Basketball analytics: the use of data science and machine learning techniques to improve teams management
Martinese, Marco (A.A. 2022/2023) Basketball analytics: the use of data science and machine learning techniques to improve teams management. Tesi di Laurea in Big data and smart data analytics, Luiss Guido Carli, relatore Irene Finocchi, pp. 94. [Master's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Literature review. Historical background. Related work. Main research topics. NBA draft 2023 analysis. Best scorers in NBA 2023 draft. Generating stats-based historical comparisons for the draft lottery. Predict All-Stars from the 2023 draft. Salary prediction for NBA free agents. Motivation. Background. Data sources. Methodology. Error analysis. Clustering NBA players into offensive roles based on playtype stats. Methodology. Results.
References
Bibliografia: pp. 86-87.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Big data and smart data analytics |
| Thesis Supervisor: | Finocchi, Irene |
| Thesis Co-Supervisor: | Sinaimeri, Blerina |
| Academic Year: | 2022/2023 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 11 Jan 2024 16:34 |
| Last Modified: | 11 Jan 2024 16:34 |
| URI: | https://tesi.luiss.it/id/eprint/37523 |
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