| Soccer is the most popular sport worldwide. Over time, the importance of soccer has grown beyond the sports domain, making it a large industry, a source of national pride, and the center of public attention in most countries. Due to this increased significance, it is highly important for soccer teams at both the club and national levels to invest in sciences providing a competitive edge over opponents. Quantitative analysis of soccer is one of the domains that have enjoyed a sharp growth in the recent years. Using the advanced data collection and analysis tools, it has become possible to implement more sophisticated performance analysis methodologies. In this study, a model has been developed to anticipate the collective team performance based on the attributes of the individual players. The model is then used to predict how the hiring of new players affects team performance. The data used for this study has been collected from the English Premier League between the 2008/09 and 2011/2012 seasons. Using the model, team performance can be predicted with an average error of 7.857 units of goal differential. Also, the effect of a new player on team performance can be predicted with an average error of 18.912 units of goal differential. Using a classification strategy, the model was able to correctly predict the direction of change in team performance caused by a new player 85.6% of the time. This provides a minimum of 20% increase in accuracy compared to the current transfer success rate at the highest level of club soccer. Therefore, using this model is expected to save clubs large amounts of money while enhancing performance. |
