Oakland Athletic’s coach and early pioneer of analytics in sports, Billy Beane, once said, ‘We've got to use every piece of data and piece of information, and hopefully that will help us be accurate with our player evaluation. For us, that's our life blood.’
It was, perhaps, Beane who first brought the use of data in sports to the public consciousness, with the film Moneyball showing his success to millions. Now, every major professional sports team employs an analytics expert - or a whole team of experts - who pour over every bit of data that players amass from sensors around the stadiums, wearable devices on players, and information taken from within the bodies of the athletes themselves such as DNA.
Analytics is now vital to decision making in sport - choosing which players to draft, trade, develop, coach and which system to play, replacing the reliance on gut instinct that coaches traditionally relied on. We’ve look at five teams who relied heavily on analytics in their decision-making, and saw tremendous, and often unprecedented, success as a result.
England rugby team’s 2003 World Cup win
Coach Sir Clive Woodward has long championed technology and data in sports, and has done so now across a variety of sports, including soccer with Southampton. However, it is the 2003 Rugby World Cup that still offers the best evidence of his approach being successful. Prior to world cup, he approached Prozone to build a technology solution that provided the same insights that football coaches and clubs were using to support their tactical preparation and strategy, becoming the first coach to adapt Prozone to rugby.
When Sir Clive started in the role, rugby had only been a professional sport for eight years, and was lagging behind, due both to lack of investment and lack of expertise.
Woodward installed cameras all over Twickenham that collected data on how England and their opponents played. Woodward would give the players a CD containing all their data and videos of their performance, which they would take two days to analyze. They would then have to present analysis of their personal performance to him and set targets from it.
Woodward has noted that the data removed preconceived notions about the way other teams played, and removed the mystique around opponents that allowed them to more easily spot their weaknesses. Woodward said, ‘Prozone gives you a birds-eye view of all the players, there is no hiding place. From a coach’s point of view it’s a brilliant system, it actually allows you to coach and I think it makes you a better coach and a better team.’
England would go on to win the 2003 Rugby World Cup, defeating Australia 20 points to 17 in a nail biting final, and analytics in rugby has become the norm, with Saracens using it heavily to win their first ever premiership title in 2009.
The reputation of professional cycling has taken a battering over the last decade, as revelations around the extent of doping in the sport coming thick and fast. For Team Sky’s Chris Froome, this resulted in him having urine thrown at him, with people apparently unable to believe he could go so fast unaided by illegal means. However, his success is really primarily down to the approach taken by Team Sky, which makes heavy use of the latest internet of things sensor and network technologies as well as data analytics to ensure optimal performance.
Under the auspice of analytics expert Robby Ketchell, they have won 3 of the last 4 Tour De Frances. In a recent interview, Ketchell told us: ‘Numbers have always been a big part of sports, not just cycling. Endurance sports in general have recently become more and more data dependent with new sensors that measure aspects of physiology and physical performance. Cycling has grown to become more of a numbers aware sport with similar sensors, social media and using humans as sensors, onboard devices, and software dedicated to the analysis of all of the data collected.’
One of the key ways they have managed to use data to revolutionized the way that data is used in sport is through looking at it in a completely new way. By employing Tim Kerrison as their Lead Sports Scientist who had no previous experience in cycling, he could look at what actually mattered in a performance, rather than what was perceived to be the best from traditional thinking.
When Billy Beane took over at Oakland almost 20 years ago, he decided to rely heavily on data analytics, using a technique known as sabermetrics, implementation of which had begun under his predecessor Sandy Alderson but which he really took to another level. The success he saw using the techniques, despite having one of the smallest budgets in the league, inspired the film Moneyball, and showed the world of elite sports what could be gained from looking at the data. They went on to set an American League Record with 20 consecutive wins, and while they didn’t win a championship themselves, the methods were adopted by teams like Boston Red Sox who have won three using the techniques. Now, almost every MLB team is heavily reliant on analytics, although Oakland is still the most efficient team in major league baseball in terms of wins per dollar spent, with just eight players on the A’s Opening Day roster earning in excess of $2 million.
McLaren Formula 1 Victory 2014
The McLaren Group has taken home the last two Formula 1 championships, and it has done so by relying heavily on data analytics. The McLaren F1 car is fitted with more than 160 sensors that send a constant stream of data to the engineering team, with decision makers analyzing the information in real time using SAP’s HANA platform to make decisions during a race, as well as leveraging insights to design cars in the future.
Stuart Birrell, CIO of McLaren – and former CIO at Gatwick Airport – describes how the racing team’s engineers are beginning to use SAP’s in-memory database HANA. ‘We can run queries like ‘show me all races in last three years where we ran a particular suspension strut at a particular setting’. How could you query that in a traditional database? It would take hours. We use MatLab at the top level to model and compare, say, fluid dynamics, wind tunnel and telemetry time-sequenced data. For that query we got a result from 13 billion data points in 100ms.’
Golden State Warriors Win The 2014/2015 NBA Championship
In 2015, the NBA’s Golden State Warriors won its first NBA state championship for 40 years. According to FiveThirtyEight’s Elo ratings, it was the best team the NBA has ever seen that didn’t have Michael Jordan in it. Central to this has been the use of data analytics, which they have used to great effect in every facet of their operations.
The Warriors’ owners include a number of Silicon Valley heavyweights, such as Kleiner Perkins Caufield & Byers’ partner Joe Lacob and Mandalay Entertainment chairman Peter Guber, which puts them in a good position when it comes to adopting new technology. The Warriors were one of the first NBA teams to invest in, among other analytic devices, SportVU cameras, which monitor player movement during a game. In training, every aspect of players’ fitness and performance is measured and analyzed.
In March 2016, they were named recipients of the ‘Best Analytics Organization’ award at the 2016 MIT Sloan Sports Analytics Conference, with General Manager Bob Myers saying of the accolade: ‘We’re extremely appreciative and proud to receive this award. Analytics has become a huge part of sports, especially over the last few years, and we certainly put an emphasis on it as an organization. Kirk Lacob, in particular, has led our charge in analytics and he should be commended for putting us in positon to be viewed as a leader in this area and recognized for our efforts.’
Assistant GM Lacob has acknowledged the role analytics has played in their success, but noted that it forms just part of their efforts rather than being the main driver. He said, ‘I wouldn’t directly credit our success to analytics, but a large portion is due to how we utilize things like analytics. We like information. We make good decisions because we analyze a lot of information. Sure, we could run our team without all of the available data. But why would we?’