Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League - Murat Koklu; Yavuz Selim Taşpınar; İlkay Çınar (MANAS: Journal of Engineering)
Football is one of the most popular sports in terms of number of fans in the world. This situation arises from the unpredictable nature of football. People are becoming more and more connected to this sport as it combines emotions such as excitement and joy that it creates in people. Match result prediction is a very challenging problem, and recently the solution to this problem has become very popular. With the result of this unpredictable game the events that occur during the match that affect this result are tried to be predicted by machine learning methods. This study demonstrates our work on finding the most effective features in match result prediction using match statistics from the Italian Serie A League's 2027 pieces match between the 2014-2015 and 2019-2020 seasons and with 54 features for each match. Feature selection testing was conducted to estimate the results of a football match and determine the most important factors. The selection of features was made using the ANOVA method and it was predicted that 28 of the 54 features would be effective in predicting match results. After this stage, fairly high rates classification success was achieved using the logistic regression method. 88.85% as a result of the prediction made with all features and 89.63% success was achieved as a result of the prediction made with 28 selected features. With these results, it is possible to say that process of feature selection increase success in match result prediction.