Acquiring the positioning skill in a soccer game using a fuzzy Q-learning

Nakashima, Tomoharu; Udo, Masayo; Ishibuchi, Hisao

doi:10.1109/cira.2003.1222217

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…For example, in statistical analyses of soccer, game logs -files of soccer matches -are "obtained through next-generation tracking technologies and physiological training data collected through novel miniature sensor technologies have become available for research" [7]. By encoding game logs, there is the potential to predict when an opponent will perform strategic actions like pass prediction [65].…”

Section: Statistical Elementsmentioning

confidence: 99%

“…While some ethical concerns are tied to sport analytics [16,39], there is the need to appreciate it as an insightful professional tool rather than dismissing it based on its negative aspects, a characteristic synonymous with most innovations. Alamar [79] notes that "as data becomes more accessible, decision-makers have found clearer insight into their organizations and the nature of the decisions they face through the use of metrics that did not exist even a few years ago" (p. 65). Within that context, data collected through the sport analytics process is subject to legislation, including, for example, the Privacy Act 1988 (Privacy Act) in Australia and international legislation such as SOC 2 (Service Organization Control Type 2) cybersecurity compliance frameworks designed to ensure that third-party service providers (e.g., sport data analytics companies) store and process sensitive data in a secure manner [80].…”

Section: Technology In Sports -Recent Advances New Perspectives and A...mentioning

confidence: 99%

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Sport Analytics: Graduating From Alchemy

Mountifield

2023

Technology in Sports - Recent Advances, New Perspectives and Application

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Sport analytics allows sport teams and organizations to improve performance and associated business decisions. There is an increasing demand for sport analytics, in part connected to the emergence of Big Data, resulting in a new discipline in the sport industry. Business models related to sport analytics offer the opportunity to analyze the performance of athletes, teams, clubs, and sport organizations. The burgeoning yet competitive objectives based on sport analytics explain, to a degree, why it is rare to find algorithms, predictive models, and other statistical methods and analyses being carried out in the public domain. This chapter first outlines topical views of the developing field of sport analytics that suggest that its application is based on organizational self-interest, resulting in a degree of obfuscation that may limit the pursuit of knowledge. Countering these opinions, however, is evidence pointing to sport analytics becoming more mainstream and a domain of shared knowledge. The chapter provides a non-exhaustive literature review, including sections addressing statistical elements, performance optimization, theoretical frameworks, and the application of sport analytics, followed by some overall observations. Within that context, recent developments in the sport industry demonstrate that sport analytics is more than alchemy.

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Section: Statistical Elementsmentioning

confidence: 99%

Section: Technology In Sports -Recent Advances New Perspectives and A...mentioning

confidence: 99%

Sport Analytics: Graduating From Alchemy

Mountifield

2023

Technology in Sports - Recent Advances, New Perspectives and Application

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“…During the last 10 years, RoboCup scholars have addressed the positioning problem in different ways [2][3][4][5][6][7][8]. In order to demonstrate the unique features of our approach, here we propose a generic two-level model that includes the existing methods for player positioning.…”

Section: Introductionmentioning

confidence: 99%

“…One more group of the improvements to player positioning is a set of methods based on learning algorithms [6,7,8]. Like the coordination graph, some of these methods do not treat positioning as a standalone player skill, which makes theoretical comparisons difficult.…”

Section: Introductionmentioning

confidence: 99%

Pareto-Optimal Offensive Player Positioning in Simulated Soccer

Kyrylov

Razykov

2008

RoboCup 2007: Robot Soccer World Cup XI

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The ability by the simulated soccer player to make rational decisions about moving without ball is a critical factor of success. Here we limit our scope to the offensive situation, i.e. when the ball is controlled by own team, and propose a systematic method for determining the optimal player position. Existing methods for accomplishing this task do not systematically balance risks and rewards, as they are not Pareto optimal by design. This may result in overlooking good opportunities. One more shortcoming of these methods is over simplifications in predicting the situation on the field, which may lead to performance loss. We propose two new ideas to address these issues. Experiments demonstrate that this results in a substantial increase in the team performance.

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Fuzzy Q-learning in a nondeterministic environment: developing an intelligent Ms. Pac-Man agent

DeLooze

Viner

2009

2009 IEEE Symposium on Computational Intelligence and Games

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Acquiring the positioning skill in a soccer game using a fuzzy Q-learning

Cited by 4 publications

References 8 publications

Sport Analytics: Graduating From Alchemy

Sport Analytics: Graduating From Alchemy

Pareto-Optimal Offensive Player Positioning in Simulated Soccer

Fuzzy Q-learning in a nondeterministic environment: developing an intelligent Ms. Pac-Man agent

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