Automatically Discovering Offensive Patterns in Soccer Match Data

Haaren, Jan Van; Dzyuba, Vladimir; Hannosset, Siebe; Davis, Jesse

doi:10.1007/978-3-319-24465-5_25

Cited by 18 publications

(15 citation statements)

References 13 publications

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“…Van Haaren et al 14 proposed an inductive logic programming approach to learn pass sequences that capture attack regularities. Game event data were also exploited by Lucey et al 15 to visualize plays that start from specific areas of the pitch as well as by Brandt and Brefeld 16 who deployed page rank algorithms on pass sequences to capture team interaction.…”

Section: Related Workmentioning

confidence: 99%

Learning to Rate Player Positioning in Soccer

Dick

Brefeld

2019

Big Data

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We investigate how to learn functions that rate game situations on a soccer pitch according to their potential to lead to successful attacks. We follow a purely data-driven approach using techniques from deep reinforcement learning to valuate multiplayer positionings based on positional data. Empirically, the predicted scores highly correlate with dangerousness of actual situations and show that rating of player positioning without expert knowledge is possible.

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Section: Related Workmentioning

confidence: 99%

Learning to Rate Player Positioning in Soccer

Dick

Brefeld

2019

Big Data

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“…In addition, sports analytics researchers have investigated the passing interactions between players as well as the passing behavior of teams (Beetz et al 2009; (Duch, Waitzman, and Nunes Amaral, 2010); Grund 2012;Van Haaren et al 2015). Furthermore, Gudmundsson and Horton (2017) provide an extensive overview of sports analytics approaches that operate on spatio-temporal match data.…”

Section: Related Workmentioning

confidence: 99%

Measuring soccer players’ contributions to chance creation by valuing their passes

Bransen¹,

Haaren²,

Velden

2019

Journal of Quantitative Analysis in Sports

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Scouting departments at soccer clubs aim to discover players having a positive influence on the outcomes of matches. Since passes are the most frequently occurring on-the-ball actions on the pitch, a natural way to achieve this objective is by identifying players who are effective in setting up chances. Unfortunately, traditional statistics such as number of assists fail to reveal players excelling in this area. To overcome this limitation, this paper introduces a novel metric that measures the players’ involvement in setting up chances by valuing the effectiveness of their passes. Our proposed metric identifies Arsenal player Mesut Özil as the most impactful player in terms of passes during the 2017/2018 season and proposes Ajax player Frenkie de Jong as a suitable replacement for Andrés Iniesta at FC Barcelona.

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“…Sports teams will apply tactics to improve their performance, and computational methods to detect, analyse and represent tactics have been the subject of several recent research efforts [4,11,14,19,20,21]. Two manifestations of team tactics are in persistent and repeated occurrence of spatial formations of players, and in plays -a coordinated sequence of actions by players.…”

Section: Tactical Analysis In Footballmentioning

confidence: 99%

Compact Flow Diagrams for State Sequences

Buchin

Gudmundsson

et al. 2017

ACM J. Exp. Algorithmics

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Abstract. We introduce the concept of compactly representing a large number of state sequences, e.g., sequences of activities, as a flow diagram. We argue that the flow diagram representation gives an intuitive summary that allows the user to detect patterns among large sets of state sequences. Simplified, our aim is to generate a small flow diagram that models the flow of states of all the state sequences given as input. For a small number of state sequences we present efficient algorithms to compute a minimal flow diagram. For a large number of state sequences we show that it is unlikely that efficient algorithms exist. More specifically, the problem is W [1]-hard if the number of state sequences is taken as a parameter. We thus introduce several heuristics for this problem. We argue about the usefulness of the flow diagram by applying the algorithms to two problems in sports analysis. We evaluate the performance of our algorithms on a football data set and generated data.

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Automatically Discovering Offensive Patterns in Soccer Match Data

Cited by 18 publications

References 13 publications

Learning to Rate Player Positioning in Soccer

Learning to Rate Player Positioning in Soccer

Measuring soccer players’ contributions to chance creation by valuing their passes

Compact Flow Diagrams for State Sequences

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