Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

July 5, 2024


Pages


DOI

Article

A study on the modeling of shooting probability in basketball sports competition sport

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Authors

Quanzhong Gao Affiliation:
Anhui Institute of International Business, Hefei, Anhui, 231131, China.


Abstract

In basketball sports competition sports, shooting percentage is an important indicator of basketball players. In this paper, the trajectory of basketball sports shooting is solved by mathematical modeling and simulation. In the first step, the hyperparameter estimation of the variance is obtained by using the SURE-Type Double shrinkage estimation method. In the second step, the variance of the SUREDSC method is determined by sampling from the hyperparametric distribution using the Bootstrap method. In the third step, the covariates were taken into account and combined with the Ghoreishi bi-level shrinkage estimation method to obtain the optimal shrinkage estimate of the probability of hitting a basketball shot. The results of the study yielded that α differs from β when α ≤ 75°. When α >75°, α and β are very close. When the angle of incidence is certain, the angle of the ball shot is negatively correlated with the height of the athlete, and the degree of reduction decreases with the increase of the angle of incidence. The prediction success rate of the shot probability model was 87.98%, and the overall error rate was small, and the prediction results of the seven games were basically consistent with the prediction results of the constructed shot probability model. The study offers a guide for basketball managers to weigh the pros and cons of player transfers.


Keywords

Shooting probability model, Shrinkage estimation method, SUREDSC method, Mathematical modeling, Basketball sports, 68T05


Citation

Gao, Q. (2024). A study on the modeling of shooting probability in basketball sports competition sport. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-1616
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