Larsson’s current trajectory could be likened to one of football’s most famous transformations: Cristiano Ronaldo.

Ola Lidmark Eriksson, the founder of PlaymakerAI, stands as one of the most influential pioneers in football data analytics. In this interview he shares insights on how clubs can maximise their use of football data for scouting and analysis.

The role of data analytics in scouting has become impossible to ignore. With every season, teams are turning to advanced data to make smarter decisions, whether it’s finding a hidden gem in a lesser-known league or optimizing their squad for long-term success. Some even argue that it’s only a matter of time before data completely replaces traditional scouting methods.

The player is awarded a rating for each game based on how good the player's performance was compared to other players in the same position, with data from a magnitude of historical games.

If you would like to compare players level inbetween leagues, Universal Rank is the way to go.

In football analytics, Expected Points (xP) has emerged as a key performance indicator (KPI), reflecting the likelihood of a team winning a match based on the quality and quantity of chances created. This metric has earned attention within our team and across the broader analytical community. But how is xP calculated, and what makes it so insightful?

n today’s fast-paced, content-heavy world, capturing and holding the attention of football fans is no easy feat. While traditional match reports and score updates still have their place, modern fans crave more than just raw data—they want context, analysis, and, most importantly, visual storytelling.

Challenging the traditional 90-min game metric.