6.1 Plus/minus models

In this lecture I introduce different ways of scouting player actions using data.

References:

6.2 Percentiles and player radars

Lotte Bransen gives an introduction to a series of lectures on valuing actions in football using statistical models and machine learning.

All the code for this tutorial series is available here: https://github.com/SciSports-Labs/fot...

Jan Van Haaren talks us through the pipeline for evaluating players using an action value based approach.

Lotte Bransen talks us through the the process of generating features for evaluating actions.

1, Investigate data in SPADL represenation.

2, Construct features to represent actions.

3, Construct features to represent game states.

Jan Van Haaren talks us through the process of training various machine learning models on action data.

Split the dataset into a training set and a test set.
Construct the baseline classifiers by using conservative hyperparameters for the learning algorithm.
Optimize the classifiers by tuning the hyperparameters for the learning algorithm.
Construct the final classifiers using the optimal hyperparameters for the learning algorithm.

6.9 Valuing actions: analysing models and results

Lotte concludes the series, looking at how to interpret the results.

6.10 Discussion with Lotte and Jan.

In this video Lotte gives an overview of the approach taken above and we discuss how these rankings work.

6.11 Player rankings

This video explains another, alternative way of ranking players, also based on machine learning.