We discuss 'Player Chemistry: Striving for a Perfectly Balanced Soccer Team' with Lotte Bransen. This paper builds on the VAEP framework previously introduced Lotte and her colleagues, in order to quantify player chemistry. Our discussion covers details of the paper along with general challenges of estimating player chemistry in soccer and other sports, as well as the importance of interpretable machine learning.

Lotte Bransen (@LotteBransen) is a Lead Data Scientist at SciSports, where she leads the Data Analytics team that develops analytical tools to derive actionable insights from soccer data. An avid soccer player herself, Lotte primarily works on developing machine learning models to measure the impact of soccer players’ in-game actions and decisions on the courses and outcomes of matches. Prior to SciSports, Lotte obtained a Master of Science degree in Econometrics & Management Science from Erasmus University Rotterdam and a Bachelor of Science degree in Mathematics from Utrecht University.

References:

• 'Player Chemistry: Striving for a Perfectly Balanced Soccer Team' - https://arxiv.org/pdf/2003.01712.pdf


• 'Actions Speak Louder than Goals: Valuing Player Actions in Soccer' - https://arxiv.org/pdf/1802.07127.pdf


• 'Wide Open Spaces: A statistical technique for measuring space creation in professional soccer' - http://www.sloansportsconference.com/wp-content/uploads/2018/03/1003.pdf


• Interpretable Machine Learning - https://christophm.github.io/interpretable-ml-book/


• San Francisco 49ers recently hired Harvard Biostatistics PhD Matt Ploenzke (@MPloenzke) whose thesis was on 'Interpretable Machine Learning Methods with Applications in Genomics'

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit statthinksportsanalytics.substack.com