Player Performance Prediction in Football Game

In the game of football (soccer), the evaluation of players for transfer, scouting, squad formation and strategic planning is important. However, due to the vast pool of grassroots level player, short career span, differing performance throughout the individual's career, differing play conditions, positions and varying club budgets, it becomes difficult to identify the individual player's performance value altogether. Our Player Performance Prediction system aims at solving this complex problem analytically and involves learning from various attributes and skills of a football player. It considers the skill set values of the football player and predicts the performance value, which depicts the scope of improvement and the capability of the player. The objective of this system is to help the coaches and team management at the grassroots as well as higher levels to identify the future prospects in the game of football without being biased to subjective conditions like club budget, competitiveness in the league, and importance of the player in the team or region. Our system is based on a data-driven approach and we train our models to generate an appropriate holistic relationship between the players' attributes values, market value and performance value to be predicted. These values are dependent on the position that the football player plays in and the skills they possess.

[1]  William Scherer,et al.  Implementation of a recruit visualization tool for UVA football , 2017, 2017 Systems and Information Engineering Design Symposium (SIEDS).

[2]  Noah Wardrip-Fruin,et al.  Mining game statistics from web services: a World of Warcraft armory case study , 2010, FDG.

[3]  Mohamed Hefeeda,et al.  Competition-Wide Evaluation of Individual and Team Movements in Soccer , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[4]  László Gyarmati,et al.  Towards Data-Driven Football Player Assessment , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[5]  Brent E. Harrison,et al.  Using sequential observations to model and predict player behavior , 2011, FDG.

[6]  Xu Xu,et al.  A Novel Decision Support Model to Discover the Interesting Pattern in Football Match , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[7]  Adel Ali Al-Jumaily,et al.  PCA indexing based feature learning and feature selection , 2016, 2016 8th Cairo International Biomedical Engineering Conference (CIBEC).

[8]  William T. Scherer,et al.  Implementing data analytics for U.Va. Football , 2017, 2017 Systems and Information Engineering Design Symposium (SIEDS).