A comprehensive R tutorial on applying Survival Analysis to sports data, moving beyond simple binary classification for injury prediction. This analysis simulates a 25-man football squad over 4 seasons to model the "time-to-event" for non-contact soft tissue injuries. It covers the creation of Kaplan-Meier survival curves, multivariate Cox Proportional Hazards regression, and the interpretation of Hazard Ratios to quantify the cost of workload and squad rotation.

This is a beginner-friendly, step-by-step guide to building a modern Expected Threat (xT) model. It will show you how to use a Random Forest in regression mode to predict a continuous xT_value. This tutorial moves beyond simple, static zones by incorporating dynamic predictors like defensive pressure and density to create a more realistic model. You'll learn the complete ML workflow: creating mock data, splitting into training/testing sets, building and tuning the randomForest model, and interpreting the results with caret and varImpPlot.

This is a detailed case study on predicting fan attendance on matchdays for the Singapore Premier League (SPL). It is intended as a complete tutorial for R beginners and aspiring analysts. Following a 9-step process, this guide provides all the code and instructions needed to: Create a mock dataset for analysis. Build a predictive model using linear regression. Use stepwise selection (AIC) to find the best-fitting model. Validate the model's performance on a test set. Statistically determine which factors (e.g., stadium capacity, match importance, team popularity) are the most significant drivers of attendance.