Alex Marin Felices: Transforming data into info, knowledge & wisdom

Alex Marin Felices: Data as a tool for wisdom

Alex Marin Felices: I’ve been the Data Scientist at Nottingham Forest since early 2024, working mostly in recruitment. Outside of the club, I run a newsletter called The xG Football Club, where I summarise and get key insights from the newest research in football analytics.

For the purposes of this presentation, there is one particular piece of research, published in 2024, that I would like to share. In the study, the authors surveyed practitioners from federations that had qualified for the 2022 World Cup, as well as ones from a range of professional clubs around the world.

In total, there were respondents from 29 federations and 32 clubs. The idea was to explore the characteristics of the practitioners’ data analytics infrastructure, of their roles and also their value for elaborating player monitoring and positional data.

The findings were eye-opening. Only 58% of the respondents agreed that data was ready to generate decisions at their clubs or federations, and the consistency between departments regarding data was 36%.

So the problem is not the amount of data we have available, but what we do with it. As Matteo Matteotti mentioned in the previous talk, a single match with tracking data can generate three million data points.

We have match data – line-ups, who scored, red cards, etc. We have video of these matches or of training, from a tactical camera or from a broadcast feed or from a drone in training.

Then we have what has probably been the key of all football analytics up until now, which is event data. Of course, this only shows you what happens when one player has the ball though and ignores what happens in the rest of the pitch, when the players don’t have the ball.

This is why tracking data is now making an appearance and becoming more and more important, because it allows you to see what happens in every second for all players and the ball.

Then we have some physical and load data, that may be GPS, distance, speed, but also sleep quality, injury history of the players etc, and finally we have some contextual and off-the-pitch data, for example to analyse if a player is suitable for the culture of the club.

Some clubs are using AI or natural language processing to check news or social media to see if this player will be suitable for them.

We all want a data holistic approach, incorporating all of these different types of data, but there is a problem if this is not leading to actual useful decisions at clubs.

I got this image from Jan Van Haaren, defining data as a tool for wisdom. We need to gather data and transform it into information, knowledge and finally wisdom – namely insights that can be actually used.

Gabriele Gnecco: The changing landscape of data in football 

Gabriele Gnecco: Hudl’s Customer Solutions team are privileged to have tens of thousands of interactions with professional clubs across every major continent, from clubs operating with world-class budgets to those navigating tighter financial constraints; from organisations with deep technological expertise to those just beginning their data journeys.

Data Adoption

Thanks to this extensive and diverse experience, we have been able to construct a clear Data Adoption Curve within professional football. This defines four precise and distinct phases in a club’s journey towards data maturity:

• • Phase One is what we call outsourced analysis. The club uses raw data but relies entirely on external consultants or providers to perform and interpret the complex analytical task. In this situation, the internal capacity of the club is minimal and no extra staff are needed.
  • In Phase Two, the analysis is internalised, while the engineering is kept external. So the club starts building internal analytical capabilities and hiring analysts, but the challenging foundational process of data engineering, cleaning, and transforming and storing data remains outsourced.
  • In Phase Three, all data phases are internalised, from engineering to analysis but a major bottleneck persists, so different datasets are analysed in separate silos. Performance data is viewed independently of scouting ones and financial data is separate from medical reports and this prevents from the generation of cross-functional insights.
  • Phase Four is the holistic approach. This is the destination, this is what we are talking about today. Data is centralised, unified and analysed, with an overall and comprehensive view. This phase is characterised by cross-functional collaboration driven by integrated data. It requires data engineers and a massive effort to map all the data services that are implemented in the cloud data pipeline.

Growing Sophistication

Technological sophistication in professional football is increasing, driving more clubs toward a holistic data approach. This unified approach is necessary to manage the massive volume and variety of data with over 22 million data points and more than 100 hours of video, weekly.

Jon Ollington: Creating a single source of truth with data & video 

Jon Ollington: At Hudl, we have developed a solution called Insight that allows clubs to seamlessly and automatically integrate event and tracking data with video without manually mapping between different services or custom coding.

This can help clubs combine the physical and tracking data with the event data and video. This is where clubs move on from simply integrating single-source data to actually understanding playing style and tactical behaviours.

By adopting these workflows, we can enable in-depth analysis of team performance by automatically integrating event and tracking data with video. Key analytical areas coveredin the demonstration above include:

• Out of Possession: Visualising team structure (low/mid/high block) and analysing immediate actions after losing possession under pressure.
• Attacking Play: Examining dynamic elements like runs behind the lines (with customisable rules), passes that progress play, and layering in physical data (eg distance and sprints).
• Set Pieces: Comprehensive analysis of set-piece outcomes, such as goals and shots from specific areas.