TY - GEN

T1 - "Does 4-4-2 exist?"

T2 - 2nd ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2019, co-located with ACM Multimedia 2019

AU - Müller-Budack, Eric

AU - Theiner, Jonas

AU - Rein, Robert

AU - Ewerth, Ralph

PY - 2019/10/15

Y1 - 2019/10/15

N2 - The chance to win a football match can be significantly increased if the right tactic is chosen and the behavior of the opposite team is well anticipated. For this reason, every professional football club employs a team of game analysts. However, at present game performance analysis is done manually and therefore highly time-consuming. Consequently, automated tools to support the analysis process are required. In this context, one of the main tasks is to summarize team formations by patterns such as 4-4-2 that can give insights into tactical instructions and patterns. In this paper, we introduce an analytics approach that automatically classifies and visualizes the team formation based on the players’ position data. We focus on single match situations instead of complete halftimes or matches to provide a more detailed analysis. The novel classification approach calculates the similarity based on pre-defined templates for different tactical formations. A detailed analysis of individual match situations depending on ball possession and match segment length is provided. For this purpose, a visual summary is utilized that summarizes the team formation in a match segment. An expert annotation study is conducted that demonstrates 1) the complexity of the task and 2) the usefulness of the visualization of single situations to understand team formations. The suggested classification approach outperforms existing methods for formation classification. In particular, our approach gives insights into the shortcomings of using patterns like 4-4-2 to describe team formations.

AB - The chance to win a football match can be significantly increased if the right tactic is chosen and the behavior of the opposite team is well anticipated. For this reason, every professional football club employs a team of game analysts. However, at present game performance analysis is done manually and therefore highly time-consuming. Consequently, automated tools to support the analysis process are required. In this context, one of the main tasks is to summarize team formations by patterns such as 4-4-2 that can give insights into tactical instructions and patterns. In this paper, we introduce an analytics approach that automatically classifies and visualizes the team formation based on the players’ position data. We focus on single match situations instead of complete halftimes or matches to provide a more detailed analysis. The novel classification approach calculates the similarity based on pre-defined templates for different tactical formations. A detailed analysis of individual match situations depending on ball possession and match segment length is provided. For this purpose, a visual summary is utilized that summarizes the team formation in a match segment. An expert annotation study is conducted that demonstrates 1) the complexity of the task and 2) the usefulness of the visualization of single situations to understand team formations. The suggested classification approach outperforms existing methods for formation classification. In particular, our approach gives insights into the shortcomings of using patterns like 4-4-2 to describe team formations.

KW - Annotation study

KW - Formation classification

KW - Pattern analysis

KW - Sports analytics

UR - http://www.scopus.com/inward/record.url?scp=85075745754&partnerID=8YFLogxK

U2 - 10.1145/3347318.3355527

DO - 10.1145/3347318.3355527

M3 - Conference contribution

AN - SCOPUS:85075745754

SP - 25

EP - 33

BT - MMSports 2019

Y2 - 25 October 2019 through 25 October 2019

ER -