A deep learning framework for football match prediction
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A deep learning framework for football match prediction Md. Ashiqur Rahman1 Received: 5 August 2019 / Accepted: 28 November 2019 © Springer Nature Switzerland AG 2020
Abstract An efficient framework is developed by deep neural networks (DNNs) and artificial neural network (ANNs) for predicting the outcomes of football matches. A dataset is used with the rankings, team performances, all previous international football match results and so on. ANN and DNN are used to explore and process the sporting data to generate prediction value. Datasets are divided into sections for training, validating and testing. By using the proposed DNN architecture, corresponding model performed excellently on predicting the FIFA world cup 2018 matches. This model had predicted 63.3% matches accurately. However, this accuracy can be increased with proper datasets and more accurate information of the teams. The outcome of this hypothesis can be derived that deep learning may be used for successfully predicting the outcomes of football matches or any other sporting events. For more accurate performance of the prediction, prior and more information about each team, player and match is desirable. Keywords Football match prediction · Deep neural networks · Deep learning · Artificial neural networks
1 Introduction Football being one of the world’s most popular game has a craze in everyone’s mind. A football fan must have wanted to know the results before the game at least once in a lifetime! In sport prediction, large numbers of features can be collected including the historical performance of the teams, results of matches, and data on players, to help different stakeholders understand the odds of winning or losing forthcoming matches. The decision of which team is likely to win is important because of the financial assets involved in the betting process; thus bookmakers, fans, and potential bidders are all interested in approximating the odds of a game in advance [1]. The aim of this research is predicting football matches using deep learning algorithms such as ANN and DNN. Deep neural networks (DNNs) have been used successfully in many scientific, industrial and business domains as a method for extracting knowledge from vast amounts of data. However, the use of DNN techniques in the sporting
domain has been limited. Sporting organizations have begun to realize that there is a wealth of untapped knowledge contained in the data and there is great interest in techniques to utilize this data. The purpose of this study is to develop an efficient framework that will predict football matches correctly and maintain a higher accuracy. An algorithmic study of the prediction techniques is essential for understanding how the deep learning, ANN and DNN technology works to classify item-set. To predict the outcome of a match between two teams, a person will generally take into account certain factors like the recent performances of the teams, whether the match is going to be played at home or away, recent player transfers, recent coach and staffing ch
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