Deep soccer analytics: learning an action-value function for evaluating soccer players

Liu, Guiliang; Luo, Yadan; Kharrat, Tarak

doi:10.1007/s10618-020-00705-9

Cited by 44 publications

(25 citation statements)

References 20 publications

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“…Basketball and data should be effectively combined to establish a complete data analysis system to achieve the purpose of high-quality player selection [9]. Liu G et al concluded by studying deep learning in real-time analysis of basketball sports data that, in today's big data era, a team's completeness, accuracy, and systematization of data collection in basketball games and the results of its analysis will not only make the management mode of basketball data change, but also indirectly affect the level to which basketball games will develop in the future [10]. erefore, the current basketball tournament should first establish the awareness of information technology data management, apply relevant data collection and analysis software, and perfect a data query platform to provide new momentum for the development of basketball [11].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Analysis of Basketball Sports Data Based on Deep Learning

Peng

2021

Complexity

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This paper focuses on the theme of the application of deep learning in the field of basketball sports, using research methods such as literature research, video analysis, comparative research, and mathematical statistics to explore deep learning in real-time analysis of basketball sports data. The basketball posture action recognition and analysis system proposed for basketball movement is composed of two parts serially. The first part is based on the bottom-up posture estimation method to locate the joint points and is used to extract the posture sequence of the target in the video. The second part is the analysis and research of the action recognition algorithm based on the convolution of the space-time graph. According to the extracted posture sequence, the basketball action of the set classification is recognized. In order to obtain more accurate and three-dimensional information, a multitraining target method can be used in training; that is, multiple indicators can be detected and feedback is provided at the same time to correct player errors in time; the other is an auxiliary method, which is compared with ordinary training. The method can actively correct technical movements, train players to form muscle memory, and improve their abilities. Through the research of this article, it provides a theoretical basis for promoting the application of deep learning in the field of basketball and also provides a theoretical reference for the wider application of deep learning in the field of sports. At the same time, the designed real-time analysis system of basketball data also provides more actual reference values for coaches and athletes.

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Section: Related Workmentioning

confidence: 99%

Real-Time Analysis of Basketball Sports Data Based on Deep Learning

Peng

2021

Complexity

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“…The combination of RL techniques with deep learning has great potential to tackle the idiosyncrasies of football and to close the gap between statistical and human analysis. It is exciting to see recent progress in this direction in sports analytics, including ice hockey (Liu & Schulte, 2018) and football (Liu, Luo, Schulte, & Kharrat, 2020;X. Sun, Davis, Schulte, & Liu, 2020) (with additional related works detailed in Appendix A).…”

Section: Statistical Learningmentioning

confidence: 99%

Game Plan: What AI can do for Football, and What Football can do for AI

Tuyls

Omidshafiei²,

Müller³

et al. 2021

jair

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The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

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“…Recent advances in the analysis of soccer dynamics have been accomplished with the developments registered in the area of sports analytics [ 28 , 29 ]. Sports analytics consists of the mathematical and statistical analysis of data related to sports, with the objective of providing a competitive advantage to a team or an individual.…”

Section: Introductionmentioning

confidence: 99%

Uniform Manifold Approximation and Projection Analysis of Soccer Players

Lopes

Machado

2021

Entropy

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In professional soccer, the choices made in forming a team lineup are crucial for achieving good results. Players are characterized by different skills and their relevance depends on the position that they occupy on the pitch. Experts can recognize similarities between players and their styles, but the procedures adopted are often subjective and prone to misclassification. The automatic recognition of players’ styles based on their diversity of skills can help coaches and technical directors to prepare a team for a competition, to substitute injured players during a season, or to hire players to fill gaps created by teammates that leave. The paper adopts dimensionality reduction, clustering and computer visualization tools to compare soccer players based on a set of attributes. The players are characterized by numerical vectors embedding their particular skills and these objects are then compared by means of suitable distances. The intermediate data is processed to generate meaningful representations of the original dataset according to the (dis)similarities between the objects. The results show that the adoption of dimensionality reduction, clustering and visualization tools for processing complex datasets is a key modeling option with current computational resources.

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Deep soccer analytics: learning an action-value function for evaluating soccer players

Cited by 44 publications

References 20 publications

Real-Time Analysis of Basketball Sports Data Based on Deep Learning

Real-Time Analysis of Basketball Sports Data Based on Deep Learning

Game Plan: What AI can do for Football, and What Football can do for AI

Uniform Manifold Approximation and Projection Analysis of Soccer Players

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