Visual analysis of pressure in football

Andrienko, Gennady; Budziak, Guido; Dykes, Jason; Fuchs, Georg; Landesberger, Tatiana von; Weber, Hendrik

doi:10.1007/s10618-017-0513-2

Cited by 83 publications

(85 citation statements)

References 69 publications

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“…Thus, in the ground transportation domain [12], analysis tasks may require clustering of trajectories according to their parts on highways, or during rush hours, or within congested areas. In football analysis [11], relevant parts of players' trajectories may need to be clustered taking into account ball possession, position on the pitch, and direction and speed of the movement. As the notion of relevance may change throughout the analysis process, interactive filtering tools should enable dynamic modification of the relevance masks, and the clustering needs to be done without destroying the original trajectories due to extracting only relevant parts.…”

Section: Resultsmentioning

confidence: 99%

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Andrienko

Fuchs

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Different approach routes to London airports are represented in a 3D view by central trajectories of density-based clusters of relevant parts of flight trajectories. The images show the routes that were used in two days with different wind parameters.Abstract-Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.

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Section: Resultsmentioning

confidence: 99%

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Andrienko

Fuchs

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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“…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%

“…Clearly, the disagreement between PlayeRank with the soccer scouts is less significant when the players are close in the ranking (i.e., their distance <10). Indeed, the comparison between soccer players is a well-known difficult problem, as witnessed by the significant increase in the fraction of unanimous answers by the scouts, which goes from a low 58% in the range [1,10] to a reasonable 71% in the range [21, +∞]. This a fortiori highlights the robustness of PlayeRank: The scouts' disagreement decreases as pairs of players are farther and farther in the ranking provided by PlayeRank.…”

Section: Validation Of Playerankmentioning

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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“…Therefore, the quality of passes by means of distance, shot strength, and pressuring of the receiving player is evaluated. A general visual analysis approach for pressure in soccer matches has been introduced by Andrienko et al [5]. Perl et al [38] propose the analysis of tactical performance, based on a combination of pattern recognition with neural networks and analysis of ball possession.…”

Section: Sports Analysismentioning

confidence: 99%

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Stein

Janetzko

Lamprecht

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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130

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach. Permanent repository link

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Visual analysis of pressure in football

Cited by 83 publications

References 69 publications

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

PlayeRank

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

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