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A Multi-Stage Clustering Algorithm to Re-Evaluate Basketball Positions and Performance Analysis

Baumann, Andrew (2022) A Multi-Stage Clustering Algorithm to Re-Evaluate Basketball Positions and Performance Analysis. Masters thesis, Dublin, National College of Ireland.

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Availing of computer-based methods of statistical analysis and machine learning has a heavy and budding presence in sport. Data collection and analysis is growing more and more with every passing year, with exciting times lying ahead for data analytics within sport. However, the repetition and staleness are clear to see, and the area is in need of new and innovative approaches. In this report, it is argued that one machine learning methodology remains underutilised, despite displaying opportunity to aid research and the relationship between machine learning and sport.

It is presented in this report that the five traditional playing positions in basketball are outdated for the modern game. The clustering of players based on their performance output and thus their specific skill set proposes a unique approach in fitting an athlete to a position more suitably describing the modern game. New groupings based on a novel multi-level clustering algorithm methodology are proposed in order to better classify and rate players skillset, also allow for ranking of said players and thus assisting in the decision-making and planning for basketball teams, and an overall redefinition of how positions and players are seen in the sport.

Item Type: Thesis (Masters)
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: Tamara Malone
Date Deposited: 18 Jan 2023 16:13
Last Modified: 06 Mar 2023 17:02

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