Player Health & Injury Analytics

# Player Health & Injury Analytics Data-driven approaches to injury prediction and prevention can reduce injury rates and optimize player availability. ## Injury Risk Modeling ### Predictive Model ```python from sklearn.ensemble import GradientBoostingClassifier import pandas as pd # Features for injury prediction features = [ 'age', 'minutes_played', 'sprint_count', 'acceleration_load', 'days_since_injury', 'training_load', 'match_density' ] X = player_data[features] y = player_data['injury_next_week'] # Train injury risk model model = GradientBoostingClassifier(n_estimators=200, max_depth=5) model.fit(X, y) # Predict injury risk risk_scores = model.predict_proba(current_squad[features])[:, 1] high_risk = current_squad[risk_scores > 0.3] ``` ### Load Monitoring ```r library(ggplot2) library(dplyr) # Calculate acute:chronic workload ratio calculate_acwr <- function(training_data) { training_data %>% group_by(player_id) %>% arrange(date) %>% mutate( acute_load = zoo::rollmean(training_load, k = 7, fill = NA), chronic_load = zoo::rollmean(training_load, k = 28, fill = NA), acwr = acute_load / chronic_load ) %>% filter(acwr > 1.5) # High injury risk threshold } at_risk <- calculate_acwr(training_data) ``` ## Prevention Strategies - Workload management systems - Recovery optimization - Biomechanical screening - GPS-based load monitoring