Pass in Human Style: Learning Soccer Game Patterns from Spatiotemporal Data

Khaustov, Victor; Bogdan, Georgii Mola; Mozgovoy, Maxim

doi:10.1109/cig.2019.8848112

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…For example, in the case of team sports games (such as soccer, basketball, or rugby) it might be important to know, for example, which athlete is currently possessing the ball, whether a snapshot with a ball in the air belongs to a pass or a shot on goal sequence, and so on. One realistic scenario where such information is necessary is the development of a case-based reasoning AI system for playing a sports game [6]. The AI needs to learn actions performed by the athletes in specific game situations, so both these actions and situations must be reconstructed from a series of snapshots, contained in a dataset.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Events in Spatiotemporal Soccer Data

Khaustov

Mozgovoy

2020

Applied Sciences

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Spatiotemporal datasets based on player tracking are widely used in sports analytics research. Common research tasks often require the analysis of game events, such as passes, fouls, tackles, and shots on goal. However, spatiotemporal datasets usually do not include event information, which means it has to be reconstructed automatically. We propose a rule-based algorithm for identifying several basic types of events in soccer, including ball possession, successful and unsuccessful passes, and shots on goal. Our aim is to provide a simple procedure that can be used for practical soccer data analysis tasks, and also serve as a baseline model for algorithms based on more advanced approaches. The resulting algorithm is fast, easy to implement, achieves high accuracy on the datasets available to us, and can be used in similar scenarios without modification.

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Section: Introductionmentioning

confidence: 99%

Recognizing Events in Spatiotemporal Soccer Data

Khaustov

Mozgovoy

2020

Applied Sciences

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“…A practical key will facilitate the acceptance of our proposal by our acquaintances. The passing pattern in football is a fundamental aspect of the team's strategic conduct (Khaustov, Bogdan, and Mozgovoy, 2019).…”

Section: Discussionmentioning

confidence: 99%

Needs Analysis in Preparing Playing Technique Skills Tests for Adolescent Football Athletes

Wismanadi,

Widodo,

Rusdiawan

2024

Compet. J. Pendidik. Kepelatihan Olahraga

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This research aims to develop a test for football-playing techniques for teenagers aged 16-20. The process involves analyzing technical skills components, conducting expert focus group discussions (FGD), developing test types, conducting first and second trials, and analyzing the results. The first trial involved 30 players and a bivariate correlation test, followed by 75 players and factor analysis. The results will be used to determine priority techniques for developing youth football technique test instruments. The research aims to improve football skills among teenagers. The result of factor analysis showed variables LLP2 (0.814), RSho2, (0.178), LSho2 (.683) on the first factor, while the variables RLP2(0.557), CP2(0.776) on the second factor. Conclusion: Left long passing (LLP), proper shooting (RSho), and left shooting (LSho) are the priority, while right long passage (RLP) and control passing are the second priority.

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“… Tracking Data: It is a spatio-temporal dataset [17]. This dataset will contain X and Y coordinates of the players on the field as well as the ball, with respect to the centre of the football pitch  Event Data: This is also a spatio-temporal dataset [9].…”

Section: B Player Evaluationmentioning

confidence: 99%

Football Analysis: An Automated System for Team Generation

Desai¹,

Tapiawala²,

Ram³

2021

New Frontiers in Communication and Intelligent Systems

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Football Analytics has seen major growth in the past couple of years. Many of the top football clubs have started implementing various aspects of the vast field of football analytics. The utilization of data has become particularly important with respect to player development. In such a fast-paced game, keeping track of all the players at once is an extremely difficult task. In the paper, we propose the idea of an automated system that will help the manager to pick out the team strategy, with the playing line-up, for a particular match depending on the opponent’s team tactics. The system will include 2 phases, first phase will predict the player’s playing position using cluster analysis. In the next phase, the system will use deep learning to evaluate the performance of players based on their position. Using the output from the two phases, we will predict the team strategies.

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Pass in Human Style: Learning Soccer Game Patterns from Spatiotemporal Data

Cited by 6 publications

References 9 publications

Recognizing Events in Spatiotemporal Soccer Data

Recognizing Events in Spatiotemporal Soccer Data

Needs Analysis in Preparing Playing Technique Skills Tests for Adolescent Football Athletes

Football Analysis: An Automated System for Team Generation

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