In Focus/ Data Analytics: Strong Impact In Football
Business in today’s world is revolving around ‘data’. What began as a basic tool to create a logbook has now metamorphosed into the biggest decision-making tool growing exponentially, particularly in the last few years.
Recent analyses by International Data Corporation (IDC) indicate that approximately 90% of the world’s data has been generated within the past two years, volume of data stored globally is doubling approximately every four years and approximately 402.74 million terabytes of data are being generated daily.
To give it a further perspective, the global volume of data in 2025 is projected to reach 181 zettabytes which is equivalent to storing in 45 trillion DVDs! Crazy? It is.
This exponential growth in data is affecting every aspect of our lives. Naturally, sports can’t escape this. Modern day football strategy and decisions are being consciously encapsulated within data. Data and associated analytics are not a new concept but it is now getting more accepted with the increasing use of technology like use of IoT devices, real-time data processing and cloud-based storage.
In simple terms data leads to a pattern which further leads to insights prompting informed decision-making. So, more data collected at granular level leads to a better understanding of patterns and insights ultimately making decision-making more robust.
In this blog, we look at the key areas in football where data analytics has been playing a defining role.
Areas of Impact
Sports and analytics have been developing a deep relationship amongst each other and kind of revolutionising the approach to sports business. In the big-ticket cricket tournament, Indian Premier League, data analytics has been the backbone of player valuations in auctions going further to develop the match strategy.
In the USA, data analytics has been instrumental in a more holistic manner, from player and team related strategies to athlete performance and injury monitoring to sponsorship, making television viewing more interesting and fan-understanding at the other end of the spectrum.
European football is somewhat caught in the middle of it. We have seen tremendous progress in utilising analytics in some areas notably:
– Player scouting: Data analysis has turned modern day player scouting upside down. Modern analysis tools developed and employed by multiple specialist agencies and organisations enable clubs and national teams to identify talented players at an early stage and assess their development potential. From video analysis to training analysis, the range of gather data points are huge, as much as 1.4 million data per game, that is 10 data points each second per player. Now, making sense of the data points and creating insights to rank or scout the best possible player from a huge selection of under-the-radar players is the essence of data analytics employed for player scouting which is becoming one of the most impacted areas.
– Training, Match Preparation & Analysis: The scope for utilising player data for preparation, understanding weaknesses and rectifying it to the extent of studying opponents’ style of play is immense and is being increasingly utilised by managers and analysts.
– Injury Prevention: Player data points gathered during training, practicing and even exercising is being used to study their physiology to judge the fitness. Coupled with predictive data analytics, they are used to gauge preventive injuries at an early stage.
Of course, there are other areas where it’s being used like understanding fan data resulting in superior fan engagement, broadcasting, stadium management etc. Multiple modes of gathering data, for example, in case of players, tools like using GPS trackers, heartrate monitors and biomechanical sensors, which is then processed in real-time and actionable insights procured helping the coaches / managers and analysts to make immediate decisions. In the case of stadium management, video analysis, ticketing analysis, fan data can be analysed through native apps and so on.
Implementation in Football Clubs
The ‘Moneyball’ approach in player scouting is getting so much traction that most clubs have a specialised data analytics department where the bigger clubs have multiple staff with specialised roles while clubs in the lower half of the table or lower tier have one or maybe two members. The point is analytics is becoming the basis of informed decision-making and it is up to the club to adapt as soon as possible.
One club that has maximised the use of data analytics is Brighton & Hove Albion, perhaps the most exciting usage giving strong returns. Tony Bloom, the owner, a mathematics expert who built his business primarily through the gambling industry before venturing into sport. Under him, Brighton is now regarded as one of the best-run clubs in the English football.
For context, Brighton’s turnover in 16/17, which was their last season in the EFL Championship, was £29.2m, which has skyrocketed to £223m in 23/24, that is almost a 680% jump in seven years. Not only is the club stable in the Premier League, but it is also consistently pushing for a European place.
The really exciting part is the use of analytics and Bloom’s closely guarded algorithm which revolutionised their player recruitment and is one of the most sought-after tool in the sporting world. Not only has Brighton generated more than £300m in player sales in recent years but Bloom’s other club where he holds minority shareholding, Union Saint-Gilloise of Belgium, have gone from second division to grabbing the title in 24/25, all in a span of three years.
There are many other examples of clubs using data analytics to better their sporting success, the moot point is, analytics is an integral part of sports business now.
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In Conclusion
Data analytics and its various uses in football is transforming the sport as we know it. It’s becoming more of a necessity. The only differentiation are the ways and methods by which data is leveraged. As the technology continues to evolve, we can expect to see even more innovative uses of analytics in football which will make artificial intelligence and predictive analytics stronger.
