Machine Learning for Football Matches and Tournaments

Vashist, Mrigank; Bahl, Vasudha; Sengar, Nidhi; Goel, Amita

doi:10.1109/com-it-con54601.2022.9850673

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…They used a small dataset because the performance with a large dataset may make the results less accurate. A more comprehensive study was carried out by Mrigank Vashist [6], who used the England Premier League dataset consisting of 380 matches and consisting of 45 features. To compare the advantages and disadvantages of algorithms on this problem, he used nine algorithms and compared them.…”

Section: Competition Results Analysismentioning

confidence: 99%

An Overview of Machine Learning Applications in the Football Field

Sun

2023

ACE

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Machine learning has become one of the hottest research directions in computing today and is widely used in various fields, including the field of football, which is considered the worlds number one sport. The application of machine learning in the football field has brought great convenience to players, coaches, club managers, spectators, and various professions engaged in the football industry, driving the development of the football industry. As the applications of machine learning are continuously developed and explored, machine learning has a broad development prospect in the field of football. This paper outlines various applications of machine learning in the match process, football odds, and team management, analyzes the shortcomings of previous research, and explores the future directions for the development of machine learning in the field of football.

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Section: Competition Results Analysismentioning

confidence: 99%