期刊论文详细信息
Applied Sciences
Modeling In-Match Sports Dynamics Using the Evolving Probability Method
Ante Gojsalić1  Ana Šarčević2  Damir Pintar2  Mihaela Vranić2 
[1] AVL-AST d.o.o., Strojarska 22, HR-10000 Zagreb, Croatia;Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia;
关键词: Bayes estimation;    Markov process;    Monte Carlo simulation;    non-iid distribution;    predictive model;    psychological momentum;   
DOI  :  10.3390/app11104429
来源: DOAJ
【 摘 要 】

The prediction of sport event results has always drawn attention from a vast variety of different groups of people, such as club managers, coaches, betting companies, and the general population. The specific nature of each sport has an important role in the adaption of various predictive techniques founded on different mathematical and statistical models. In this paper, a common approach of modeling sports with a strongly defined structure and a rigid scoring system that relies on an assumption of independent and identical point distributions is challenged. It is demonstrated that such models can be improved by introducing dynamics into the match models in the form of sport momentums. Formal mathematical models for implementing these momentums based on conditional probability and empirical Bayes estimation are proposed, which are ultimately combined through a unifying hybrid approach based on the Monte Carlo simulation. Finally, the method is applied to real-life volleyball data demonstrating noticeable improvements over the previous approaches when it comes to predicting match outcomes. The method can be implemented into an expert system to obtain insight into the performance of players at different stages of the match or to study field scenarios that may arise under different circumstances.

【 授权许可】

Unknown   

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