Moneyball, and Competing through Analytics

I've been doing a lot of traveling recently, which as usual means that I've been doing a lot of reading. I normally take a novel with me when I'm on the road but based on some recommendations, I've been working through a batch of books on business analytics. First off was "Competing on Analytics", the seminal text on using analytics to gain a competitive advantage, next was "Smart (Enough) Systems", the book by James Taylor and Neil Raden which was the inspiration for my recent Collaborate'08 presentation, then it was "The E-Myth Revisited", recommended by Mark Waite from Griffiths Waite, then "Supercrunchers", recommended by Charlie Berger (the ODM product manager), and finally this last week I've been reading "Moneyball : The Art of Winning an Unfair Game" by Michael Lewis, a book that on the surface appears to be about baseball, about as far away from business analytics as you could possibly get. So where's the interest here?

Although it's not so big outside of the North America, baseball is of course one of America's most popular sports, with big TV contracts and top players on contracts worth tens of millions of dollars each year. Baseball is unusual though in that it lends itself to statistics gathering more than most sports; unlike football (soccer) baseball is a structured game where events happen in a defined order and the individual contribution of players can be easily measured. What certain people had begun to realise though was that the statistics that players were usually measured on - stolen bases, runs batted in and so on - were not really the key factors in whether a team won or not, and in fact less glamourous measures - walks (the equivalent, I guess of 0-0 draws or packing your defense in football), on base percentage and avoiding an out were better indicators of whether a team would win. The problem was, the traditional measures of player skills tended to emphasise "glamour" players, players who once in a while turned a game or pulled off some amazing feat, whilst the less glamourous measures tended to favour the more pedestrian players, the ones that turned in a solid performance each month but were never considered stars.

What Billy Beane and the Oakland A's did was take this analysis, which was called "Sabermetrics" after "Society for American Baseball Research", and used it to pick players who weren't glamourous, weren't the established stars, were perhaps fading stars who could still do one thing - say, ensure they always got on base - and buy them for a pittance. Then, they took these players and proceeded to grind out effective but boring victories, to the point where within their regional league they had the lowest payroll figure and the highest amount of points scored and the end of the season. Going back to the analogy, it's like a premier league side selling all their star players, searching the lower leagues for players who never lost the ball or took risks and then going on to get themselves into Europe.

Where the business intelligence interest is, is around their use of analytics to spot what are effectively inefficiencies in the market, the market for baseball players. The market is pricing players on one set of statistics that actually aren't good indicators of team success, what Billy Beane and the Oakland A's did was determine what the true significant statistics were, buy players based on that and then, in effect, win games at a lower payroll cost. In effect it's arbitrage, as the players that the Oakland A's bought then often went on to be transferred to higher spending teams who didn't use Sabermetrics, whereapon the A's would then take the money, but more mis-priced players and repeat the process again. The trick though was to spot what the true signifying statistics were, and this is where techniques such as regression came in as hundreds of amateur statisticians ran the numbers and tried to establish just what player traits and actions were most likely to lead to either runs being scored, or hitters being caught out. Going back to the "Competiting on Analytics" book, it's a classic case study of an organization gaining competitive advantage through an analytical approach to their business.

Of course the real inspiration wasn't the raw numbers, they'd been available more or less for free for many years, it was the ability of Billy Beane to firstly spot the opportunity, then have the courage to act on it, and then to determine, on an ongoing basis, the correlations and the causations. Once the Oakland A's had their initial success some of the other, better funded teams copied the ideas, hired some of their staff, meaning that the A's then had to repeat the process, finding more inefficiencies and so on, to keep things going. One interesting twist to the story was that this approach gave them great results in the league, but when they got to the play-offs and the World Series, they found they couldn't reproduce their success; they put this down in the end to the fact that their analytic approach meant that on average, they'd win more games than lose, but for the one-off, sudden death games, one star player could turn the match. Also, they were clear in saying that the intended benefit of the approach wasn't to make them the best in their league, it was to deliver results at a much lower cost, which still meant that for the big, end of season games, they would almost expect to lose as they hadn't invested the required huge amounts of money on the star players that were needed.

Again going back to the Premiership and football, Bolton in Sam Allardyce's days are the obvious comparison, they hired in either unknowns or faded stars, used the ProZone statistics, got amazing value out of them but were never really going to win a cup or get into the Champions League, they didn't have the equivalent of a Ronaldo or Gerrard who could win a match for them single-handed, but for a commensurately high salary.

Anyway, I thought it was a good book, and a good companion to Competing on Analytics. It gets a bit too baseball at times (I certainly had to turn to Wikipedia once or twice to work out what a "pop fly", for example, was) but it was good nonetheless, and probably a book you've heard being discussed at some point or other if you're from the States as I understand it caused a bit of a controversy.