Let’s Penetrate the Defense: A Machine Learning Model for Prediction and Valuation of Penetrative Passes

Rahimian, Pegah; Gomes, Dayana Grayce da Silva Guerra; Berkovics, Fanni; Toka, László

doi:10.1007/978-3-031-27527-2_4

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The motivation for this prioritization is to maximize the recall of categories that are considered important by practitioners. For example, since penetrations are closely related to scoring opportunities, many coaches in professional clubs extract and analyze situations with penetrative passes or runs [Mychalczyk, 2020;Rahimian et al, 2022]. Likewise, it is widely accepted that effective pressing was a tactical key of many successful clubs such as Jürgen Klopp's Liverpool FC and Gian Piero Gasperini's Atalanta BC.…”

Section: Quantifying the Taxonomy And A Rule-basedmentioning

confidence: 99%

Republic of Korea (South Korea)

Kim¹

The Crime of Aggression

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Despite recent progress in semantic image synthesis, complete control over image style remains a challenging problem. Existing methods require reference images to feed style information into semantic layouts, which indicates that the style is constrained by the given image. In this paper, we propose a model named RUCGAN for user controllable semantic image synthesis, which utilizes a singular color to represent the style of a specific semantic region. The proposed network achieves reference-free semantic image synthesis by injecting color as userdesired styles into each semantic layout, and is able to synthesize semantic images with unusual colors. Extensive experimental results on various challenging datasets show that the proposed method outperforms existing methods, and we further provide an interactive UI to demonstrate the advantage of our approach for style controllability. The codes and UI are available at: https://github.com/BenjaminJonghyun/RUCGAN

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Section: Quantifying the Taxonomy And A Rule-basedmentioning

confidence: 99%

Republic of Korea (South Korea)

Kim¹

The Crime of Aggression

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Pass Receiver and Outcome Prediction in Soccer Using Temporal Graph Networks

Rahimian,

Kim,

Schmid

et al. 2024

Communications in Computer and Information Science

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In-game soccer outcome prediction with offline reinforcement learning

Rahimian,

Mihalyi,

Toka

2024

Mach Learn

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Predicting outcomes in soccer is crucial for various stakeholders, including teams, leagues, bettors, the betting industry, media, and fans. With advancements in computer vision, player tracking data has become abundant, leading to the development of sophisticated soccer analytics models. However, existing models often rely solely on spatiotemporal features derived from player tracking data, which may not fully capture the complexities of in-game dynamics. In this paper, we present an end-to-end system that leverages raw event and tracking data to predict both offensive and defensive actions, along with the optimal decision for each game scenario, based solely on historical game data. Our model incorporates the effectiveness of these actions to accurately predict win probabilities at every minute of the game. Experimental results demonstrate the effectiveness of our approach, achieving an accuracy of 87% in predicting offensive and defensive actions. Furthermore, our in-game outcome prediction model exhibits an error rate of 0.1, outperforming counterpart models and bookmakers’ odds.

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Let’s Penetrate the Defense: A Machine Learning Model for Prediction and Valuation of Penetrative Passes

Cited by 3 publications

References 5 publications

Republic of Korea (South Korea)

Republic of Korea (South Korea)

Pass Receiver and Outcome Prediction in Soccer Using Temporal Graph Networks

In-game soccer outcome prediction with offline reinforcement learning

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