Data has played a role in football for decades, but in recent years its use has expanded rapidly. Football’s data analysts are working out how to encourage innovation within a world that remains built on tradition, ego and rivalry.
After watching his side lose 3-2 to Luton Town, Southampton manager Harry Redknapp turned to one of the club’s data analysts and said: "I’ll tell you what, next week, why don’t we get your computer to play against their computer and see who wins?" The analyst had given a pre-match briefing to Redknapp and his players using statistics from player-tracking software Prozone, demonstrating how the team should approach the game.
Redknapp’s denouncement was symptomatic of an industry that has often struggled to balance technological advancement with ingrained traditions. This was, however, all the way back in 2005, and Redknapp has never been known as one of the more forward-thinking minds in world football.
The analysts took a back seat for the rest of the season and Redknapp went back to relying solely on experience and intuition. Southampton failed to gain promotion that year. Fast-forward to 2021 and 19 of the 20 Premier League clubs are using Prozone. Data and analytics has fundamentally changed the way that football is played, managed and viewed.
This was underlined by Brentford’s recent promotion to the Premier League. The club – nicknamed the Bees - is owned by data-driven ex-banker Matthew Benham and operates on a data-led recruitment model. Brentford’s approach has stolen the headlines, but clubs up and down the football pyramid are now utilising data either through investment in their in-house analytical capabilities or by collaborating with specialist providers.
The slow infiltration of data into the beautiful game can reveal a lot to data practitioners about the importance of patience, the benefits of building a data culture, and the best methods for landing analytical messages with an audience that, in many cases, might be directly oppositional to them.
A brief background to football analytics
Data is applied to football in three key areas: performance analysis, recruitment and strategy. In performance analysis, data is used to support pre- and post-match analysis of key player traits, set-piece trends, chance creation, and team shape during various phases of play. Post-match this data can be used to evaluate how the team and individual players performed in relation to pre-determined KPIs as part of the game plan and football strategy.
In terms of recruitment, data is increasingly being used to benchmark and analyse players across different markets against current squad players and potential transfer targets. This is used in conjunction with traditional live scouting methods and video. Recruitment is traditionally geared towards simply improving the squad, but consideration is increasingly being given to long-term financial gain. This approach has been key to Brentford’s success.
The Bees use mathematical and statistical modelling to identify undervalued players to buy, develop and sell on for large profits. The club profited around £67m from the sale of Neal Maupay, Said Benrahma and Ollie Watkins alone. From a strategic perspective, data can be used to track and identify the characteristics of successful clubs. Data can also support pathway management and succession planning. Brentford’s data-driven approach to succession sees the club sell on its better players while remaining competitive. They won their first ever Premier League game 2-0. Sorry, Arsenal fans.
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