In basketball, each player’s skill level is the key to a team’s success or failure, the skill level is affected by many personal and environmental factors. A physics-informed AI statistics has become extremely important. In this article, a complex non-linear process is considered by taking into account the average points per game of each player, playing time, shooting percentage, and others. This physics-informed statistics is to construct a multiple linear regression model with physics-informed neural networks. Based on the official data provided by the American Basketball League, and combined with specific methods of R program analysis, the regression model affecting the player’s average points per game is verified, and the key factors affecting the player’s average points per game are finally elucidated. The paper provides a novel window for coaches to make meaningful in-game adjustments to team members.
References
[1]
Chen, S.T., Cheng, C. and Chai, Y.J. (2015) A Study on the Correlation between Salary, Team Performance, and Player Performance. Sports, 123, 33-35. (In Chinese)
[2]
Song, H.F. and Han, G.L. (2020) Technical Statistics and Analysis of Women’s Basketball League in the Third Xizang Workers’ Games. Vocational Education Research, 6, 249-251. (In Chinese)
[3]
Li, Y.X. and Xiu, C.G. (2020) Analysis of Factors Influencing the Victory and Defeat of the 2018-2019 CBA Regular Season—Based on Multiple Linear Regression. Statistics and Management, 7, 113-116. (In Chinese)
[4]
Tan Z.L. (2022) Based on the Multiple Regression Model CBA, the Impact of Scoring Methods of China Guangzhou Team on Game Results during the 2020-2021 Season. Master’s Thesis, Guangzhou University, Guangzhou. (In Chinese)
[5]
Akakuru, O., Adakwa, C., Ikoro, D., et al. (2023) Application of Artificial Neural Network and Multi-Linear Regression Techniques in Groundwater Quality and Health Risk Assessment around Egbema, Souastern Nigeria. Environmental Earth Sciences, 82, Article No. 77. https://doi.org/10.1007/s12665-023-10753-1
[6]
Ravichandran, C. and Padmanaban, G. (2024) Estimating Cooling Loads of Indian Residences Using Building Geometry Data and Multiple Linear Regression. Energy and Built Environment, 5, 741-771. https://doi.org/10.1016/j.enbenv.2023.06.003
[7]
Aziz, A. and Anwar, M.M. (2024) Assessing the Level of Urban Sustainability in the Capital of Pakistan: A Social Analysis Applied through Multiple Linear Regression. Sustainability, 16, Article 2630. https://doi.org/10.3390/su16072630
[8]
Huang, Z.P., Ma, X., Chen, X., et al. (2024) Analysis and Application of Colorful Guizhou Tourism Data Based on Linear Regression Algorithm. Soft Engineering, 27, 63-66. (In Chinese)
[9]
Liu, D.B., Jin, Z.Y., Ke, Z.F., et al. (2023) Regression Analysis of Building Scale Data and Estimation of Demolition Rate. Acta Scientiarum Naturalium Universitatis Pekinensis, 59, 547-554. (In Chinese)
[10]
Zhang, J. and Xue, Y. (2022) Environmental DSGE Models’ Important Parameters: Research Based on Multiple Regression Analysis. Construction Economy, 43, 840-843. (In Chinese)
[11]
Li, Y., Zhu, F.Y., Chen, J.Y., et al. (2020) An Empirical Study on Cross-Broder E-Commerce Development of Agricultural Products in China Based on Multiple Linear Regression Analysis. Mathematics in Practice and Theory, 50, 299-310. (In Chinese)
[12]
Bhargavi, N.and Poornima, T. (2024) Radiative Impact on Jeffery Trihybrid Convective Nanoflow over an Extensible Riga Plate: Multiple Linear Regression Analysis. Contemporary Mathematics, 5, 1036-1053. https://doi.org/10.37256/cm.5120244058
[13]
Wang, S., Monjurul, H. and Ming, L. (2024) Global Sensitivity Analysis Methodology for Construction Simulation Models: Multiple Linear Regressions versus Multilayer Perceptions. Journal of Construction Engineering and Management, 150, Article ID: 04024035. https://doi.org/10.1061/JCEMD4.COENG-14059
[14]
Rekabi, S., Garjan, H., Goodarzian, F., et al. (2024) Designing a Responsive-Sustainable-Resilient Blood Supply Chain Network Considering Congestion by Linear Regression Method. Expert Systems with Applications, 245, Article ID: 122976. https://doi.org/10.1016/j.eswa.2023.122976
[15]
Mao, S.S., Cheng, Y.M. and Pu, X.L. (2019) Course in Probability Theory and Mathematical Statistics. Higher Education Press, Beijing, 319. (In Chinese)
