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

<p>Tennis, as one of the most popular individual sports in the world, holds an important role in the betting world. There are two main categories of bets: pre-match betting, which is conducted before the match starts, and live betting, which allows placing bets during the sporting event. Betting systems rely on setting sports odds, something historically done by domain experts. Setting odds for live betting represents a challenge due to the need to follow events in real-time and react accordingly. In tennis, hierarchical models often stand out as a popular choice when trying to predict the outcome of the match. These models commonly leverage a recursive approach that aims to predict the winner or the final score starting at any point in the match. However, recursive expressions inherently contain computational complexity which hinders the efficiency of methods relying on them. This paper proposes a more resource-effective alternative in the form of a combinatorial approach based on a binomial distribution. The resulting accuracy of the combinatorial approach is identical to that of the recursive approach while being vastly more efficient when considering the execution time, making it a superior choice for live betting in this domain.</p>
</abstract>

Tennis, as one of the most popular individual sports in the world, holds an important role in the betting world. There are two main categories of bets: pre-match betting, which is conducted before the match starts, and live betting, which allows placing bets during the sporting event. Betting systems rely on setting sports odds, something historically done by domain experts. Setting odds for live betting represents a challenge due to the need to follow events in real-time and react accordingly. In tennis, hierarchical models often stand out as a popular choice when trying to predict the outcome of the match. These models commonly leverage a recursive approach that aims to predict the winner or the final score starting at any point in the match. However, recursive expressions inherently contain computational complexity which hinders the efficiency of methods relying on them. This paper proposes a more resource-effective alternative in the form of a combinatorial approach based on a binomial distribution. The resulting accuracy of the combinatorial approach is identical to that of the recursive approach while being vastly more efficient when considering the execution time, making it a superior choice for live betting in this domain.

A Combinatorial Approach in Predicting the Outcome of Tennis Matches

International Journal of Applied Mathematics and Computer Science

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

{"article-title":"A Combinatorial Approach in Predicting the Outcome of Tennis Matches"}

Tennis, as one of the most popular individual sports in the world, holds an important role in the betting world. There are two main categories of bets:...

A Combinatorial Approach in Predicting the Outcome of Tennis Matches

Publié en ligne: 27 sept. 2021

Pages: 525 - 538

Reçu: 01 févr. 2021

Accepté: 25 juil. 2021

DOI: https://doi.org/10.34768/amcs-2021-0036

Mots clés
binomial distribution, final score prediction, independent and identical distribution, predictive model

© 2021 Ana Šarčević et al., published by Sciendo

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

A Combinatorial Approach in Predicting the Outcome of Tennis Matches

Publié en ligne: 27 sept. 2021

Pages: 525 - 538

Reçu: 01 févr. 2021

Accepté: 25 juil. 2021

DOI: https://doi.org/10.34768/amcs-2021-0036

Mots clésbinomial distribution, final score prediction, independent and identical distribution, predictive model

© 2021 Ana Šarčević et al., published by Sciendo

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

Mots clés
binomial distribution, final score prediction, independent and identical distribution, predictive model