Basketball Performance Prediction Models and Team Efficiency Factors

Nousher, Karthik (2023) Basketball Performance Prediction Models and Team Efficiency Factors. Masters thesis, Dublin, National College of Ireland.

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Abstract

The project revolves around the performance-increasing factors that contribute to the efficiency of a basketball team. The study uses various algorithms primarily linear regression, random forest and decision tree, later on using the algorithm with the highest efficiency to predict the team efficiency. Here, Linear regression showed a higher efficiency value. the study critically examines different prediction models, in which it highlights the impact of linear regression, having a low MSE of 130.736, RME of 130.736 and an MAE of 9.385. The main idea of his research is to contribute to the sports world, thereby helping the team to win the game. The study also addresses some of the key elements in the basketball game such as player efficiency ratings, shooting accuracy, defensive ability, and team management. Coaches and analysts can optimize player roles, devise winning strategies, and improve overall team performance. This approach reduces the gap between data analytics and basketball performance evaluation. Knowing the factors that affect the player’s efficiency and also understanding the best-fit model are some of the important factors that are needed to advance the basketball game. This research provides some valuable insights that can be used to optimize player roles, develop effective strategies, and elevate total team performance.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Mulwa, Catherine UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning G Geography. Anthropology. Recreation > GV Recreation Leisure > Sports
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	18 May 2025 14:59
Last Modified:	18 May 2025 14:59
URI:	https://norma.ncirl.ie/id/eprint/7579

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