This report applies multiple linear regression to examine key factors influencing basketball player scoring performance. Using variables such as minutes played, field goals, assists, rebounds, and turnovers, the model explains approximately 69% of the variation in total points scored. Results show that offensive activity and efficiency measures strongly predict scoring outcomes, while turnovers have a negative effect. The findings provide practical insights for coaches and analysts aiming to improve player performance and team success.

A step-by-step tutorial on forecasting team performance using ARIMA and ETS time series models. It covers data setup, model fitting, forecasting, and accuracy evaluation, helping analysts apply time series methods to sports performance prediction.