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Higher dimensional sports statistics and real-time game prediction
- 1 Shanghai University
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Abstract
The rapid expansion of comprehensive sports datasets and the successful application of data mining techniques in various domains have given rise to the emergence of sports data prediction techniques. These techniques enable the extraction of hidden knowledge that can significantly impact the sports industry, as more and more clubs are using Machine Learning (ML) and Deep Learning (DL) methods to manage athletes and training. In this research, the focusing and intriguing aspects is predicting the outcomes of a specific basketball athletes, which has garnered significant attention for research. The paper was motivated by a dual interest in college and NBA basketball matches, alongside a keen observation of the evolving strategies employed by coaches in athlete management. Additionally, the interest was further reinforced by firsthand observations of such evolving methods during a baseball game at City Field in New York. These factors collectively underpin the relevance and significance of this research endeavor, highlighting the intersection of personal interest and the evolving landscape of sports management as compelling reasons for its pursuit. In the process of data selection, we acquired data from previously published essays as well as from Kaggle, a reputable online platform. Following this, we proceeded to evaluate several prominent machine learning models, namely Linear Regression, KNN, Gradient Boosting, Elastic Net, and Lasso, to ascertain their effectiveness in predicting the performance of specific players. Through rigorous analysis and comparison, we concluded that Linear Regression and Gradient Boosting exhibited superior predictive capabilities compared to the other models considered. These two models demonstrated a higher degree of accuracy and reliability in forecasting player performance, thus establishing them as the most suitable choices for our predictive modeling purposes. This meticulous selection process, involving both data acquisition and model evaluation, forms the foundation of our research methodology and underscores the rigor and precision with which our conclusions are drawn.
Keywords
data predictions, machine learning, basketball matches, sports statistics
[1]. Rodrigues, F., & Pinto, Â. (2022). Prediction of football match results with Machine Learning. Procedia Computer Science, 204, 463-470.
[2]. Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied computing and informatics, 15(1), 27-33.
[3]. Apostolou, K., & Tjortjis, C. (2019, July). Sports Analytics algorithms for performance prediction. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA) (pp. 1-4). IEEE.
[4]. Mahmood, Z., Daud, A., & Abbasi, R. A. (2021). Using machine learning techniques for rising star prediction in basketball. Knowledge-Based Systems, 211, 106506.
[5]. Pifer, N. D., Mak, J. Y., Bae, W. Y., & Zhang, J. J. (2015). Examining the relationship between star player characteristics and brand equity in professional sport teams.
[6]. Thabtah, F., Zhang, L., & Abdelhamid, N. (2019). NBA game result prediction using feature analysis and machine learning. Annals of Data Science, 6(1), 103-116.
[7]. Karakaya, A., Ulu, A., & Akleylek, S. (2022). GOALALERT: A novel real-time technical team alert approach using machine learning on an IoT-based system in sports. Microprocessors and Microsystems, 93, 104606.
[8]. Nguyen, N., Ma, B., & Hu, J. (2020). Predicting National Basketball Association players performance and popularity: A data mining approach. In Computational Collective Intelligence: 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30–December 3, 2020, Proceedings 12 (pp. 293-304). Springer International Publishing.
[9]. Nguyen, N. H., Nguyen, D. T. A., Ma, B., & Hu, J. (2022). The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity. Journal of Information and Telecommunication, 6(2), 217-235.
[10]. Wang, J., & Fan, Q. (2021, March). Application of machine learning on nba data sets. In Journal of Physics: Conference Series (Vol. 1802, No. 3, p. 032036). IOP Publishing.
[11]. Humphreys, B. R., & Johnson, C. (2020). The effect of superstars on game attendance: Evidence from the NBA. Journal of Sports Economics, 21(2), 152-175.
[12]. Cao, C. (2012). Sports data mining technology used in basketball outcome prediction.
[13]. Edouard, P., Verhagen, E., & Navarro, L. (2022). Machine learning analyses can be of interest to estimate the risk of injury in sports injury and rehabilitation. Annals of physical and rehabilitation medicine, 65(4), 101431.
[14]. Ang, Z. (2023). Application of IoT technology based on neural networks in basketball training motion capture and injury prevention. Preventive Medicine, 175, 107660.
[15]. Chen, Z., & Zhang, G. (2023). CNN sensor based motion capture system application in basketball training and injury prevention. Preventive Medicine, 174, 107644.
[16]. Martin, R. K., Pareek, A., Krych, A. J., Kremers, H. M., & Engebretsen, L. (2021). Machine learning in sports medicine: need for improvement. Journal of ISAKOS, 6(1), 1-2.
[17]. Chu, Y., Knell, G., Brayton, R. P., Burkhart, S. O., Jiang, X., & Shams, S. (2022). Machine learning to predict sports-related concussion recovery using clinical data. Annals of physical and rehabilitation medicine, 65(4), 101626.
[18]. Hinduja, S., Afrin, M., Mistry, S., & Krishna, A. (2022). Machine learning-based proactive social-sensor service for mental health monitoring using twitter data. International Journal of Information Management Data Insights, 2(2), 100113.
[19]. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
[20]. Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.
[21]. Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301-320.
[22]. Ranstam, J., & Cook, J. A. (2018). LASSO regression. Journal of British Surgery, 105(10), 1348-1348.
[23]. Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21.
[24]. Maszczyk, A., Gołaś, A., Pietraszewski, P., Roczniok, R., Zajac, A., & Stanula, A. (2014). Application of neural and regression models in sports results prediction. Procedia-Social and Behavioral Sciences, 117, 482-487.
[25]. O’Donoghue, P., & Cullinane, A. (2011). A regression-based approach to interpreting sports performance. International Journal of Performance Analysis in Sport, 11(2), 295-307.
Cite this article
Yuan,X. (2024). Higher dimensional sports statistics and real-time game prediction. Advances in Engineering Innovation,8,9-18.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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