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October 24, 2016

Football’s next great superstar will be found with Big Data

How can football managers make sure their star signings are going to work out?

By James Nunns

Football is the world’s global sport – with 270 million people active in the sport, meaning 4% of the world’s population is directly involved in football. Part of this activity is in the scouting, development and transfer of players. Transfers are one of the most lucrative markets. On a professional level, the movement of world-class talent to Europe’s biggest clubs have seen transfer fees rocket.

In the UK alone, Premier League clubs spent over £1 billion in the summer 2016 transfer window. This rise in spending is linked to the new TV rights; people still want to see their favorite players, and there are more competing TV networks trying to take

Vinay Joosery, CEO, Severalnines.

Vinay Joosery, CEO, Severalnines.

the lead in this £5.136bn market. These record breaking transfer contracts are signed by Premier League clubs who now have more money to spend.

The issue is, like in many industries, to make sure return on investment is delivered. So how can football managers make sure their star signings are going to work out?

In the past we used to watch football to see if our favorite players or teams won in the local park. In modern sport, one football match will be covered by over 100 cameras, this means each tackle, pass, shot, corner, throw-in is a data point for football coaches and scouts. Each data point leads to a conglomeration of stats on every individual player. With the game now becoming more interactive, fans and professionals alike look up stats, figures and game ratings of the next opponent to see where their weaknesses lie, thus changing the tactics and strategy of the game, rather than the skills of the players.

Scouting for raw talent has become digital as well. It has moved from word of mouth to constant review and measurement, for example British Eventing (BE) are measuring horse and rider at every event from local shows to international 3*’s, Rugby clubs such as Bath Rugby are recording all games and analyzing data from training sessions, looking at increased academy intake. Baseball fans in the US may have heard about Billy Beane’s unconventional mathematical methods to assemble a competitive team for Oakland Athletics back in 2002. That was an early example of leveraging data to make recruitment decisions, it was later dramatised under the name ‘Moneyball’.

In football, the Wyscout platform arms footballing superpowers such as Barcelona, Real Madrid and Arsenal with performance statistics, adding 1000 plus new matches every week, generating and tending over 250,000 player profiles. The whole outlook of scouting has changed; instead of having middle aged men driving to all parts of Europe in an affordable car, today all information can be delivered on a mobile phone or iPad. Scouts are now able to follow player progression and development in real – time.

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As you can imagine, to handle this constant creation of new stats collected from every game requires a solid data platform to protect and store this vital information. Whilst at the same time there is an expectation that online platforms should be scalable, secure and always be available – so it can be accessed by scouts, teams and players. In such a data-intensive business, where new data constantly needs to be compiled, categorized, analyzed and made searchable in useful ways, legacy IT approaches may be a hindrance. A modern IT architecture should be built from a robust distributed database solution upwards.

Technology used by sports teams require data platforms to manage large scale data and churn it out in easily attainable bites for football managers. Poor management of data could easily result in football clubs missing out signing the next Cristiano Ronaldo for a small figure or a defeat to their most bitterest of rivals.

The sports industry is changing largely in part to the availability of this data. With greater pressure on matches, training and developing talent, scouts are now able to define the superstars earlier. The effective application of data will sort winners from the losers in sport, with an objectivity that was not seen previously.

The first stage of this is to make sure data is always available, which can be used for scouting and analysis. Sport companies will see an increase in demand for stats, generating a requirement for data organisation platforms, to allow such large quantities of data to be consumed. Sport, like other industries such as retail and financial services, are facing disruption through technology. This does not signify the end of the traditional scouting network, but it does question where scouts should fit in the relationship between data and the future success of a football club, on and off the pitch.

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