Machine learning approaches for analyzing time series from sports

2 December 2021, online
AI for Times Series seminar, Jesse Davis, KU Leuven
VAIA, Flanders AI Research & KU Leuven STADIUS

Currently, sports is an incredibly data rich domain as it is possible to collect massive amounts of data from both training sessions and matches. Typically, this data comes in the form of time series such as sensor data (e.g., accelerations, heart rate, GPS, etc.), event stream data, and optical tracking data.

The availability of this data has driven an explosion of interest in the automated analysis of sports. The goal of this talk is to provide an overview of this area with illustrative examples arising out of work done in my research group. I will specifically focus on three things. First, I will motivate and explain the reoccurring challenges that we have encountered when working with time series arising from the sports world. Second, I will discuss some of the work we have done in terms of analyzing sensor data about runners. Third, I will overview our work on analyzing time series arising from professional football matches

Jesse Davis

Jesse Davis is a Professor in the CS department at KU Leuven, Belgium. His research focuses on developing novel artificial intelligence, data science, machine learning, and data mining techniques, with a particular emphasis on analyzing structured data. Jesse’s passions lie in using these techniques to make sense of lifestyle data, address problems in (elite) athlete monitoring and detecting anomalies. Prior to joining KU Leuven, he obtained his bachelor’s degree from Williams College, his PhD from the University of Wisconsin, and completed a post-doc at the University of Washington.

Jesse has co-founded and serves on the board of directors for two startups: Activ84Health and RunEASI. Activ84Health is an awarding winning start-up that develops innovative technology to motivate nursing home residents to be physically active and improve their quality of life. RunEASI is a recently launched company that aims to provide real-time biomechanical feedback about running, particularly in the context of rehabilitation.

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AI for Time Series Seminars

Several research groups in the Flanders AI Research Program (FLAIR) conduct world-class research on time series, both in the development of algorithms and tools, as in a wide area of application fields. In a recent poll in the Flanders AI community, ‘time series’ came up as the most wanted topic for future workshops or courses. With this seminar series, we bring together researchers that are interested in, or are conducting research related to, time series. We offer a varied program of national and international speakers.

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