NBA Results Forecast: From League Dynamics Analysis to Predictive Model Implementation
This study presents a machine learning-based approach to predicting the outcosmes of NBA games, with the aim of enhancing decision-making in sports betting and performance analysis. Using a dataset spanning 20 NBA seasons (2003–2023), we incorporated key features such as team statistics, player perf...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Sciendo
2025-05-01
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| Series: | International Journal of Computer Science in Sport |
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| Online Access: | https://doi.org/10.2478/ijcss-2025-0006 |
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| _version_ | 1850271390714822656 |
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| author | Rodrigues F Pires F |
| author_facet | Rodrigues F Pires F |
| author_sort | Rodrigues F |
| collection | DOAJ |
| description | This study presents a machine learning-based approach to predicting the outcosmes of NBA games, with the aim of enhancing decision-making in sports betting and performance analysis. Using a dataset spanning 20 NBA seasons (2003–2023), we incorporated key features such as team statistics, player performance metrics, and external factors like team fatigue and rankings. The methodology followed the CRISP-DM process, involving data preprocessing, feature selection, and model evaluation. |
| format | Article |
| id | doaj-art-9c5ee1a0ebaa4fb6b1c4b9cc66e1fa02 |
| institution | OA Journals |
| issn | 1684-4769 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Computer Science in Sport |
| spelling | doaj-art-9c5ee1a0ebaa4fb6b1c4b9cc66e1fa022025-08-20T01:52:15ZengSciendoInternational Journal of Computer Science in Sport1684-47692025-05-012419411510.2478/ijcss-2025-0006NBA Results Forecast: From League Dynamics Analysis to Predictive Model ImplementationRodrigues F0Pires F1ISEP, Polytechnic of Porto, R. Dr. Aº Bernardino de Almeida, 431, Porto, 4249-015, Portugal.ISEP, Polytechnic of Porto, R. Dr. Aº Bernardino de Almeida, 431, Porto, 4249-015, Portugal.This study presents a machine learning-based approach to predicting the outcosmes of NBA games, with the aim of enhancing decision-making in sports betting and performance analysis. Using a dataset spanning 20 NBA seasons (2003–2023), we incorporated key features such as team statistics, player performance metrics, and external factors like team fatigue and rankings. The methodology followed the CRISP-DM process, involving data preprocessing, feature selection, and model evaluation.https://doi.org/10.2478/ijcss-2025-0006nbamachine learninggame outcome predictionfeature selectionclassification |
| spellingShingle | Rodrigues F Pires F NBA Results Forecast: From League Dynamics Analysis to Predictive Model Implementation International Journal of Computer Science in Sport nba machine learning game outcome prediction feature selection classification |
| title | NBA Results Forecast: From League Dynamics Analysis to Predictive Model Implementation |
| title_full | NBA Results Forecast: From League Dynamics Analysis to Predictive Model Implementation |
| title_fullStr | NBA Results Forecast: From League Dynamics Analysis to Predictive Model Implementation |
| title_full_unstemmed | NBA Results Forecast: From League Dynamics Analysis to Predictive Model Implementation |
| title_short | NBA Results Forecast: From League Dynamics Analysis to Predictive Model Implementation |
| title_sort | nba results forecast from league dynamics analysis to predictive model implementation |
| topic | nba machine learning game outcome prediction feature selection classification |
| url | https://doi.org/10.2478/ijcss-2025-0006 |
| work_keys_str_mv | AT rodriguesf nbaresultsforecastfromleaguedynamicsanalysistopredictivemodelimplementation AT piresf nbaresultsforecastfromleaguedynamicsanalysistopredictivemodelimplementation |