X (consistency: 0.95)
2. Team Y (consistency: 0.92)
3. Team Z (consistency: 0.90)

Conclusion

The Match Predictor combines traditional scouting metrics with advanced analytics and machine learning to provide comprehensive team analysis and match predictions. By analyzing multiple aspects of team performance and using both historical and current data, it helps teams make informed strategic decisions during competitions.