Tennis Betting Strategies Based on Neural Networks

Jeremy Flahault, Nicolas Le Roger, Marius-Cristian Frunza
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Abstract

The aim of this paper is to explore betting strategies for tennis matches using neural networks. We used public data and we implemented a neural network prediction model, with an accuracy of 85% on the validation and testing sets. Based on the predictive model we tested several investment strategies which incorporates investor's risk profile. The optimal strategy is to place bets on games where our model indicates a high likelihood of victory and an appropriate bookmaker's odds.
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基于神经网络的网球投注策略
本文的目的是利用神经网络来探索网球比赛的投注策略。我们使用公共数据,并实现了一个神经网络预测模型,在验证和测试集上的准确率为85%。基于预测模型,我们测试了几种包含投资者风险特征的投资策略。最佳策略是在我们的模型显示获胜可能性高且庄家赔率合适的游戏中下注。
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