Edd Webster, data scientist with LEGO, shows you how to access and manipulate datasets for football and player performance.

Jamie Dos Anjos shows you how to use Python to analyse a Premier League match. Using your JSON file of the match, you will go through the cleaning-summarising-visualising process.

In part one of our Tableau Masterclass, Tom Goodall gives an overview of the software's capabilities.

In part two, Tom discusses how to create your own metrics and a custom post-match dashboard.

Jan Van Haaren, Data Scientist at Belgium's FC Brugge, explains how to evaluate and recruit players using open-source tools.

Sunderland Data Scientist Akhil Shah explains the importance of data engineering - and how football clubs can excel at it.

Head of Quantitative Analysis Mikkel Keldmann charts Brondby's data science rise from having one intern with excel to fully-automated analysis.

Jonny Whitmore, Head of the Data Analyst Team at Stats Perform, explains how to quantify player decision-making with predictive data.

Southampton's Lead Data Scientist Alex Kleyn explains how the club developed a team philosophy, created a data-driven performance model and then aligned that throughout the club.

Devin Pleuler, Director of Analytics at Toronto FC, explores how data science can be used to compare players.

Arsenal data scientist Arjav Trivedi and Professor Geir Jordet from the Norwegian School of Sports Sciences outline their two-year research project on elite scanning, which blended research and analytics.