PREDICTION OF THE NUMBER OF INDEX POINTS OF PLAYERS IN THE ABA LEAGUE WITH A FOCUS ON DATA COLLECTION AND EXPLORATORY ANALYSIS

Abstract

This paper presents the machine learning approach to automatically predict the number of index points that a player achieves in a basketball game. The focus of the work is on data collection and exploratory analysis. Data was collected from eurobasket.com using web-scraping techniques. After cleaning the data set, feature extraction and exploratory data analysis were performed. The prediction was made using three different regressors: Lasso, Random Forest, and LightGBM By optimizing the hyperparameters of these algorithms' implementations, we came up with models that were used to predict the number of index points. The LASSO regression model achieved the best results with the mean absolute error MAE = 5.617. The paper proposes further improvements of the data set as future work.