Team-Builder: Toward More Effective Lineup Selection in Soccer

Anqi Cao, Ji Lan, Xiao Xie, Hongyu Chen, Xiaolong (Luke) Zhang, Hui Zhang, Yingcai Wu

DOI: 10.1109/TVCG.2022.3207147

Room: 106

2023-10-25T22:48:00ZGMT-0600Change your timezone on the schedule page
2023-10-25T22:48:00Z
Exemplar figure, described by caption below
System user interface. The interface consists of three views: a tactic view (A), a player view (B), and a lineup view (C). The tactic view provides confrontation tactic lists (A1, A2, A3, A4) to navigate tactics used in the lineup. The player view contains a lineup edit board (B1) for lineup generation, a candidate player list (B2) for player constraint identification, and an explanation component for comprehension of the reason of the selection of a player. The lineup view includes a candidate lineup list (C1) and lineup thumbnails (C2) for comparing multiple lineups.
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Keywords

Sports visualization;lineup selection;design study

Abstract

Lineup selection is an essential and important task in soccer matches. To win a match, coaches must consider various factors and select appropriate players for a planned formation. Computation-based tools have been proposed to help coaches on this complex task, but they are usually based on over-simplified models on player performances, do not support interactive analysis, and overlook the inputs by coaches. In this paper, we propose a method for visual analytics of soccer lineup selection by tackling two challenges: characterizing essential factors involved in generating optimal lineup, and supporting coach-driven visual analytics of lineup selection. We develop a lineup selection model that integrates such important factors, such as spatial regions of player actions and defensive interactions with opponent players. A visualization system, Team-Builder, is developed to help coaches control the process of lineup generation, explanation, and comparison through multiple coordinated views. The usefulness and effectiveness of our system are demonstrated by two case studies on a real-world soccer event dataset.