How data analytics has revolutionised football

How data analytics has revolutionised football

The modern football industry is heavily reliant on statistics and analysis which are used to inform everything from managerial and coaching decisions to betting lines. Football clubs now employ a wide range of analysts who are an integral part of their success.

For years, many football experts turned their noses up at the prospect of data analytics and the use of statistics to inform strategy on and off the pitch. While it had worked in American sports like baseball, many claimed that football couldn’t be quantified and was too fluid and dynamic for statistical analysis to have any real positive impact.

How Data Analytics in Football Began

Statistics are nothing new in football and commentators have been offering up niche factoids about historical results and performances almost since the sport began. However, the ways in which data is collected and used have changed a massive amount in a fairly short period.

Data analytics started to gain a foothold in sports after the successful application of ‘sabermetrics’ in Major League Baseball. It contributed to the cash-strapped Oakland Athletics competing with much larger and more financially successful franchises and this sparked a lot of interest in the world of football too. The success of the Californian franchise in using data to target undervalued players has since been recognised in the 2011 film, Moneyball.

In 2006, sports data firm Opta Sports began the arduous process of recording as much data as possible on major football games. This included the time and location of every shot, pass, tackle and more, as well as whether the move was successful or not. Today, more than 2,000 data points are recorded by Opta in every game.

Although football was initially resistant to change, teams began to see the value of data analysis from around 2010 onwards. It has slowly become an integral part of running a football club, with teams across all levels benefitting from collecting, studying and interpreting data.

Football Data for Improving Results on the Pitch

One of the most significant uses of data analytics is in helping teams improve results on the pitch. Football, like any sport, is a results-based industry, where revenues are dictated by wins and losses. Clubs cannot survive without winning games, and success breeds further good results.

Data analytics can give coaches and players better insights into what is working and what isn’t. For example, they can look at successful duels made in certain areas of the pitch to work out which areas they should be focusing their attack on. Real-time statistical analysis is incredibly important for games, but long-term data analysis can also be used to refine training and find the right players to improve a team.

Expected Goals (xG) is one of the first new-wave metrics to become widely known in football circles, a statistic that measures the quality of a chance by calculating the likelihood that it will be scored by using information on similar shots in the past. xG, in essence, judges the overall quality of goalscoring chances, and therefore the quality of the finisher.

Liverpool have been successful in their usage of data for transfers across the last decade. Mohamed Salah, for example, was a suggestion of the data-focused recruitment team at Anfield, despite Jurgen Klopp’s initial preference for Bayer Leverkusen’s Julian Brandt.

Salah has since gone on to score more than 200 goals in all competitions for the Reds, while Andy Robertson, signed from relegated Hull City for just £8m, is another example of the club’s ability to use statistics to identify players with potential to progress to the elite level.

Liverpool recently announced the decision to bring back data-guru Michael Edwards, as the club prepare for Klopp’s departure as head coach. Edwards’ role in identifying transfer targets and extracting maximum value from sales was crucial to the early success of the Klopp era.

Brentford are another team who have gained notoriety for their data-focused approach. The Bees have consolidated themselves in the Premier League in recent campaigns, despite comparatively modest resource levels. Thomas Frank’s side finished ninth in the Premier League last season, despite possessing the lowest wage bill in the division.

How Data is Helping Clubs Increase Revenues

Football might be a sport, but professional clubs have to be run as businesses if they’re to be sustainable and achieve long-term success. This means looking for ways to maximise revenues and cut costs while still ensuring the best results. Data analysis is an excellent tool used by businesses to find a competitive advantage, and football clubs can also benefit from using it in this way.

Many clubs use data analytics to inform their commercial strategies, using it to spot ways to increase fan engagement and drive more ticket and merchandise sales. Of course, the biggest factor in how profitable a club is will be its success on the pitch. However, reducing spending and ensuring the club follows a tight budget can be just as important when thinking about the long-term survival of the team and staying ahead of rivals in the league.

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See more – FA Cup Team of the Week – Quarter-final XI

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