WomensFootBallAnalytics

A machine learning project focused on women’s football injury risk prediction using player workload, recovery metrics, and performance data.
The system analyzes key factors such as training intensity, workload-recovery balance, and player condition to help identify potential injury risks and support better decision-making for coaches and analysts.

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Overview

This project aims to use data analytics + machine learning to study injury risk in women’s football.
By collecting and processing football-related data, the model predicts whether a player may be at a higher risk of injury based on measurable workload and recovery indicators.

The project is divided into two major parts:

• DataScraper → Collects and prepares football-related data
• Injury_Prediction → Builds and evaluates the machine learning model for injury prediction

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Objectives

• Analyze player workload and recovery patterns
• Identify important injury risk indicators
• Build a predictive model using XGBoost
• Support injury prevention and performance management
• Provide actionable insights for coaches, analysts, and sports scientists

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Project Structure

WomensFootBallAnalytics/
│
├── DataScraper/           # Scripts for collecting / scraping football-related data
├── Injury_Prediction/     # ML model, preprocessing, training, and evaluation
└── README.md              # Project documentation