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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