About 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

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 in centimeters (160–200 cm)
• Weight_kg: Weight in kilograms (55–100 kg)
• Position: Playing position (Guard, Forward, Center)

Training Information

• Training_Intensity: Average session intensity (1 = low, 10 = high)
• Training_Hours_Per_Week: Total training hours per week (5–20 hours)
• Recovery_Days_Per_Week: Recovery days per week (1–3 days)

Schedule Information

• Match_Count_Per_Week: Matches per week (1–4 matches)
• Rest_Between_Events_Days: Average rest days between matches (1–3 days)

Derived Features

• Load_Balance_Score: Score (0–100) indicating the balance between training and recovery
• ACL_Risk_Score: Predicted risk score (0–100) for ACL injury

Injury Information

• Injury_Indicator: 1 if athlete sustained an ACL injury, 0 otherwise

Performance Metrics

• Fatigue_Score: Subjective fatigue level (1 = low, 10 = high)
• Performance_Score: Composite score (50–100) based on points, assists, etc.
• Team_Contribution_Score: Overall contribution to team success (50–100)