Sports Analytics
7 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Sports Analytics
Most implemented papers
Sports Camera Calibration via Synthetic Data
lood339/SCCvSD
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Here we propose a highly automatic method for calibrating sports cameras from a single image using synthetic data.
Applying Deep Learning to Basketball Trajectories
RobRomijnders/RNN_basketball
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Using a dataset of over 20, 000 three pointers from NBA SportVu data, the models based simply on sequential positional data outperform a static feature rich machine learning model in predicting whether a three-point shot is successful.
Cracking the Black Box: Distilling Deep Sports Analytics
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics.
From Motor Control to Team Play in Simulated Humanoid Football
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.
DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes
In addition to the increased complexity resulting from the multiplication of single-task models, the use of the off-the-shelf models also impedes the performance due to the complexity and specificity of the team sports scenes, such as strong occlusion and motion blur.
LTC-GIF: Attracting More Clicks on Feature-length Sports Videos
This paper proposes a lightweight method to attract users and increase views of the video by presenting personalized artistic media -- i. e, static thumbnails and animated GIFs.
A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications
Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle, which supports football, basketball, table tennis and figure skating action recognition.
