TacSIm: A Dataset and Benchmark for Football Tactical Style Imitation

2026-03-26

AI Summary

This work addresses the limitation of existing football imitation studies, which primarily optimize for scoring or win rates and thus fail to replicate authentic team tactical styles. We propose the first explicit task formulation and evaluation protocol specifically designed for whole-team tactical style imitation. To support this, we construct a large-scale dataset extracted from Premier League broadcast videos, capturing the positions and motions of all 22 players and mapping them onto a standardized pitch coordinate system. Tactical consistency in spatiotemporal patterns during offensive and defensive scenarios is quantified through similarity metrics based on spatial occupancy and movement vectors. Integrating broadcast video parsing, multi-agent modeling, coordinate projection, and a unified simulation environment, our framework establishes a benchmark platform enabling both quantitative and visual evaluation, facilitating effective comparison of baseline methods in terms of tactical coordination.

Technology Category

• Multiagent Systems: Teamwork
• Humans and AI: Teamwork, Team formation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Current football imitation research primarily aims to opti mize reward-based objectives, such as goals scored or win rate proxies, paying less attention to accurately replicat ing real-world team tactical behaviors. We introduce Tac SIm, a large-scale dataset and benchmark for Tactical Style Imitation in football. TacSIm imitates the acitons of all 11 players in one team in the given broadcast footage of Pre mier League matches under a single broadcast view. Under a offensive or defensive broadcast footage, TacSIm projects the beginning positions and actions of all 22 players from both sides onto a standard pitch coordinate system. Tac SIm offers an explicit style imitation task and evaluation protocols. Tactics style imitation is measured by using spatial occupancy similarity and movement vector similarity in defined time, supporting the evaluation of spatial and tem poral similarities for one team. We run multiple baseline methods in a unified virtual environment to generate full team behaviors, enabling both quantitative and visual as sessment of tactical coordination. By using unified data and metrics from broadcast to simulation, TacSIm estab lishes a rigorous benchmark for measuring and modeling style-aligned tactical imitation task in football.

Problem

• tactical style imitation
• football
• team behavior
• spatial-temporal similarity
• imitation learning

Innovation

• tactical style imitation
• football AI
• spatial occupancy similarity
• multi-agent behavior modeling
• broadcast-to-simulation benchmark