Machine learning predictive analytics for player movement prediction in NBA: Applications, opportunities, and challenges

Dembe Koi Stephanos; Ghaith Husari; Brian T. Bennett; Emma Stephanos

doi:10.1145/3409334.3452064

Machine learning predictive analytics for player movement prediction in NBA: Applications, opportunities, and challenges

Dembe Koi Stephanos
, Ghaith Husari
, Brian T. Bennett
, Emma Stephanos

Department of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	Proceedings of the 2021 ACMSE Conference - ACMSE 2021
Subtitle of host publication	The Annual ACM Southeast Conference
Publisher	Association for Computing Machinery, Inc
Pages	2-8
Number of pages	7
ISBN (Electronic)	9781450380683
DOIs	https://doi.org/10.1145/3409334.3452064
State	Published - Apr 15 2021
Event	2021 ACM Southeast Conference, ACMSE 2021 - Virtual, Online, United States Duration: Apr 15 2021 → Apr 17 2021

Publication series

Name	Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference

Conference

Conference	2021 ACM Southeast Conference, ACMSE 2021
Country/Territory	United States
City	Virtual, Online
Period	4/15/21 → 4/17/21

ASJC Scopus Subject Areas

Computational Theory and Mathematics
Computer Science Applications
Hardware and Architecture
Software

Keywords

Action recognition
Clustering
Data mining
Machine learning
NBA video analysis
Predictive analytics
Survey

Access to Document

10.1145/3409334.3452064

Cite this

Stephanos, D. K., Husari, G., Bennett, B. T., & Stephanos, E. (2021). Machine learning predictive analytics for player movement prediction in NBA: Applications, opportunities, and challenges. In Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference (pp. 2-8). (Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3409334.3452064

Machine learning predictive analytics for player movement prediction in NBA: Applications, opportunities, and challenges. / Stephanos, Dembe Koi; Husari, Ghaith ; Bennett, Brian T. et al.
Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference. Association for Computing Machinery, Inc, 2021. p. 2-8 (Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Stephanos, DK, Husari, G , Bennett, BT & Stephanos, E 2021, Machine learning predictive analytics for player movement prediction in NBA: Applications, opportunities, and challenges. in Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference. Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference, Association for Computing Machinery, Inc, pp. 2-8, 2021 ACM Southeast Conference, ACMSE 2021, Virtual, Online, United States, 4/15/21. https://doi.org/10.1145/3409334.3452064

Stephanos DK, Husari G , Bennett BT, Stephanos E. Machine learning predictive analytics for player movement prediction in NBA: Applications, opportunities, and challenges. In Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference. Association for Computing Machinery, Inc. 2021. p. 2-8. (Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference). doi: 10.1145/3409334.3452064

Stephanos, Dembe Koi ; Husari, Ghaith ; Bennett, Brian T. et al. / Machine learning predictive analytics for player movement prediction in NBA : Applications, opportunities, and challenges. Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference. Association for Computing Machinery, Inc, 2021. pp. 2-8 (Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference).

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title = "Machine learning predictive analytics for player movement prediction in NBA: Applications, opportunities, and challenges",

keywords = "Action recognition, Clustering, Data mining, Machine learning, NBA video analysis, Predictive analytics, Survey",

author = "Stephanos, \{Dembe Koi\} and Ghaith Husari and Bennett, \{Brian T.\} and Emma Stephanos",

note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 2021 ACM Southeast Conference, ACMSE 2021 ; Conference date: 15-04-2021 Through 17-04-2021",

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Machine learning predictive analytics for player movement prediction in NBA: Applications, opportunities, and challenges

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this