The application of visual tracking to the performance analysis of sports
players in dynamic competitions is vital for effective coaching. In racket
sports, most previous studies have focused on analyzing and assessing singles
players without occlusion in broadcast videos and discrete representations
(e.g., stroke) that ignore meaningful spatial distributions. In this work, we
present the first annotated drone dataset from top and back views in badminton
doubles and propose a framework to estimate the control area probability map,
which can be used to evaluate teamwork performance. We present an efficient
framework of deep neural networks that enables the calculation of full
probability surfaces, which utilizes the embedding of a Gaussian mixture map of
players' positions and graph convolution of their poses. In the experiment, we
verify our approach by comparing various baselines and discovering the
correlations between the score and control area. Furthermore, we propose the
practical application of assessing optimal positioning to provide instructions
during a game. Our approach can visually and quantitatively evaluate players'
movements, providing valuable insights into doubles teamwork.

本研究提出了一种基于视觉跟踪和深度神经网络的框架，通过控制区域概率图来评估双打比赛中的团队合作表现，并验证了其在实验室中的有效性，可以为比赛提供有价值的位置评估和指导。

使用顶部和背部视角的无人机视频姿态信息估计羽毛球双打的控制区域

Estimation of control area in badminton doubles with pose information  from top and back view drone videos

The transfer fees of sports players have become astronomical. This is because
bringing players of great future value to the club is essential for their
survival. We present a case study on the key factors affecting the world's top
soccer players' transfer fees based on the FIFA data analysis. To predict each
player's market value, we propose an improved LightGBM model by optimizing its
hyperparameter using a Tree-structured Parzen Estimator (TPE) algorithm. We
identify prominent features by the SHapley Additive exPlanations (SHAP)
algorithm. The proposed method has been compared against the baseline
regression models (e.g., linear regression, lasso, elastic net, kernel ridge
regression) and gradient boosting model without hyperparameter optimization.
The optimized LightGBM model showed an excellent accuracy of approximately 3.8,
1.4, and 1.8 times on average compared to the regression baseline models, GBDT,
and LightGBM model in terms of RMSE. Our model offers interpretability in
deciding what attributes football clubs should consider in recruiting players
in the future.

通过 FIFA 数据分析，本文针对足球运动员转会费用的主要影响因素，提出了基于 LightGBM 模型的改进方法，采用树形 Parzen 算法进行超参优化，并通过 SHapley Additive exPlanations 算法进行特征筛选。与基线回归模型和梯度提升模型相比，优化后的 LightGBM 模型以 RMSE 为标准的表现约为回归基线模型、GBDT 和 LightGBM 模型的 3.8 倍、1.4 倍和 1.8 倍，具有很好的准确性。该模型提供了运动俱乐部在未来招募球员时所需考虑的属性解释。