Exploring Successful Team Tactics in Soccer Tracking Data

Meerhoff, Laurentius Antonius; Goes, F R; Leeuw, Arie-Willem de; Knobbe, Arno

doi:10.1007/978-3-030-43887-6_18

Cited by 5 publications

(7 citation statements)

References 22 publications

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“…Along these lines, coaches' input should be prioritized in the development of KPIs which represent success in the context of play. This includes knowledge about specific players, various playing styles, and making comparisons between opposing teams (Meerhoff et al, 2019). As such, one limitation of our study is the lack of KPI normalization.…”

Section: Use and Value Of Kpismentioning

confidence: 99%

“…Moreover, these mathematically based measures need further refining to be represented in a way that can be understood and utilized in practice (Drust and Green, 2013). Practitioners vary in their definition of success and might be interested in different indicators based on their preferred game style and formation (Meerhoff et al, 2019;Memmert et al, 2017). Also, tactical analysis has increased in complexity (Rein and Memmert, 2016b) and the sophistication of KPIs has grown substantially since earlier key work on performance analysis (Nelson and Groom, 2012;Wright et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Attacking Key Performance Indicators in Soccer: Current Practice and Perceptions from the Elite to Youth Academy Level

Herold¹,

Kempe²,

Bauer³

et al. 2021

jsportscimed

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Key Performance Indicators (KPIs) are used to evaluate the offensive success of a soccer team (e.g. penalty box entries) or player (e.g. pass completion rate). However, knowledge transfer from research to applied practice is understudied. The current study queried practitioners (n = 145, mean ± SD age: 36 ± 9 years) from 42 countries across different roles and levels of competition (National Team Federation to Youth Academy levels) on various forms of data collection, including an explicit assessment of twelve attacking KPIs. 64.3% of practitioners use data tools and applications weekly (predominately) to gather KPIs during matches. 83% of practitioners use event data compared to only 52% of practitioners using positional data, with a preference for shooting related KPIs. Differences in the use and value of metrics derived from positional tracking data (including Ball Possession Metrics) were evident between job role and level of competition. These findings demonstrate that practitioners implement KPIs and gather tactical information in a variety of ways with a preference for simpler metrics related to shots. The low perceived value of newer KPIs afforded by positional data could be explained by low buy-in, a lack of education across practitioners, or insufficient translation of findings by experts towards practice.

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Section: Use and Value Of Kpismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attacking Key Performance Indicators in Soccer: Current Practice and Perceptions from the Elite to Youth Academy Level

Herold¹,

Kempe²,

Bauer³

et al. 2021

jsportscimed

View full text Add to dashboard Cite

show abstract

“…Synthesis of empirical games over the possible meta-strategies of two opposing teams can be used to forecast the performance of various team tactics when pitted against one another (e.g., investigating the Nash equilibrium of football at a higher level, rather than the typically considered low-level scenarios such as penalty kicks). Moreover, while characterization and ranking of players has received considerable attention in the literature (Bransen & Van Haaren, 2019;, automated ranking of tactics has received considerably less attention (Decroos, Van Haaren, & Davis, 2018;Meerhoff, Goes, Knobbe, et al, 2019). Application of game-theoretic analysis techniques here remains unexplored to the best of our knowledge.…”

Section: Predictive Modeling and Decision-makingmentioning

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).

show abstract

“…Synthesis of empirical games over the possible meta-strategies of two opposing teams can be used to forecast the performance of various team tactics when pitted against one another (e.g., investigating the Nash equilibrium of football at a higher level, rather than the typically considered low-level scenarios such as penalty kicks). Moreover, while characterization and ranking of players has received considerable attention in the literature Decroos, Bransen, Van Haaren, & Davis, 2019;, automated ranking of tactics has received considerably less attention (Decroos, Van Haaren, & Davis, 2018;Meerhoff, Goes, Knobbe, et al, 2019). Application of game-theoretic analysis techniques here remains unexplored to the best of our knowledge.…”

Section: Predictive Modeling and Decision-makingmentioning

confidence: 99%

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

Tuyls¹,

Omidshafiei²,

Müller³

et al. 2020

Preprint

View full text Add to dashboard Cite

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-theart 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).

show abstract

Exploring Successful Team Tactics in Soccer Tracking Data

Cited by 5 publications

References 22 publications

Attacking Key Performance Indicators in Soccer: Current Practice and Perceptions from the Elite to Youth Academy Level

Attacking Key Performance Indicators in Soccer: Current Practice and Perceptions from the Elite to Youth Academy Level

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

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

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