Yingfu Tian and  Lifang Zhang. Sports Performance Prediction Based on Bayesian Parameter Inference. Dynamic Systems and Applications 29 (2020) No. 5, 2148 – 2156

https://doi.org/10.46719/dsa202029550

ABSTRACT.
Rapidly, the appropriate statistical method for analyzing these data is that the spatial-temporal data is increasing in underdeveloped sports. Bayesian method is in the analysis of the sport, it has been adopted by more and more as soon as there is an increasing popular, sport settings. Identifying the benefits of the parametric method of Bayesian Parameter Inference estimation is a combination of sources, and the ability to update features to learn new data to model complex problems Probability estimates that consider uncertainty and getting a forecast. The Bayesian Parameter Inference provides a brief review of the method of the latest trends and movement coverage of Bayesian statistics. The availability of volumes and data generated by the availability of Bayesian calculations covered by various cultural and sporting activities and interfaces in recent years has already reached its ground, but this development has contributed significantly. This comprehensive survey portrays Bayesian statistics, the latest developments in sports, including methods and applications to evaluate. The proposed system found that most of these articles focused on predicting/modeling the onset of sports games and statistics that provide better results for sports performance.

Keywords: Analysis Sports Performance, Bayesian Parameter Inference, Bayesian Statistics