- Moneyball was a game-changer in raising general awareness of the possibilities for data analytics in elite sport.
- Always remember that Moneyball is only “based on a true story” and does not provide an authentic representation of how data analytics developed at the Oakland A’s.
- The conflict between scouting and analytics is exaggerated for dramatic effect.
- The real lesson of Moneyball is the value of an evidence-based approach. This goes beyond the immediate context of player recruitment in pro baseball to embrace all coaching decisions in all sports.
The publication of Moneyball in the Fall of 2003 proved to be a real game-changer both for sports analytics and myself personally. The book, and subsequent Hollywood film with an A-List cast, has probably done more than anything else to raise general awareness in elite sport of the potential competitive advantages to be gained from data analytics.
I was visiting the University of Michigan to give some presentations on what business could learn from elite sport in September 2003, just after Moneyball was first published. At that point I was making good progress in the analysis of player performance data in football (soccer) and had constructed what I later called a structural hierarchical model of invasion team sports. As I was being driven to Detroit Airport at the end of my visit, Richard Wolfe, a sport management prof, told me that I had to read Moneyball, saying “it’s you but baseball”. I picked up the book at the airport around 6pm that Friday night and had completed my first read by 6am the following morning. Here was someone actually using data analytics in elite sport to gain a competitive advantage. And there’s nothing like success to persuade others to adopt an innovation. I now had real evidence of what analytics could do; I just needed access to coaches to spread the word – easier said than done. I lost count in the coming months of the number of conversations I started with “Have you read Moneyball?” But people started to take notice and the invitations to meet directors and coaches began to follow.
The first coaching staff to invite me into the inner sanctum was at Bolton Wanderers managed by Sam Allardyce, now the England manager. I made a presentation on Moneyball and the implications for football in early October 2004 at their quarterly coaches’ away day organised by Mike Forde, the Performance Director who subsequently became Chelsea’s Performance Director. Big Sam remained pretty quiet during the presentation, restricting himself to points of information and summarising the discussions, but never revealing his opinion on what I was saying. It was Sam’s assistant, Phil Brown, currently manager at Southend United, who was the most vocal and concerned that I seemed to be advocating the use of algorithms for team selection (which I wasn’t). Bolton followed up by getting me to do some analysis of the FA Premier League including identifying the critical success factors in away performances. I also outlined an e-screening procedure for identifying prospective player acquisitions to be prioritised in the scouting process. Although it was something of an achievement to have a Premiership team ask for an analytics input in 2005, the frustration was that I was kept at arm’s length from the coaching staff and only received limited feedback on my reports. Being told that your report had provoked an “interesting discussion by the coaches” was satisfying but nothing more. What I really needed to know was what precisely had interested the coaches and how could I expand and improve the analysis to deal with any limitations they saw in it. It is an important lesson – data analytics only really works when there is full engagement between the coaches and the data analysts. My subsequent experience at Saracens showed how much I could improve the analysis by having direct contact with the coaches and being included in their discussions. As one senior member of the coaching staff at Saracens put it, I effectively became an “auxiliary member of the coaching staff” in the same way as the performance analysts and sports scientists.
Of course the biggest impact of Moneyball for me personally was to eventually connect with Billy Beane and to work with him in exploring the potential for analytics in football – the Oakland A’s own the MLS San Jose Earthquakes franchise. Seeing Billy and his staff at work at the A’s was a great education and allowed me to fully appreciate the “true story”. The book and the film are after all only “based on a true story” and make no claim, particularly the film, to be an authentic representation of the development of data analytics at the A’s.
Having seen close-up how the A’s actually operate, I’ve been better placed to respond to the criticisms of Moneyball. For example, a head of scouting at a leading European football club recently put to me that “perhaps Moneyball had become a bit of an albatross”. This head of scouting is a former player and is a very progressive individual, open to innovation to improve how things are done. But when we met he was initially very wary of adopting a more analytical approach to scouting since he thought that this would mean a reduced role for the scouts. He was won over when I pointed out that an evidence-based approach would actually enhance the role of scouts since their scouting reports would become key data that would be used in a much more meaningful way rather than just gathering dust as I suspect happens to most scouting reports.
The Oakland A’s managed by Billy Beane operate in a fundamentally different way to the Hollywood A’s managed by Brad Pitt. There is an over-emphasis in the film for dramatic effect on the conflict between the traditional scouting approach and the analytical approach. But in reality what differentiated the A’s under Billy Beane was the commitment to an evidence-based approach and a preparedness to question conventional wisdom rather than relying on gut instinct. It was the questioning of conventional wisdom that attracted Michael Lewis to the story in the first place. He started his professional life as a financial trader, trying to make profits on the financial markets by exploiting market inefficiencies caused through the over-reliance by other traders on conventional wisdom that had become outdated. Lewis applied the same lens to the MLB players’ labour market and saw Billy Beane as a kindred spirit, taking advantage of the over-reliance on traditional scouting methods. In particular, Billy Beane had recognised that the market didn’t factor into hitter salaries the ability to draw walks. Hitter salaries were mainly driven by batting and slugging averages. As economists say, walks were a “free lunch” because conventional wisdom saw them more as a pitcher error than due to the hitter’s skill in selecting when to swing and when not. Two economists, Hakes and Sauer (Journal of Economic Perspectives, 2006), have shown that on-base percentage (OBP) which includes walks had no significant effect on hitter salaries in the five seasons prior to the publication of Moneyball but in 2004 OBP was the single most significant predictor of hitter salaries. Conventional wisdom had changed because of he publication of Moneyball and, just as economic theory predicted, the ensuing market correction meant that particular free lunch quickly disappeared.
What is also forgotten is that, as in so many success stories, success was a long time coming. The evidence-based culture at the A’s was not created by Billy Beane (the film is very misleading in this respect) although he has played a leading role in the use of analytics by the A’s. But the possibility of gaining a competitive advantage from using sabermetrics was first recognised by Sandy Alderson, Billy’s predecessor as GM, and a long-time admirer of the work of Bill James. It was Sandy Alderson who employed the consultant, Eric Walker, to develop some “Bill James-type stuff that would be proprietary to the A’s”. Alderson passed on Walker’s report to Billy. The rest, as they say, is history.
It is the value of an evidence-based approach to all coaching decisions that is the real lesson of Moneyball. It is a lesson that goes beyond the immediate context of Moneyball – player recruitment in pro baseball – and is transferable to all sports. Yes the principal applications of data analytics remain in the area of player recruitment but as my experiences in football, rugby union and other sports have shown, all coaching decisions can potentially be supported to a greater or lesser extent by “knowing the numbers”, systematically analysing the available evidence both quantitative measures and qualitative assessments, and always preferring analysis over anecdote when justifying a course of action. That’s the real lesson of Moneyball.
7th September 2016