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A Two-Population Extension of the Exponential Smoothing State Space Model with a Smoothing Penalisation Scheme

Neural Networks and Betting Strategies for Tennis

by 1,*,† and 2,†
MEMOTEF Department, Sapienza University of Rome, 00185 Rome, Italy
Department of Political Sciences, University of Naples Federico II, 80136 Naples, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Risks 2020, 8(3), 68;
Received: 1 June 2020 / Revised: 20 June 2020 / Accepted: 22 June 2020 / Published: 29 June 2020
(This article belongs to the Special Issue Risks in Gambling)
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. View Full-Text
Keywords: forecasting; artificial neural networks; betting; tennis forecasting; artificial neural networks; betting; tennis
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MDPI and ACS Style

Candila, V.; Palazzo, L. Neural Networks and Betting Strategies for Tennis. Risks 2020, 8, 68.

AMA Style

Candila V, Palazzo L. Neural Networks and Betting Strategies for Tennis. Risks. 2020; 8(3):68.

Chicago/Turabian Style

Candila, Vincenzo, and Lucio Palazzo. 2020. "Neural Networks and Betting Strategies for Tennis" Risks 8, no. 3: 68.

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