ELASTIC: Event-Tracking Data Synchronization in Soccer Without Annotated Event Locations

2025-08-12

AI Summary

Synchronizing football event data with player tracking data remains challenging due to temporal and spatial inaccuracies inherent in manual event annotation; existing approaches rely on error-prone manual localization of events, leading to misalignment. This paper proposes an end-to-end synchronization framework that eliminates the need for manual position annotations by leveraging only player trajectory features. It introduces the first explicit modeling and detection of the termination instants of pass-type events and incorporates a primary-secondary event separation mechanism to mitigate error propagation. The method integrates trajectory-driven feature extraction, temporal alignment algorithms, and event-level decoupling strategies. Evaluated on 2,134 events across three Eredivisie matches, the approach achieves significantly higher synchronization accuracy than state-of-the-art methods, while markedly improving both data completeness and temporal alignment fidelity.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Time-Series/Data Streams
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The integration of event and tracking data has become essential for advanced analysis in soccer. However, synchronizing these two modalities remains a significant challenge due to temporal and spatial inaccuracies in manually recorded event timestamps. Existing synchronizers typically rely on annotated event locations, which themselves are prone to spatial errors and thus can distort synchronization results. To address this issue, we propose ELASTIC (Event-Location-AgnoSTIC synchronizer), a synchronization framework that only uses features derived from tracking data. ELASTIC also explicitly detects the end times of pass-like events and separates the detection of major and minor events, which improves the completeness of the synchronized output and reduces error cascade across events. We annotated the ground truth timestamps of 2,134 events from three Eredivisie matches to measure the synchronization accuracy, and the experimental results demonstrate that ELASTIC outperforms existing synchronizers by a large margin.

Problem

• Synchronizing soccer event and tracking data without annotated locations
• Detecting pass-like event end times to improve synchronization accuracy
• Reducing error cascade by separating major and minor event detection

Innovation

• Uses tracking data features only
• Detects pass-like event end times
• Separates major and minor events