A deep learning analysis for the effect of individual player performances on match results

Player performance is the most important factor that affects match scores. Factors affecting player performance are not the same for all players, and vary according to pitch positions. Analyzing these performance factors in relation to pitch positions can help understand which characteristics of players need to be developed in order to win. Player training can be arranged accordingly, and team tactics can be changed or improved. Although the importance of analyzing the individual performances of players according to pitch positions has been emphasized in various studies, a large amount of data available has made this analysis difficult. Machine learning can be used to overcome this difficulty. However, machine learning studies in sports mostly focus on score prediction. There is a lack of traditional and machine learning studies that examine the effect of individual player performances on game results. In this context, the datasets of the 2010 and 2014 FIFA World Cups were analyzed through multi-layer artificial neural networks. A specific model was established for each dataset by organizing relevant datasets according to year, player positions, and match levels (group–final). Rectifier Linear Unit was selected as the activation function for each model. Architecture and hyper-parameters for each model were determined through grid optimization. The factors affecting player performances were ranked by the Gedeon’s relative importance calculation. The average performance indicators for the group matches are 81.34% precision, 87% recall, and 0.84 F1 score. The area under curve for the final series is 0.798.