Max Bat
@maxbatdata
Student in Data Science crazy of sport ! ⚽️🏀🎾 Trail running lover 🎽 New on Twitter ! Follow my work on this blog http://medium.com/sports-data-an…
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I took advantage of the #statbombs open data to study the finishing qualities of #LionelMessi. It turns out that his 2011-2012 season with 50 goals in #LaLiga is a counter performance!!! To be discovered on the blog medium.com/sports-data-an…
The available training data can affect the quality of an xG model. Our blogpost answers 3 questions: How much data is needed to train an xG model? Does data go out of date? Is there a league-specific effect? @p_robberechts #FoT #socceranalytics dtai.cs.kuleuven.be/sports/blog/ho…
dtai.cs.kuleuven.be
How data availability affects the ability to learn good xG models
Coming from a background in machine learning and artificial intelligence, one of the things that interests us is data and in particular,…
New blog post! Analysis of the offensive strength of the Golden State Warriors during the 2017-2018 season I relied on the excellent book Basketball Data Science: With Applications in R Basketball medium.com/sports-data-an… #basketball @StephenCurry30
Nouveau post de blog ! Analyse de la force offensive des Golden State Warriors durant la saison 2017-2018 Je me suis appuyé sur l'excellent livre Basketball Data Science: With Applications in R 🏀 medium.com/sports-data-an… @NBAFRANCE
In the next notebook, we'll learn how to calibrate our models using sci-kit learn, and in our final notebook prepare tools to get a true understanding of how exactly our expected goals model works, and understanding where it might break down/be unsuitable for our uses
My second #FoT Expected Goals Deep Dive notebook is ready! In this notebook, we compare Logistic Regression and Random Forest for xG, and discuss why relying on traditional data science methods is not sufficient for xG models github.com/andrewsimplebe… @Soccermatics
github.com
GitHub - andrewsimplebet/expected_goals_deep_dive: A tutorial on using cross validation and...
A tutorial on using cross validation and calibrating predictions for expected goals models in soccer - andrewsimplebet/expected_goals_deep_dive
Better understand the race management of an ultra trail like UTMB thanks to machine learning and hierarchical clustering medium.com/@bataillema/th… @ultratrailwtour @TrailRunningMag @aranbyutmb @OmanbyUTMB
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