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

Lotte Bransen, Jan Van Haaren and Michel van de Velden

Abstract

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.

Appendix A Qualitative analysis of the clustering step

A.1 Example 1

Figure 9: Visualization of the four nearest neighbors of the red pass when not clustering the passes before performing the k-nearest-neighbors search.

Figure 9:

Visualization of the four nearest neighbors of the red pass when not clustering the passes before performing the k-nearest-neighbors search.

Figure 10: Visualization of the four nearest neighbors of the red pass when clustering the passes with grid cells of 15 by 17 m before performing the k-nearest-neighbors search.

Figure 10:

Visualization of the four nearest neighbors of the red pass when clustering the passes with grid cells of 15 by 17 m before performing the k-nearest-neighbors search.

A.2 Example 2

Figure 11: Visualization of the four nearest neighbors of the red pass when not clustering the passes before performing the k-nearest-neighbors search.

Figure 11:

Visualization of the four nearest neighbors of the red pass when not clustering the passes before performing the k-nearest-neighbors search.

Figure 12: Visualization of the four nearest neighbors of the red pass when clustering the passes with grid cells of 15 by 17 m before performing the k-nearest-neighbors search.

Figure 12:

Visualization of the four nearest neighbors of the red pass when clustering the passes with grid cells of 15 by 17 m before performing the k-nearest-neighbors search.

A.3 Example 3

Figure 13: Visualization of the four nearest neighbors of the red pass when not clustering the passes before performing the k-nearest-neighbors search.

Figure 13:

Visualization of the four nearest neighbors of the red pass when not clustering the passes before performing the k-nearest-neighbors search.

Figure 14: Visualization of the four nearest neighbors of the red pass when clustering the passes with grid cells of 15 by 17 m before performing the k-nearest-neighbors search.

Figure 14:

Visualization of the four nearest neighbors of the red pass when clustering the passes with grid cells of 15 by 17 m before performing the k-nearest-neighbors search.

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Published Online: 2019-02-06
Published in Print: 2019-06-26

©2019 Walter de Gruyter GmbH, Berlin/Boston