Modeling In-Match Sports Dynamics Using the Evolving Probability Method
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/10/4429 |
| _version_ | 1797534265840238592 |
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| author | Ana Šarčević Damir Pintar Mihaela Vranić Ante Gojsalić |
| author_facet | Ana Šarčević Damir Pintar Mihaela Vranić Ante Gojsalić |
| author_sort | Ana Šarčević |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:27:09Z |
| format | Article |
| id | doaj.art-c2a817661df045c2b4983634fad55a74 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:27:09Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c2a817661df045c2b4983634fad55a742023-11-21T19:33:35ZengMDPI AGApplied Sciences2076-34172021-05-011110442910.3390/app11104429Modeling In-Match Sports Dynamics Using the Evolving Probability MethodAna Šarčević0Damir Pintar1Mihaela Vranić2Ante Gojsalić3Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, CroatiaAVL-AST d.o.o., Strojarska 22, HR-10000 Zagreb, CroatiaThe 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.https://www.mdpi.com/2076-3417/11/10/4429Bayes estimationMarkov processMonte Carlo simulationnon-iid distributionpredictive modelpsychological momentum |
| spellingShingle | Ana Šarčević Damir Pintar Mihaela Vranić Ante Gojsalić Modeling In-Match Sports Dynamics Using the Evolving Probability Method Applied Sciences Bayes estimation Markov process Monte Carlo simulation non-iid distribution predictive model psychological momentum |
| title | Modeling In-Match Sports Dynamics Using the Evolving Probability Method |
| title_full | Modeling In-Match Sports Dynamics Using the Evolving Probability Method |
| title_fullStr | Modeling In-Match Sports Dynamics Using the Evolving Probability Method |
| title_full_unstemmed | Modeling In-Match Sports Dynamics Using the Evolving Probability Method |
| title_short | Modeling In-Match Sports Dynamics Using the Evolving Probability Method |
| title_sort | modeling in match sports dynamics using the evolving probability method |
| topic | Bayes estimation Markov process Monte Carlo simulation non-iid distribution predictive model psychological momentum |
| url | https://www.mdpi.com/2076-3417/11/10/4429 |
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