Applying Neural Networks to Football Matches Results Forecasting

Korotyeyeva, T. O.; Tushnytskyy, Ruslan; Kulyk, Volodymyr

doi:10.1109/stc-csit.2018.8526756

Cited by 3 publications

(1 citation statement)

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“…However, other authors have begun to create different models that are capable of predicting results with a high percentage of success using different techniques such as neural networks, Bayesian networks or different algorithms such as k-nearest neighbours. 22–26…”

Section: Introductionmentioning

confidence: 99%

In-game behaviour analysis of football players using machine learning techniques based on player statistics

García-Aliaga

Marquina

Coterón

et al. 2020

International Journal of Sports Science & Coaching

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The purpose of this research was to determine the on-field playing positions of a group of football players based on their technical-tactical behaviour using machine learning algorithms. Each player was characterized according to a set of 52 non-spatiotemporal descriptors including offensive, defensive and build-up variables that were computed from OPTA’s on-ball event records of the matches for 18 national leagues between the 2012 and 2019 seasons. To test whether positions could be identified from the statistical performance of the players, the dimensionality reduction techniques were used. To better understand the differences between the player positions, the most discriminatory variables for each group were obtained as a set of rules discovered by RIPPER, a machine learning algorithm. From the combination of both techniques, we obtained useful conclusions to enhance the performance of players and to identify positions on the field. The study demonstrates the suitability and potential of artificial intelligence to characterize players' positions according to their technical-tactical behaviour, providing valuable information to the professionals of this sport.

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Section: Introductionmentioning

confidence: 99%