Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset (2306.15664v3)

Abstract: In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection. While there is a line of effective applications to improve and investigate player performance, there are only a few public badminton datasets that can be used by researchers outside the badminton domain. Existing badminton singles datasets focus on specific matchups; however, they cannot provide comprehensive studies on different players and various matchups. In this paper, we provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies in the training set, 1,400 strokes in 450 rallies in the validation set, and 2,040 strokes in 654 rallies in the testing set, with detailed stroke-level metadata within a rally. To benchmark existing work with ShuttleSet22, we hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge @ IJCAI 2023, to encourage researchers to tackle this real-world problem through innovative approaches and to summarize insights between the state-of-the-art baseline and improved techniques, exchanging inspiring ideas. The baseline codes and the dataset are made available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023.

• The paper introduces ShuttleSet22, a comprehensive dataset with over 30,000 strokes that serves as a robust benchmark for stroke forecasting in badminton.
• The methodology leverages transformer-based architectures and cross-entropy evaluation, showing significant improvements in shot-type prediction metrics.
• The findings highlight the transformative potential of AI-driven analytics in refining badminton coaching strategies and enhancing gameplay performance.

Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset

The paper entitled "Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset" presents a novel contribution to the domain of sports analytics with an emphasis on badminton. The research introduces a large-scale dataset, ShuttleSet22, composed of stroke-level metadata gathered from elite badminton singles matches in 2022. This data serves as a comprehensive resource for evaluating the effectiveness of various AI techniques applied to the domain of sports analytics.

Dataset Composition and Challenges

ShuttleSet22 is meticulously collected, offering more than 30,000 strokes across multiple rallies, divided into training, validation, and testing sets. The dataset represents a significant advancement over previous datasets by including a diversity of matchups beyond singular player pairings, thereby allowing for broader cross-comparative analyses. As the underlying data was sourced from public match footage, the compilation process included detailed labeling managed by domain experts.

CoachAI Badminton Challenge

In collaboration with IJCAI 2023, the authors organized the CoachAI Badminton Challenge, specifically Track 2, designed to invigorate research in forecasting future strokes in badminton rallies. The challenge attracted nearly 100 participants, with 16 competitive entries contributing to a vibrant exchange of ideas and methodologies. The challenge provided a state-of-the-art baseline model which leverages a position-aware fusion of transformer-based architectures, ShuttleNet, to predict future stroke outcomes.

Evaluation and Methodology

The challenge's evaluation applied cross-entropy for shot-type prediction and mean absolute error (MAE) for area coordinate prediction, requiring participants to generate multiple prediction sequences, enhancing robustness against stochastic variations. The baseline, ShuttleNet, served as the platform for various innovative enhancements.

Key Findings

The results from CoachAI Badminton Challenge demonstrate several significant insights:

• Multiple teams surpassed the baseline model for shot-type prediction (highlighting a notable improvement from 2.1777 to 1.7892 in cross-entropy scores).
• Improvements in area prediction were more conservative, suggesting challenges in integrating predictive models for both shot type and location.
• Successful strategies often involved incremental yet impactful refinements to existing frameworks like ShuttleNet, underscoring its flexible architecture and potential for augmentation.

Practical and Theoretical Implications

This paper provides an essential contribution to the application of advanced AI techniques in sports, specifically within badminton. ShuttleSet22 not only facilitates performance evaluation of current AI models but also inspires further research into nuanced modeling of intricate player interactions and situational predictions. It opens avenues for refining strategic elements of gameplay through AI-driven insights, potentially transforming coaching and performance optimization approaches.

Future Directions

Future research avenues may explore enhancing integration between shot-type and location predictions, improving prediction accuracies. Expanding dataset collections to include additional metadata or longitudinal performance metrics could also provide deeper insights into player behaviors. AI-driven sports analytics, such as those exemplified by this paper, promise to progress toward more sophisticated adaptive strategies that reflect the dynamic nature of professional sports.

In summary, the paper showcases a structured contribution to sports analytics, introducing a vital resource for further exploration and encouraging interdisciplinary collaboration between AI researchers and sports professionals.