Neural Networks and Betting Strategies for Tennis

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ID: 120302
2020
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Abstract
Recently, the interest of the academic literature on sports statistics has increased enormously. In such a framework, two of the most significant challenges are developing a model able to beat the existing approaches and, within a betting market framework, guarantee superior returns than the set of competing specifications considered. This contribution attempts to achieve both these results, in the context of male tennis. In tennis, several approaches to predict the winner are available, among which the regression-based, point-based and paired comparison of the competitors’ abilities play a significant role. Contrary to the existing approaches, this contribution employs artificial neural networks (ANNs) to forecast the probability of winning in tennis matches, starting from all the variables used in a large selection of the previous methods. From an out-of-sample perspective, the implemented ANN model outperforms four out of five competing models, independently of the considered period. For what concerns the betting perspective, we propose four different strategies. The resulting returns on investment obtained from the ANN appear to be more broad and robust than those obtained from the best competing model, irrespective of the betting strategy adopted.
Reference Key
candila2020risksneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Vincenzo Candila;Lucio Palazzo;Candila, Vincenzo;Palazzo, Lucio;
Journal risks
Year 2020
DOI
10.3390/risks8030068
URL
https://www.mdpi.com/2227-9091/8/3/68
Keywords
artificial neural networks tennis forecasting betting

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