Football Analytics

• Credit value: 15 credits at Level 7
• Convenor and tutor: Giambattista Rossi
• Assessment: a 20-25-minute group presentation (40%) and a two-hour examination (60%)

The performance data industry in football has grown dramatically in recent years to the point where all professional teams have access to some level of data and employ performance analysts to help coaches, scouts, players and executives work with the new technologies available, particularly video. However, as detailed data and analytics become integrated within club processes, it is essential that staff are equipped with the skills to not only interpret and apply this information correctly, but also to present and argue insights to decision makers.

Teams also amass large databases of their own subjective information on players but do not manage that data appropriately, and clubs still have limited knowledge of how to use data to strategically plan for the future and in budgeting. It is the analysts who have the chance to improve this dramatically.

At the other end of the market some teams have become extremely advanced and actually employ data scientists to build advanced models. While this progress is excellent, a new problem exists where many within the team do not know how to interpret the advanced outputs they are being shown.

This module aims to develop managers who can make decisions, based on provided models, regarding both player and team valuations.

Indicative module syllabus

• Statistical landscape in sport: how data is used in elite sport
• Statistical models and their application in football
• Collecting, databasing and analysing subjective data: integration of different datasets
• Using data in an applied setting
• Analytical softwares
• Data visualisation: best practice
• Presenting advanced analysis: managing conversations; negotiating with decision-makers
• Long-term, strategic planning using data: beyond ‘day-to-day’ work
• Case studies and revision

By the end of this module, you will be able to:

• understand available statistical tools and resources within elite sport
• analyse, interpret and present solutions to problems using data-led analysis
• integrate data-led analysis into relevant decisions that positively impact an elite sports organisation.