The world of sports analytics is experiencing a transformation, thanks to advanced technologies like Temporal Convolutional Networks (TCNs)
Read more: The Role of Temporal Convolutional Networks in Ball Possession AnalysisA study, “Using Temporal Convolutional Networks to estimate ball possession in soccer games” by Matteo Borghesi, Lorenzo Dusty Costa, Lia Morra, and Fabrizio Lamberti, published in Expert Systems with Applications, Volume 223, 2023, explores this cutting-edge approach.
The Evolution of Tracking Data in Soccer
In recent years, the use of tracking data for tactical analysis in soccer has surged. Traditionally, analyzing ball possession has been a manual task performed by human operators, often leading to costly and error-prone results. This study introduces TCNs as a novel solution to automate and improve the accuracy of ball possession analysis.
TCNs: A Game-Changer in Sports Analytics
TCNs, known for their efficacy in sequential data analysis, are utilized to extract ball possession information from tracking data. This approach is a significant step forward in tactical analyses, offering a more efficient and error-resistant method compared to manual processes.
Methodology and Approaches
The research explores various classification approaches to determine the game state – whether the ball is with the home team, the away team, or in a dead state. This includes a single-branch, ternary prediction, and two binary predictions. The study leverages TCNs to create independent trajectory embeddings from tracking data of each object. The researchers also investigated permutation-invariant layers to combine these embeddings, such as an element-wise sum, a self-attention module, and 2D convolutions.
Impressive Results
The results are impressive. The proposed method using TCNs achieved an 86.2% accuracy in possession estimation, surpassing state-of-the-art rule-based methods by 7.3%. Furthermore, it reached 89.2% accuracy in dead-alive classification, a significant 33.2% improvement over existing methods. These findings highlight the potential of TCNs in enhancing the accuracy and reliability of sports analytics.
Implications for Soccer Analytics
This study’s findings are groundbreaking for soccer analytics. By integrating TCNs, teams and analysts can gain more accurate insights into ball possession, which is crucial for tactical planning and performance analysis. This technological advancement paves the way for more detailed and precise analytics in soccer, potentially influencing coaching strategies and game outcomes.
Why This Research Matters
In the competitive world of professional soccer, every small detail counts. The introduction of TCNs in ball possession analysis represents a significant leap in the accuracy and efficiency of sports analytics. It demonstrates how embracing new technologies can lead to more informed decisions in the sport.
Conclusion
The study by Borghesi and his team marks a pivotal moment in the intersection of sports and technology. By harnessing the power of TCNs, soccer analytics is set to become more sophisticated, providing teams with deeper insights and a competitive edge. This advancement is not just a win for the teams but also for the broader field of sports analytics, showcasing the endless possibilities of technology in sports.
Sources
Borghesi, M., Costa, L. D., Morra, L., & Lamberti, F. (2023). Using Temporal Convolutional Networks to estimate ball possession in soccer games. Expert Systems with Applications, 223, 119780. https://doi.org/10.1016/j.eswa.2023.119780.