In this, the final show in our AI in Sports series, I’m joined by Stephanie Kovalchik, Research Fellow at Victoria University and Senior Sports Scientist at Tennis Australia.
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Stephanie and I had a great conversation about a few of the many interesting projects underway at Tennis Australia. We look at their use of data to develop a player rating system based on ability and probability, as opposed to the current official one which is based on points scored and match results. We then get into some of the interesting products her Game Insight Group is developing, including a win forecasting algorithm, and a statistic that measures a given player’s workload during a match. Stephanie details her paper “Is there a pythagorean theorem for winning in tennis?”, which explores the development and application of a pythagorean theorem for win expectation in tennis. We also take a look at her project to develop a system for classifying “ending” shots, and an emotion tracking system that help shows the link between emotion and performance in tennis.
On July 17th at 5pm PT, Nic Teague will lead a discussion on the paper Quantum Machine Learning by Jacob Biamonte et al, which explores how to devise and implement concrete quantum software for accomplishing machine learning tasks. If you haven’t joined our meetup yet, visit twimlai.com/meetup to do so.
Also, be sure to sign up for our weekly newsletter. I recently shared a write up detailing the ML/AI Job Board we’re working on, and got a ton of encouragement and interest. To make sure you don’t miss anything, head over to twimlai.com/newsletter to sign up.
Mentioned in the Interview
- Tennis Australia
- ELO Win Probability Calculator
- Paper:Is there a Pythagorean theorem for winning in tennis?
- Bill James
- Pythagorean expectation
- AI x Sports
- TWIML Presents: Series page
- TWIML Events Page
- TWIML Meetup
- TWIML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0