Analysis and Prediction of CBA Player Position Data Characteristics Based on Machine Learning

Yuanzheng Yu^1,

¹Hamden Hall Country Day School, Hamden, USA

Cite this paper:
Yuanzheng Yu. Analysis and Prediction of CBA Player Position Data Characteristics Based on Machine Learning. American Journal of Applied Mathematics and Statistics. 2024; 12(4):75-79. doi: 10.12691/ajams-12-4-1

Abstract

This study uses player performance statistics from the Chinese Basketball Association for six seasons, from 2017 to 2022, to evaluate the statistical characteristics of guards, forwards, and centers. 20 key performance indicators including points per game, rebounds, assists, shooting percentages, etc. are employed to provide empirical evidence to identify the singular traits that have come to be associated with each position. The study uses eight different machine learning models -- Decision Tree, Linear Discriminant Analysis, Multinomial Logistic Regression, Naive Bayes, Neural Network, Random Forest, Support Vector Machine, and XGBoost -- for position prediction of players from their performance data. From the results, it can be learned that guards are much more adept at scoring, assists, steals, and three-point shooting; forwards are better rebounders and three-point shooters; centers are proficient in rebounding, blocking, and field goal percentage. Among all the considered predictive models, Random Forest and XGBoost have the best test accuracies, while some models are clearly overfitted. This study suggests that using an ensemble machine-learning approach on performance data in the CBA context works particularly well when predicting player’s position. The study contributes to a better understanding of positional attributes in professional basketball and provides methodological references for future research in the field of sports analytics.

Keywords:
CBA (chinese basketball association) player position analysis machine learning classification sports analytics performance metrics

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Mandić, R., Jakovljević, S., Erčulj, F., & Štrumbelj, E. (2019). Trends in NBA and Euroleague basketball: Analysis and comparison of statistical data from 2000 to 2017. PloS One, 14(10), e0223524.
[2]	Drinkwater, E. J., Pyne, D. B., & McKenna, M. J. (2008). Design and interpretation of anthropometric and fitness testing of basketball players. Sports Medicine, 38(7), 565-578.
[3]	Gutiérrez, G. (2024). Performance analysis in basketball players. International Journal of Sports, Exercise and Physical Education, 6(1), 86-87.
[4]	Kalman, S., & Bosch, J. (2021). NBA Lineup Analysis on Clustered Player Tendencies: A New Approach to the Positions of Basketball & Modeling Lineup Efficiency. MIT Sloan Sports Analytics Conference.
[5]	Fu, X. B., Yue, S. L., & Pan, D. Y. (2021). Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network. Soft Computing.
[6]	Musa, R. M., Majeed, A. P. P. A., Kosni, N. A., & Abdullah, M. R. (2022). Machine Learning in Team Sports: Performance Analysis and Talent Identification in Beach Soccer & Sepak-takraw. Springer, Cham.
[7]	Rossi, A., Pappalardo, L., & Cintia, P. (2022). A narrative review for a machine learning application in sports: An example based on injury forecasting in soccer. Sports, 10(5), 5.
[8]	Albert, A. A., de Mingo López, L. F., Allbright, K., & Gomez Blas, N. (2022). A Hybrid Machine Learning Model for Predicting USA NBA All-Stars. Electronics, 11(1), 97.
[9]	Wang, X., Han, B., Zhang, S., Zhang, L., Lorenzo Calvo, A., & Gomez, M.-Á. (2022). The differences in the performance profiles between native and foreign players in the Chinese Basketball Association. Frontiers in Psychology, 12, Article 788498.
[10]	Tan, H., Qin, C., Ran, Z., Wang, K., & Zheng, Z. (2024). Python-based visualization platform implementation of CBA players' regular season 2022-2023 data. Frontiers in Humanities and Social Sciences, 4(7), 104-118.
[11]	Kang, N., & Xu, H. (2020). A Study of CBA League Team's Winning Probability Based on 'Grey Prediction'. International Journal of Innovation in Science and Mathematics, 8(3), 124-129.