# ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton

arXiv: https://arxiv.org/abs/2112.01044 · HTML: https://mlsanity.com/paper/2112.01044/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Using the Output Embedding to Improve Language Models](https://mlsanity.com/paper/1608.05859/) (2016)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs](https://mlsanity.com/paper/1904.09507/) (2019)
- [Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts](https://mlsanity.com/paper/2104.06779/) (2021)
- [Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks](https://mlsanity.com/paper/1812.09395/) (2018)
- [Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation](https://mlsanity.com/paper/1805.11088/) (2018)
- [Cracking the Black Box: Distilling Deep Sports Analytics](https://mlsanity.com/paper/2006.04551/) (2020)
- [PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach](https://mlsanity.com/paper/1802.04987/) (2018)
- [What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data](https://mlsanity.com/paper/2106.01786/) (2021)
- [Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches](https://mlsanity.com/paper/2109.06431/) (2021)
- [A Bayesian Approach to In-Game Win Probability in Soccer](https://mlsanity.com/paper/1906.05029/) (2019)
- [Optimising Game Tactics for Football](https://mlsanity.com/paper/2003.10294/) (2020)
- [CoachAI: A Project for Microscopic Badminton Match Data Collection and Tactical Analysis](https://mlsanity.com/paper/1907.12888/) (2019)
- [Autoregressive Text Generation Beyond Feedback Loops](https://mlsanity.com/paper/1908.11658/) (2019)

## Built on by (most cited first, 21 of 21)

- [Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports](https://mlsanity.com/paper/2401.01505/) (2024)
- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis](https://mlsanity.com/paper/2306.04948/) (2023)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton](https://mlsanity.com/paper/2211.12217/) (2022)
- [Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset](https://mlsanity.com/paper/2306.15664/) (2023)
- [F3Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos](https://mlsanity.com/paper/2504.08222/) (2025)
- [PlayBest: Professional Basketball Player Behavior Synthesis via Planning with Diffusion](https://mlsanity.com/paper/2306.04090/) (2023)
- [A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)](https://mlsanity.com/paper/2211.12234/) (2022)
- [ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton](https://mlsanity.com/paper/2312.10942/) (2023)
- [Benchmarking Badminton Action Recognition with a New Fine-Grained Dataset](https://mlsanity.com/paper/2403.12385/) (2024)
- [TCDformer-based Momentum Transfer Model for Long-term Sports Prediction](https://mlsanity.com/paper/2409.10176/) (2024)
- [Automated Hit-frame Detection for Badminton Match Analysis](https://mlsanity.com/paper/2307.16000/) (2023)
- [Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion](https://mlsanity.com/paper/2403.12406/) (2024)
- [ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling](https://mlsanity.com/paper/2603.17324/) (2026)
- [A Badminton Recognition and Tracking System Based on Context Multi-feature Fusion](https://mlsanity.com/paper/2306.14492/) (2023)
- [MuLMINet: Multi-Layer Multi-Input Transformer Network with Weighted Loss](https://mlsanity.com/paper/2307.08262/) (2023)
- [Team Intro to AI team8 at CoachAI Badminton Challenge 2023: Advanced ShuttleNet for Shot Predictions](https://mlsanity.com/paper/2307.13715/) (2023)
- [TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification](https://mlsanity.com/paper/2605.02558/) (2026)
- [Decoupling Spatio-Temporal Adapter for Fine-Grained Badminton Action Localization](https://mlsanity.com/paper/2605.23355/) (2026)
- [ShuttleArena: Interpretable Self-Play in Physics-Based Badminton](https://mlsanity.com/paper/2608.25246/) (2026)

## Abstract (arXiv)

The increasing demand for analyzing the insights in sports has stimulated a line of productive studies from a variety of perspectives, e.g., health state monitoring, outcome prediction. In this paper, we focus on objectively judging what and where to return strokes, which is still unexplored in turn-based sports. By formulating stroke forecasting as a sequence prediction task, existing works can tackle the problem but fail to model information based on the characteristics of badminton. To address these limitations, we propose a novel Position-aware Fusion of Rally Progress and Player Styles framework (ShuttleNet) that incorporates rally progress and information of the players by two modified encoder-decoder extractors. Moreover, we design a fusion network to integrate rally contexts and contexts of the players by conditioning on information dependency and different positions. Extensive experiments on the badminton dataset demonstrate that ShuttleNet significantly outperforms the state-of-the-art methods and also empirically validates the feasibility of each component in ShuttleNet. On top of that, we provide an analysis scenario for the stroke forecasting problem.