Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data

Decroos, Tom; Haaren, Jan Van; Davis, Jesse

doi:10.1145/3219819.3219832

Cited by 74 publications

(52 citation statements)

References 15 publications

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“…CatBoost We used the official Python implementation from Yandex. 9 We set all parameters to their default values, except for the number of parallel threads, which we set to 40.…”

Section: A3 Experimental Setup and Implementationmentioning

confidence: 99%

Actions Speak Louder than Goals

Decroos

Bransen²,

Haaren³

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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124

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Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in which the actions occurred. This paper introduces (1) a new language for describing individual player actions on the pitch and (2) a framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. By aggregating soccer players' action values, their total offensive and defensive contributions to their team can be quantified. We show how our approach considers relevant contextual information that traditional player evaluation metrics ignore and present a number of use cases related to scouting and playing style characterization in the 2016/2017 and 2017/2018 seasons in Europe's top competitions.

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“…CatBoost We used the official Python implementation from Yandex. 9 We set all parameters to their default values, except for the number of parallel threads, which we set to 40.…”

Section: A3 Experimental Setup and Implementationmentioning

confidence: 99%

Actions Speak Louder than Goals

Decroos

Bransen²,

Haaren³

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

Self Cite

124

View full text Add to dashboard Cite

show abstract

“…The availability of massive data portraying soccer performance has facilitated recent advances in soccer analytics. The so-called soccer-logs [4,15,40,46], capturing all the events occurring during a match, are one of the most common data formats and have been used to analyze many aspects of soccer, both at team [8,11,25,35,50] and individual levels [6,12,33]. Among all the open problems in soccer analytics, the data-driven evaluation of a player's performance quality is the most challenging one, given the absence of a ground-truth for that performance evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…Our survey consisted of a set of pairs of players randomly generated by a two-step procedure, defined as follows: First, we randomly selected 35% of the players in the dataset. Second, for each selected player u, we cyclically iterated over the ranges [1,10], [11,20], and [21, ∞] and selected one value, say x, for each of these ranges, and then picked the player being x positions above u and the one being x positions below u in the role-based ranking (if they exist). This generated a set P of 211 pairs involving 202 distinct players.…”

Section: Validation Of Playerankmentioning

confidence: 99%

“…We found that c maj = 68% and c una = 74%, indicating that PlayeRank has in general a good agreement with the soccer scouts, compared to the random choice (for which c maj = c una = 50%). Figure 11 offers a more detailed view on the results of the survey by specializing c maj and c una on the three ranges of ranking differences: [1,10], [11,20], [21, ∞]. The bars show a clear and strong correlation between the concordance among scouts' evaluations (per majority or unanimity) and the difference between the positions in the ranking of the checked pairs of players: When the ranking difference is ≤10 it is c maj = 59% and c una = 61%; for larger and larger ranking differences, PlayeRank achieves a much higher concordance with experts, which is up to c maj = 86% and c una = 91% when the ranking difference is ≥20.…”

Section: Validation Of Playerankmentioning

confidence: 99%

“…On the one hand, many sports companies, websites, and television broadcasters-such as Opta, WhoScored.com, and Sky, as well as the plethora of online platforms for fantasy football and e-sports-widely use soccer statistics to compare the performance of professional players with the purpose of increasing fan engagement via critical analyses, insights, and scoring patterns. On the other hand, coaches and team managers are interested in analytic tools to support tactical analysis and monitor the quality of their players during individual matches or entire seasons [11,27]. Not least, soccer scouts and performance analysts are continuously looking for data-driven tools to improve the retrieval of talented players with desired characteristics, based on evaluation criteria that take into account the complexity and the multi-dimensional nature of soccer performance.…”

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confidence: 99%

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PlayeRank

Pappalardo

Cintia

Ferragina

et al. 2019

ACM Trans. Intell. Syst. Technol.

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The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank-i.e. searching players and player versatility-showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics. 59:2 L. Pappalardo et al. INTRODUCTIONRankings of soccer players and data-driven evaluations of their performance are becoming more central in the soccer industry [4,14,15,20,32,34,40,46]. On the one hand, many sports companies, websites, and television broadcasters-such as Opta, WhoScored.com, and Sky, as well as the plethora of online platforms for fantasy football and e-sports-widely use soccer statistics to compare the performance of professional players with the purpose of increasing fan engagement via critical analyses, insights, and scoring patterns. On the other hand, coaches and team managers are interested in analytic tools to support tactical analysis and monitor the quality of their players during individual matches or entire seasons [11,27]. Not least, soccer scouts and performance analysts are continuously looking for data-driven tools to improve the retrieval of talented players with desired characteristics, based on evaluation criteria that take into account the complexity and the multi-dimensional nature of soccer performance. While selecting talents on the entire space of soccer players is unfeasible (if not impossible!) for humans, data-driven performance scores help select a small subset of the best players who meet specific constraints (e.g., age, performance features and trends, roles). This allows scouts and performance analysts to analyze a smaller set of players, thus saving considerable time and economic resources while broadening scouting operations and career opportunities of talented players.The problem of data-driven evaluation of player performance is gaining interest in the scientific community, too, thanks to t...

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