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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
Introduction to scouting
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)
Expected goals
What makes expected goals so useful (19:31)
The data underlying expected goals (4:47)
The model underlying expected goals (15:17)
Introducing more features into the model (17:16)
Fitting a model to Statsbomb 360 data step-by-step with Pegah (76:17)
Neural networks and stepwise regression (19:20)
Expected threat
Overview of position-based xT (11:43)
Sarah Rudd's Markov model
Position-based expected threat
Overview of action-based xT (12:58)
Action-based expected threat
Applications of xT (4:36)
Project 2: Expected danger model
What you should do
Building models
The art of modelling (8:32)
The Van Dijk metric (6:23)
Predicting player development (8:51)
Repeatability over seasons (5:56)
Using large language models to describe data
Fully automated reports in Earpiece (16:39)
What LLMs can and can't do (9:54)
Wordalisation (17:12)
Wordalisation Code Walkthrough (2:47)
Introduction to tracking data
Types of data (16:17)
Working with Skillcorner data (12:45)
Physical metrics
Velocity and acceleration (7:25)
High speed runs and other metrics (9:39)
Formations and tactics
Measuring formations (6:16)
Compactness and shape (4:16)
Pitch control
Introduction to pitch control (14:07)
Will Spearman's and Laurie Shaw's lectures
Pitch control code walthrough (12:10)
Combining with expected threat (5:25)
Scouting with tracking data
The Barcelona zonal model (6:41)
Measuring off-ball runs
Defensive positioning
Synchronised defending
Introduction to deep learning
Pegah Rahimian introduces deep learning (84:56)
Teach online with
Combining data with observation
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