Sport is big business. In 2010 alone, the industry generated £20.3 billion for the UK’s economy, and also accounted for 2.3% of the country’s overall employment. It’s a similar story in North America. PwC estimate that sport revenue will reach $67.7 billion by 2017 in the region, spurred on by the opportunities created by smartphone applications and streaming services.
The world’s biggest sports teams are run like businesses. Manchester United’s failure to qualify for the UEFA Champions League was an on-the-pitch disaster, but the off-the-pitch implications threatened to have a much bigger impact. If the club was no longer considered part of the elite, could its overseas support be relied on? Could they attract the players necessary to bring them back? That season could have been the catalyst for years of financial hardship.
As this proves, there’s simply more on the line now than there used to be. And for that reason, coaches are trying to make sport more of an exact science. Data-driven coaching setups, therefore, are commonplace in professional sports clubs, and in individual sports, like tennis. Forbes contributor, Ryan Sommers, for example, recently discussed how women’s tennis is set to become the next sport to be ‘revolutionized’ by data.
Michael Lewis’s 2003 book - Moneyball - is often cited as the starting point. The story chronicled former Oakland Athletics’ GM and baseball player, Billy Beane, and his use of sabermetrics. The theory he came up with was a simple one; the team with a higher on-base average would be more likely to score runs, and therefore the match. He drafted players who fit this mould, ignoring any preconceived ideas about what made a championship winning side. His ideas were revolutionary not only in baseball, but throughout the entire sports industry.
Things have moved on considerably since Moneyball. The FIFA World Cup - arguably the world’s most prestigious sporting tournament - was affected by analytics, with certain media organizations calling it Germany’s ‘12th man’ enroute to winning the competition. The German’s teamed up with SAP AG to create a custom match engine that collected and analyzed player and team performance. The team cut its average possession time down from 3.3 seconds to 1.1 seconds, a tactic which was put to good use against Brazil. After Germany’s victory, Wall Street Journal’s Jonathan Clegg said: ‘Despite possessing the ball for 52% of Tuesday’s game, Brazil created barely a handful of chances’ and that ‘Germany passed the ball at full speed to create holes in the defence and clinically took advantage.’
Sites such as FiveThirtyEight.com have grown considerably since their inception and are indicative of data’s ability to capture fan’s imagination. In tennis, IBM’s SlamTracker is also popular, and ranks players depending on Twitter sentiment. Analytics has also given rise to ‘professional’ sports gamblers, like Bob Voulgaris, who won millions betting on the NBA.
Despite having its detractors, the industry still has a scope to improve, and has enough financial backing to do just that. According to Forbes, the next step could be: ‘predicting how a player’s mental make-up will adjust to the rigors of professional sports and how the emotional aspect of the responsibility correlates to on-the-field performance.’
A player’s mental strength when close to victory, or when in need of a fightback, is crucial. In individual sports it’s often the difference between the top ranked players and those just around them. If sports analytics could tap into that, it would make coaching even more of an exact science.