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Soccermatics Pro
Why you should take this course!
7 reasons in 4 minutes (3:58)
Introduction
Meet David Sumpter (13:03)
Winning matches with football analytics: Hammarby (15:56)
Setting and reaching targets: Burton, Wrexham & Degerfors (12:01)
Analytics for Set Pieces: Wycombe Wanderers (3:43)
Preparing for a big match: Manchester United's FA Cup (3:49)
How we will work in this course (7:25)
Getting started with football data
Setting up your environment (20:59)
Basic plotting (23:19)
Visualising footballing actions
Visualising football
Plotting passes and shots (14:23)
Match reports (12:15)
Coding a metric for regains (9:35)
Season reports (6:59)
The importance of answering footballing questions (7:02)
Project 1: understanding a player with data
What you should do
Scouting with data
KPIs for playing positions (7:53)
The qualities and metrics approach (12:08)
The single number approach (2:47)
Combining data with observation (10:28)
The Gary Neville metric (4:44)
Creating a Streamlit App
Setting up Streamlit (8:11)
Machine learning and models
Linear regression
Modelling approaches
Expected goals
How xG models should be used
Fitting the model step by step
Statsbomb 360 data
Adding more features
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
Setting and reaching targets: Burton, Wrexham & Degerfors
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