Athlete Injury And Performance Dataset

This dataset is designed to analyze the impact of complex scheduling algorithms on injury rates and athletic performance in a collegiate sports environment. It provides synthetic but realistic data for athletes, capturing their demographics, training regimes, schedules, fatigue levels, and injury risks.

Features Overview

1. Athlete Information
   Athlete_ID: Unique identifier for each athlete (e.g., A001, A002).
   Age: Athlete's age (18–25 years).
   Gender: Gender of the athlete (Male/Female).
   Height_cm: Height of the athlete in centimeters (160–200 cm).
   Weight_kg: Weight of the athlete in kilograms (55–100 kg).
   Position: Playing position in the team (Guard, Forward, Center).
2. Training Information
   Training_Intensity: Average intensity of training sessions on a scale of 1 (low) to 10 (high).
   Training_Hours_Per_Week: Total hours of training per week (5–20 hours).
   Recovery_Days_Per_Week: Number of days dedicated to recovery per week (1–3 days).
3. Schedule Information
   Match_Count_Per_Week: Number of matches scheduled per week (1–4 matches).
   Rest_Between_Events_Days: Average rest days between matches (1–3 days).
4. Derived Features
   Load_Balance_Score: A calculated score (0–100) indicating the balance between training load and recovery. A higher score reflects a better balance.
   ACL_Risk_Score: Predicted risk score (0–100) for ACL injuries. A higher score indicates a greater risk of injury.
5. Injury Information
   Injury_Indicator: Target column indicating whether the athlete sustained an ACL injury (1 = Yes, 0 = No).
6. Performance Metrics
   Fatigue_Score: Subjective fatigue level on a scale of 1 (low) to 10 (high).
   Performance_Score: Composite performance score (50–100) based on metrics like points scored and assists.
   Team_Contribution_Score: Athlete’s overall contribution to the team’s success on a scale of 50–100.