Nov 10th, 2014, 1:00 pm in Huxley 145

How to win the next Wimbledon

I will be talking about using spatio-temporal information for the analysis of tennis matches. State-of-the-art tennis modelling techniques use player statistics such as the percentage of points won in the first serve or double faults. Advances in computer vision techniques and technology have made it possible to obtain spatio-temporal data from tennis matches. It is now possible to get the 3D position of both players and the ball, the shot speed and angle and many more characteristics. In my work, I use this data in order to build better models. For instance, one of the key differences with the state-of-the-art tennis predicting models is that we can now look at intra-point dynamics.

In this talk I will first describe the state-of-the-art approach to modelling tennis matches. Then I will give an overview of some computer vision and machine learning techniques that can be used to extract spatio-temporal data from tennis videos. Finally I will talk about how these can be used to build better tennis models.

Silvia Vinyes-Mora Silvia is a first year PhD student in the AESOP (Analysis, Engineering, Simulation & Optimization of Performance) group doing research under the supervision of William Knottenbelt. She works on video-based analysis of professional tennis matches. Previously, she pursued a BSc in Neuroscience at King's College London and an MSc in Computing Science at Imperial College London, where she was the Course Representative. In her free time she enjoys playing piano and traveling. Email to me→