<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In today’s sports environment, prediction has a significant effect on athletes to achieve good performance and improve training efficiency. This study explores the construction of a prediction model for tennis match data based on an intelligent analyzing system in combination with tennis match results. The probabilistic algorithm for predicting tennis tournament performance has been designed and implemented, and the Glicko ranking system has been optimized using the improved Apriori algorithm. After cleaning, integrating, and dimensional standardizing the data from previous tournaments, the player’s match ranking is predicted based on the Glicko ranking system. Players’ eigenvalues, technical averages, and tactical decisions are used to verify the accuracy of the model prediction. All 10 selected features have a certain degree of influence on the match results, as evidenced by the results, suggesting that the model has a certain reference value. According to the ranking prediction based on athletes’ technical averages, the prediction correct rate of both the Australian and French Open reaches no less than 80%, which proves that the model in this paper is able to effectively predict the performance of tennis tournaments.</p>
</abstract>

In today’s sports environment, prediction has a significant effect on athletes to achieve good performance and improve training efficiency. This study explores the construction of a prediction model for tennis match data based on an intelligent analyzing system in combination with tennis match results. The probabilistic algorithm for predicting tennis tournament performance has been designed and implemented, and the Glicko ranking system has been optimized using the improved Apriori algorithm. After cleaning, integrating, and dimensional standardizing the data from previous tournaments, the player’s match ranking is predicted based on the Glicko ranking system. Players’ eigenvalues, technical averages, and tactical decisions are used to verify the accuracy of the model prediction. All 10 selected features have a certain degree of influence on the match results, as evidenced by the results, suggesting that the model has a certain reference value. According to the ranking prediction based on athletes’ technical averages, the prediction correct rate of both the Australian and French Open reaches no less than 80%, which proves that the model in this paper is able to effectively predict the performance of tennis tournaments.

Intelligent Analysis and Predictive Modeling of Tennis Match Data

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Intelligent Analysis and Predictive Modeling of Tennis Match Data"}

In today’s sports environment, prediction has a significant effect on athletes to achieve good performance and improve training efficiency. This study explores...

Intelligent Analysis and Predictive Modeling of Tennis Match Data

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 14 mar 2024

Przyjęty: 29 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1593

Słowa kluczowe
Tennis matches, Intelligent analytics, Machine learning, Performance prediction models

© 2024 Yansong Dong., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Intelligent Analysis and Predictive Modeling of Tennis Match Data

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 14 mar 2024

Przyjęty: 29 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1593

Słowa kluczoweTennis matches, Intelligent analytics, Machine learning, Performance prediction models

© 2024 Yansong Dong., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Tennis matches, Intelligent analytics, Machine learning, Performance prediction models