performance variables or indicators that predict the probability of success
The latest developments in technology have the potential of adding speed and novelty to the exploration of contextual variables that are connected to the game of football (Brefeld and Zimmermannm, 2017). The automatic, quantitative analysis offered by machine learning is beyond the scope of observational study, since supervised classifying models can produce the same and even better observational data in terms of richness. In models and techniques presented above, the two main types of input data, which are tracking data and event data, are differentiated. The main possible findings from event data include the recognition of team characteristics and patterns and the identification of key performance variables or indicators that predict the probability of success. In most analyses, it can often be deduced that the findings from the tracking data are likely to be process-centered, such as the determinants of players’ scoring capabilities and how well they play their passes. Despite the benefits and the breakthroughs being developed in the field, scenarios which involve rapid and unpredictable movements, like occlusions between the players are likely to present massive challenges for the practitioners regarding the accuracy of the data and information that is provided, in comparison to the actual events taking place in the field during a game of football. Modeling or research in the area should focus on reducing these inherent errors, and practitioners should also proceed with caution when comparing the data outcomes of different player performance tracking systems. Nonetheless, data obtained from player tracking are being used by many sports leagues and competitions across the world, and the raw data is being turned into vital actionable and useful insights.
Computer scientists conduct most applications or studies which use machine learning since they have the knowledge and skills of managing more sophisticated approaches. However, the challenge of using these complicated models is that they often create black boxes where the results obtained, primarily through the use of neuronal networks (Constantinou and Fenton, 2017). With these black boxes, it is nearly impossible to determine the essential features. For example, a practitioner can have a passing model that does exceptionally when in quantifying passes but does not provide further information on attributes like the position of the player, the length of the pass or the speed with which the pass is traveling, all of which should make it a good pass. Therefore, it is nearly impossible to provide vital feedback to the practitioners and coaches who prefer straight-forward analyses, providing a quick overview of the performance of the team (Constantinou and Fenton, 2017). Sports analytics, which include complex and substantial mathematical and statistical equations, are not considered the most important things by coaches (Brefeld and Zimmermannm, 2017). Neither have these equations and knowledge been integrated into the subject of football coaching. Therefore, a significant number of machine learning analysts’ work is being done by computer science research groups who dd not involve match analysts, sports scientists, or the coaching staff.
What is more, a significant amount of research has been focusing on the prediction of matches instead of analyzing teams’ performances to improve the quality of the group, or even to determine if the team will outscore their opponents (Brefeld and Zimmermannm, 2017). Since there is a lacking interaction between computer science and practice, the results rarely transfer into practice, which implies considerable room has been left for improvement upon applying knowledge gained by the data to the experience that is applicable to the actual game (Brooks, Kerr and Guttag, 2016). A solution, therefore, is to this problem is to integrate computer scientists and football coaches to get more information, which would help improve individual and team performance.