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Soccermatics Pro
Why you should take this course!
7 reasons in 4 minutes (3:58)
Getting started with football data
Meet David Sumpter
Winning matches with football analytics
What you will learn
Setting up your environment
Visualising footballing actions
The importance of answering footballing questions
Plotting passes and shots
Passing networks and heat maps
Match and season reports
Principles behind visualisation
Communicating with coaching staff
Project 1: understanding a player with data
What you should do
Scouting with data
The "Gary Neville" metric
KPIs for playing positions
The qualities and metrics approach
The single number approach
Combining data with observation
Creating a Streamlit App
Setting up Streamlit
Making interactive plots
Making your app look good
Machine learning and models
Linear regression
Modelling approaches
Expected goals
How xG models should be used
Fitting the model step by step
Adding more features
Statsbomb 360 data
Project 2: Expected pass value
What you should do
Expected threat
Sarah Rudd's Markov model
Position-based expected threat
Action-based expected threat
Example: expected duels
The Van Dijk metric
Other machine learning models
Transfer predictions
Predicting player development
Transfer outcomes
Predicting transfer values
Simulating matches
Poisson distribution
Dixon-Coles model
Simulating a season
Introduction to tracking data
Types of data
Working with Skillcorner data
Working with Respovision data
Physical metrics
Speed and acceleration
High speed runs and other metrics
Formations and tactics
Measuring formations
Compactness and shape
Pitch control
Introduction to pitch control
Player feedback
The Barcelona zonal model
Expected possession value
Scouting with tracking data
Measuring off-ball runs
Defensive positioning
Synchronised defending
Introduction to deep learning
Pegah Rahimian introduces deep learning (84:56)
Teach online with
The "Gary Neville" metric
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