A Statistical Model Of Serve Return Impact Patterns In Professional Tennis With Stephanie Kovalchik Open Source Sports podcast

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A Statistical Model of Serve Return Impact Patterns in Professional Tennis with Stephanie Kovalchik

2y ago 1:05:40

M4A•Pagina principale dell'episodio

In this episode we talk to Stephanie Kovalchik about her paper 'A Statistical Model of Serve Return Impact Patterns in Professional Tennis' (co-authored with Jim Albert). Stephanie is a Staff Data Scientist at Zelus Analytics, where she works on advanced performance valuation for multiple pro sports. Before joining Zelus, Stephanie led data science innovation for the Game Insight Group of Tennis Australia, building first-of-a-kind metrics and real-time applications with tracking data. Stephanie is the founder of the tennis analytics blog "On the T" and tweets @StatsOnTheT.

For additional references mentioned in the show:

ATP Tour Second Screen
Stephanie's article in Harvard Data Science Review: Why Tennis Is Still Not Ready to Play Moneyball
Grand Slam R package: courtvisionr
Stephanie's GitHub with various resources for accessing tennis data: https://github.com/skoval
Stan tutorials: https://mc-stan.org/users/documentation/tutorials
Register now for the Carnegie Mellon Sports Analytics Conference: https://www.stat.cmu.edu/cmsac/conference/2022/
Check out the Big Data Derby now on Kaggle

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