Lotte Bransen's guest appearances

Lotte Bransen

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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Open Source Sports

Jina Anne

Meredith Broussard

Ilias Diakonikolas

Scott Clark

Celeste Kidd

Justin Ehrlich

Dennis Mortensen

Rob Walker

Jared Spool

Sanmi Koyejo

Chelsea Finn

Dario Amodei

Jianxiong Xiao

Kate Darling

Jason Holmberg

OwlTail

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 &amp; 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'

We discuss <a href="https://arxiv.org/pdf/2003.01712.pdf" rel="nofollow" target="_blank">'Player Chemistry: Striving for a Perfectly Balanced Soccer Team</a>' with Lotte Bransen. This paper builds on the <a href="https://arxiv.org/pdf/1802.07127.pdf" rel="nofollow" target="_blank">VAEP framework previously introduced Lotte and her colleagues</a>, 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 (<a href="https://twitter.com/LotteBransen" rel="nofollow" target="_blank">@LotteBransen</a>) 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 &amp; Management Science from Erasmus University Rotterdam and a Bachelor of Science degree in Mathematics from Utrecht University. References: <ul> <li>'Player Chemistry: Striving for a Perfectly Balanced Soccer Team' - <a href="https://arxiv.org/pdf/2003.01712.pdf" rel="nofollow" target="_blank">https://arxiv.org/pdf/2003.01712.pdf</a></li> <li>'Actions Speak Louder than Goals: Valuing Player Actions in Soccer' - <a href="https://arxiv.org/pdf/1802.07127.pdf" rel="nofollow" target="_blank">https://arxiv.org/pdf/1802.07127.pdf</a></li> <li>'Wide Open Spaces: A statistical technique for measuring space creation in professional soccer' - <a href="http://www.sloansportsconference.com/wp-content/uploads/2018/03/1003.pdf" rel="nofollow" target="_blank">http://www.sloansportsconference.com/wp-content/uploads/2018/03/1003.pdf</a></li> <li>Interpretable Machine Learning - <a href="https://christophm.github.io/interpretable-ml-book/" rel="nofollow" target="_blank">https://christophm.github.io/interpretable-ml-book/</a></li> <li>San Francisco 49ers recently hired Harvard Biostatistics PhD Matt Ploenzke (<a href="https://twitter.com/MPloenzke" rel="nofollow" target="_blank">@MPloenzke</a>) <a href="https://twitter.com/rafalab/status/1303694804841361408?s=20" rel="nofollow" target="_blank">whose thesis was on 'Interpretable Machine Learning Methods with Applications in Genomics'</a></li></ul>

Player Chemistry in Soccer with Lotte Bransen

Technological advancements have been changing sports for the last couple of decades: carbon fiber, wearable sensors, player tracking, clothes manufacturing and many more are helping athletes and coaches in raising the bar to almost superhuman levels. But we shouldn’t sit back and enjoy the ride. New technologies are popping up and it’s important to keep track of what is happening right now. Teams or countries that don’t do this stand a high risk of falling behind. This is why we held an evening on the current stance of where we are today and how we see sports evolving in the future. We sat down with Lotte Branson, Lead Data Scientist at SciSports. The conversation is in Dutch, but this blogpost on the same topic might be interesting if you're not a native Dutch speaker: https://www.inthepocket.com/blog/how-machine-learning-is-changing-sports

Technological advancements have been changing sports for the last couple of decades: carbon fiber, wearable sensors, player tracking, clothes manufacturing and many more are helping athletes and coaches in raising the bar to almost superhuman levels. But we shouldn’t sit back and enjoy the ride. New technologies are popping up and it’s important to keep track of what is happening right now. Teams or countries that don’t do this stand a high risk of falling behind. This is why we held an evening on the current stance of where we are today and how we see sports evolving in the future. We sat down with Lotte Branson, Lead Data Scientist at SciSports. The conversation is in Dutch, but this blogpost on the same topic might be interesting if you're not a native Dutch speaker: <a href="https://www.inthepocket.com/blog/how-machine-learning-is-changing-sports" rel="nofollow" target="_blank">https://www.inthepocket.com/blog/how-machine-learning-is-changing-sports</a>

Sports & AI with Lotte Bransen (SciSports)

Today we're joined by Lotte Bransen, a Scientific Researcher at SciSports. With a background in mathematics, econometrics, and soccer, Lotte has honed her research on analytics of the game and its players, using trained models to understand the impact of mental pressure on a player’s performance. In this episode, Lotte discusses her paper, ‘Choke or Shine? Quantifying Soccer Players' Abilities to Perform Under Mental Pressure’ and the implications of her research in the world of sports.

Measuring Performance Under Pressure Using ML with Lotte Bransen - TWIML Talk #296