Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Abstract

Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

Machine learning application in soccer: a systematic review

“…The current trend focusing on talent has defined which term as a dynamically varying relationship molded by the constraints imposed by the physical and social environments, the task experienced, and the personal resources of a player [ 50 ], highlighting talent identification in one of the more important challenges in soccer, not only during early ages [ 8 , 10 ], but also for player transferal between soccer clubs [ 9 ]. In addition, ML can be used in this way to predict the most suitable players for decision-making for competition starter players (choosing players and playing positions) [ 10 , 33 ].…”

“…To date, it has been systematically reviewed that technical and time-motion variables are clustered into principal components that explain the behavior of soccer players [ 51 ]. Based on these ideas, the most used inputs (features) to identify the talent of soccer players are technical variables such as passes, tackles, possessions, clearances, or shots [ 8 – 10 ], as well as variables extracted from tracking systems [ 10 ], and psychological indicators [ 9 ]. For example, Barron et al [ 8 ] analyzed 966 soccer players (209 from low level, 637 for Championship Football League, and 120 from English Premier League), and with an accuracy of between 61 and 79% of attempts, the authors predicting players’ career trajectory using 347 technical attributes.…”

Football Match Line-Up Prediction Based on Physiological Variables: A Machine Learning Approach

“…Artificial Intelligence (AI) presence in sports is gradually increasing, in particular in football, where ML algorithms are used to detect meaningful patterns based on positional data [6]. ML is already used in football to predict and prevent injuries in players [5,9,10], as well as in the categorization of football players and football training sessions [1,11], in the evaluation of football players regarding their market value [12,13], and in predicting the results of football matches [4,14], among others.…”

“…Then, for each cluster, an automatic regression method, able to detect the relevant features, was trained, and they were able to estimate the value of players with 74% accuracy. In this line, authors from [13] presented a study where ML techniques are explored to plan possible player transfers and build a professional football team with interesting results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). A dataset with the complete overview of a football player was used: technical and physical parameters, but also his psychological state in order to improve the scouting of players and to understand if it was possible to create the definition of a successful transfer (the results were not constant for all experiments).…”

Pre-processing methods in chest X-ray image classification

“…At the same time, modern technologies have gathered more interest. Artificial intelligence (AI) and ML can be used in numerous applications such as cybersecurity [ 2 ], pedestrian detection [ 3 ], telemedicine [ 4 ], biometrics [ 5 ] or sports analytics [ 6 ]. Thus, the implementation of AI and ML in COVID-19 and other lung diseases seems to be the desired natural progression.…”