How Data Analytics has changed football 

How Data Analytics has changed football 

Analytics and data are driving the strategies of big corporations around the world, enabling various companies to make better and prompt decisions, tailoring new products and services to their biggest group of consumers thanks to the insights gained from data mining and analytics. Data analytics is widely used in healthcare, travel and the retail industry, just to name a few. Similar to how it has revolutionized the online shopping industry, data analytics is filtering into football as well. Just like how consumer’s purchasing patterns are being tagged and analyzed with specific marketing strategies designed to influence their decision making, a footballer’s every move on the pitch is being tagged, passes and interceptions are marked as figures, and player profiles are easily accessible to any potential suitors in the market. 

The huge player and statistical database that companies like Sports Interactive (developers of Football Manager), Opta Sports, Squawka and Prozone have created and the pool of dedicated scouts they have maintained has more or less reduced the need for football clubs to maintain a huge talent scouting network across so many different regions, given the travel, accommodation and other fees incurred to maintain this network, especially in more remote regions. With over 1,300 scouts assessing a 550,000-player database, it is little wonder that leading premier league clubs such as Chelsea is using Sports Interactive’s database. Consequently, this has also evened the playing field for many smaller clubs who subscribe to their services, who may lack the financial power to maintain a diverse scouting network, thereby ensuring that they do not fall behind in identifying promising talents suited to their playing styles.  

Image result for football manager
Credits: Football Manager/Steam

Take Burnley for instance, who signed James Tarkowski – who has since established himself as a defensive bulwark at the centre of their defense, from Championship side Brentford in 2016. He was a statistically solid and dependable centre half who is a huge aerial presence (winning 60% of his aerial duels) and capable of huge long passes when Burnley snapped him up. Tarkowski fitted the Burnley way of play, huge in the air, solid defender, capable of a long pass to the target man, and all these traits were easily filtered out by the sports databases. 

Image result for james tarkowski
Credits: Getty

Leicester City’s scouting team has also relied on certain statistical indexes to identify the most value for money talents, helping them unearth world class players like Riyad Mahrez and N’golo Kante from little known French clubs Le Harve and Caen respectively, both of whom eventually led them to an astounding Premier League title in the 15/16 season. Rob MacKenzie, formerly of Leicester’s scouting and recruitment team, acknowledged that data analysis enabled them to compare both on ball and off ball movements that their transfer targets had and how they compared to their current squad. He once revealed in an interview that they ‘had a positivity index for the use of the ball and how successful they are, and Riyad scored highly. He was always looking to do something positive with the ball and was successful, which prompted them to send their scouts over to watch him as they did not have a scouting base in France then. 

Image result for mahrez kante
Credits: Getty (Mahrez and Kante during their time at Leicester City)

Data analytics has become indispensable not only to scouting but top tier football training, management, and performance measurement as well. Every team in Major League Soccer (MLS) has hired someone as a data scientist or analyst in their team, using the figures gained to evaluate their own players and prepare for matches (analyzing how the opponent plays and their tactical shape) on top of its influence on player trade considerations. One of the earliest believers of how metrics and data can enhance performance is Sam Allardyce, whose reputation as a premier league survivor first started during his managerial spell at Bolton Wanderers, in which he defied all logic and led the team to at least an 8th placing from 2003 to 2007. He was Prozone’s earliest customer and hired a team of young sports science graduates to translate the video analysis provided by Prozone’s software into actionable plays. In return, his team of data scientists calculated that any team that ran faster and further than their opponents would win or draw 80% of their matches, while also identifying positions of maximum opportunity on the pitch. Bolton started practicing throw-ins, corners and free kicks almost obsessively to give them the offensive edge in games, on top of their defensive resoluteness. ‘Big Sam’ shaped his Bolton into such a formidable and difficult team to beat on the applied analysis of sports data. 

Skeptics have always maintained that football is too fluid a game to be carved apart by statistical analysis, but the advent of sports data in football is proving otherwise. Will football ultimately prove to be more than a numbers’ game (albeit a highly complex one at that)? Do the statistics of a footballer speak for themselves or does it really require a seasoned talent scout to pick out the unpolished gems?

Only time will tell.

Leave a comment

Send a Comment

Your email address will not be published. Required fields are marked *