Higher dimensional sports statistics and real-time game prediction

Abstract

The rapid expansion of comprehensive sports datasets and the successful application of data mining techniques in various domains have given rise to the emergence of sports data prediction techniques. These techniques enable the extraction of hidden knowledge that can significantly impact the sports industry, as more and more clubs are using Machine Learning (ML) and Deep Learning (DL) methods to manage athletes and training. In this research, the focusing and intriguing aspects is predicting the outcomes of a specific basketball athletes, which has garnered significant attention for research. The paper was motivated by a dual interest in college and NBA basketball matches, alongside a keen observation of the evolving strategies employed by coaches in athlete management. Additionally, the interest was further reinforced by firsthand observations of such evolving methods during a baseball game at City Field in New York. These factors collectively underpin the relevance and significance of this research endeavor, highlighting the intersection of personal interest and the evolving landscape of sports management as compelling reasons for its pursuit. In the process of data selection, we acquired data from previously published essays as well as from Kaggle, a reputable online platform. Following this, we proceeded to evaluate several prominent machine learning models, namely Linear Regression, KNN, Gradient Boosting, Elastic Net, and Lasso, to ascertain their effectiveness in predicting the performance of specific players. Through rigorous analysis and comparison, we concluded that Linear Regression and Gradient Boosting exhibited superior predictive capabilities compared to the other models considered. These two models demonstrated a higher degree of accuracy and reliability in forecasting player performance, thus establishing them as the most suitable choices for our predictive modeling purposes. This meticulous selection process, involving both data acquisition and model evaluation, forms the foundation of our research methodology and underscores the rigor and precision with which our conclusions are drawn.