GCRIS

Increasing popularity of data analytics has impacted the sport industry. Dimension of available data and best practices on the usage of data analytics increased as a result of this trend. Player profiling is one of emerging hot topics among those, especially in football. On the other hand, income and expense balance of transfers has been biggest burden on clubs’ financials while it should be reverse. Scouting processes are currently dominated by bilateral relations and intuitive comments of scouting staff. It is an important step to transform into data driven decision framework to overcome this situation. It is crucial to replace a player who leave the team with someone who has potential and very close playing style. Player profiling is the first step to do this. The data set used in this project is obtained from Opta – a sport focused data company – and contains all actions performed on-ball at player level from Turkish Super League, English Premier League and German Bundesliga in three seasons between 2015 and 2018. Principal component analysis is applied to the dataset in order to reduce dimensionality to the 15 features which consists of 2469 players and 271 features at the beginning. As a result of this study, it is observed that there are twelve different player clusters within the traditional main positions; three for defenders, four for midfielders and five for forwards. Clubs can enrich and benefit from these clusters in three ways: 1) evaluation of a player style over a period of time and detecting the best role fit 2) analyzing the effect of cluster combination to decide which line-up yields better team results 3) finding the closest match to a player who is subject to replacement.

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