Why Sports Analytics?
Sports analytics had its public breakthrough as early as the 1970s when baseball enthusiasts started developing a range of statistical tools for analyzing players, teams, and strategies. Due to a combination of early successes, increased computational power and advanced automated data collection methods, sports analytics has been a steadily growing area in the last decade. Nowadays, sports analytics has found its way in other professional sports and clubs have started hiring performance analysts whose main task is to analyze the large quantities of data that are being collected.
The discipline is no longer restricted to designing new statistics and building simple statistical models. The complexity and the large quantities of data that are available nowadays involve many relevant machine learning and data mining challenges. For example, in fluid sports, such as basketball and soccer, it is hard to distinguish between events which are not necessarily sequential and have complex interrelations. Sophisticated machine learning and data minining techniques are needed to extract knowledge from this data.
Research topics
Soccer
- Identifying players' playing styles from play-by-play data
- Automatically generating match reports from play-by-play data
- Predicting match outcomes using raw statistics and match ratings
- Predicting and preventing injuries using test results and workloads
Running
- Fatigue detection
- Predicting impact on joints, muscles and bones
Basketball
- Predicting match outcomes using raw statistics
- Identifying playing styles of both teams and players
- Determining optimal player rotations using play-by-play data