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The ‘Beautiful Game’ Goes Scientific

The beautiful game, football (soccer), has been undergoing a quiet revolution in the land of its birth – England.

English clubs have always sent football talent scouts the length and breadth of the country and further afield too. Decisions on footballing matters and transfers were once based on nothing more than a hunch, a gut feel, and an ‘eye’ for talent. Admittedly there have been numerous success stories from these unsophisticated methods – I think of the late George Best being plucked from Belfast at the age of 15 by Manchester United scout Bob Bishop. Best went on to become a successful player winning several team and individual honors and was notably the European Footballer of the Year in 1968.

As for today, across England a data revolution is taking place as various top Premier League clubs are starting to realize the benefit of data and statistical analytics. You could say that football has finally embraced the premise of Moneyball (now a movie with Brad Pitt, which is set to be released in September). English football has become scientific.

Moneyball: The Art of Winning an Unfair Game, written by Michael Lewis, proved that data does matter in sports and can often be the difference between winning and losing.  Lewis’ book focuses on the Oakland Athletics baseball team of the early 2000s, who, under General Manager Billy Beane, assembled a winning team by focusing on an analytical approach to evaluating player performances. Beane concentrated on statistics like on-base percentage as an indicator of offensive success, going against the conventional thinking and trusted player performance measurements of the time.

Much like at the Oakland A’s, number crunchers at various English Premier League (EPL) clubs are now playing a vital role in footballing strategy. Player statistics that truly matter have been isolated. Presentations you’d expect to be given by a “quant” at an investment bank are becoming the norm. Nearly every minute aspect of a football match can be recorded and analysed. The ‘nerds’ are ‘over the moon’, as pundits and players like to say in England. Numbers are beginning to give clubs an edge.

There are numerous advocates of the data revolution.

Arsene Wenger, manager of Arsenal FC, an Economics graduate and keen mathematician, was an early pioneer of using data to make decisions on player transfers. Even at Monaco in the late 1980s, before his success at Arsenal, he had realized the importance of data and was collating and using factual evidence on players. Manchester City now has a performance analytics division, while Chelsea has a performance director – Mike Forde.  The role of a performance director, like Forde, is to support the coaching team by looking at all facets of performance outside of coaching and bring structure, policy and procedures to it.

In a recent Financial Times article, Forde, who studied Psychology in Beane’s hometown of San Diego and has studied American sports, illustrated the staggering amount of data at his disposal:

Weve somewhere around 32 million data points over 12,000, 13,000 games now”.

Other clubs, such as Bolton, have also adopted the more scientific approach. Former manager Sam Allardyce is an old school football man and is somebody you’d probably expect to favor the more traditional methods – like intuition –but he too is a disciple of data. The approach certainly worked for him at Bolton. Not a historically fashionable or wealthy club, Bolton enjoyed a period of unprecedented success under his management with the team never finishing outside the Premiership top ten from 2002/2003 – 2006/2007, as well as qualifying for the UEFA cup in 2005/2006 and 2007/2008. This was an extraordinary achievement for a club of its size and limited financial resources.

Allardyce’s signing of Welsh international Gary Speed, aged 34, for Bolton is a prime example of how he used data to evaluate a transfer decision. Many would have expected Speed to have been a dud at an age when most players have hung up the boots. However, Speed’s physical data compared favorably with younger, more celebrated and, more importantly, more expensive players such as Liverpool’s Steven Gerrard. Allardyce had no hesitation in signing Speed, a relative bargain, who went on to play until he was 38 at Bolton. Speed incidentally is still playing today for Sheffield United. It is little surprise too that Mike Forde, now at Chelsea, was at Bolton with Allardyce.

So what are the numbers and statistics that really matter for football success?

There is probably no holy grail and if there is, it hasn’t been found. Possession doesn’t necessarily win matches in football (unlike in, say, rugby) and finding a relationship between distances covered by midfielders and winning has not been proven either. Most of the key statistics now being used by top clubs have not been shared, which isn’t surprising. We do at least know that clubs are focusing on the distances covered by players at high speeds which they term ‘high-intensity output’.  Another approach is to measure a host of data on a player, including the number of passes, tackles and distances covered, and then monitor those data sets over a number of years. This at least reveals when a player regresses and is comparable to a risk management methodology by clubs. Another challenge for the number crunchers is comparability. Can you realistically compare a striker scoring a heap of goals at the top of the Championship with a striker plying his trade in the  basement of the Premier League?

Is data really making a difference?

It is probably too early to conclude whether or not the number crunchers in English football are holding the keys to the unlocking of sustained success. At times talent is so obvious that you don’t need data analytics to support a decision in football. I doubt whether Barcelona FC needed any statistics to support their decision to sign a young Argentine  by the name of Lionel Messi when he was just 13. You might also argue that statistical analytics has hardly been working at Arsenal recently as the North London club last lifted a trophy in 2005.

However, surely anything that gives you an edge in football – a multi-million dollar industry where the margins between success and failure are so slim – is worthy of embracing. It is for that reason that data analytics in football is here to stay.


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One Response to The ‘Beautiful Game’ Goes Scientific

  1. MuhammadSaif October 1, 2011 at 4:21 am #

    Real Madrid

    How i don’ t understand about your sentence what is the reason behind this can you clear i am rather upset about this, too much different between sport and science only can add some good technique like research about foot ball.

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