on traditional beliefs. Baseball, too, was until quite recently an old game stuffed with old lore. Since time immemorial, players had stolen bases, hit sacrifice bunts and been judged on their batting averages. Everyone in baseball just knew that all this was right.
But that was before Bill James came along. James was from the rural state of Kansas in the middle of the US. He hadn’t done much in life beyond keeping the stats in the local children’s baseball ‘Little League’ and watching the furnaces in a pork-and-beans factory. However, in his spare time he had begun to study baseball statistics with a fresh eye and discovered that ‘a great portion of the sport’s traditional knowledge is ridiculous hokum’. James wrote that he wanted to approach the subject of baseball ‘with the same kind of intellectual rigor and discipline that is routinely applied, by scientists great and poor, to trying to unravel the mysteries of the universe, of society, of the human mind, or of the price of burlap in Des Moines’.
James told us that baseball set the trend for the global data revolution, because the game’s record-keepers had begun gathering stats in the nineteenth century – before almost any other human activity. James explained: ‘So when the computer revolution started 100 years later, we were ahead of the game. We had 100 years of really interesting data to play around with. So the analytical revolution hit in baseball before places where sensibly you would think it would hit.’
In self-published mimeographs masquerading as books, the first of which sold seventy-five copies, James began demolishing the game’s myths. He found, for instance, that an extremely telling statistic in batting was the rarely mentioned ‘on-base percentage’ – how often a player manages to get on base. James and his followers (statisticians of baseball who came to be known as sabermetricians) showed that time-honoured strategies like sacrifice bunts and base stealing didn’t make any sense.
His annual Baseball Abstracts turned into real books; eventually they reached the best-seller lists. One year the cover picture showed an ape, posed as Rodin’s Thinker, studying a baseball. As James wrote in one Abstract: ‘This is outside baseball. This is a book about what baseball looks like if you step back from it and study it intensely and minutely, but from a distance.’
Some Jamesians started to penetrate professional baseball. One of them, Billy Beane, general manager of the little Oakland A’s, is the hero of Michael Lewis’s earth-moving book Moneyball and the film starring Brad Pitt. In recent years Beane, like so many Americans, has become a football nut. He has spent a lot of time thinking about how his insights into baseball might apply to football, and in 2015 he made his first official foray into the game, as adviser to AZ Alkmaar in the Netherlands. In December 2017 he was one of a consortium of foreign investors that took over Barnsley. (We’ll say more later about Beane’s gaming of baseball’s transfer market and its lessons for football.)
For several seasons Beane’s Oakland A’s did so well using Jamesian ideas that eventually even people inside baseball began to get curious about James. In 2002 the Boston Red Sox appointed him ‘senior baseball operations adviser’. That same year the Red Sox hired one of his followers, the twenty-eight-year-old Theo Epstein, as the youngest general manager in the history of the major leagues. (Beane had said yes and then no to the job.) The ‘cursed’ club quickly won two World Series. Today large statistical departments are the norm at American baseball clubs. Now football has embarked on its own Jamesian revolution.
A NUMBERS GAME
It’s strange that football always used to be so averse to studying data, because one thing that attracts many fans to the game is precisely a love of numbers.
The man to ask about that is Alex Bellos. He wrote the magnificent Futebol: The Brazilian Way of Life, but has also written several books about maths. ‘Numbers are incredibly satisfying,’ Bellos tells us. ‘The world has no order, and math is a way of seeing it in an order. League tables have an order. And the calculations you need to do for them are so simple: it’s nothing more than your three-times table.’
Though most fans would probably deny it, a love of football is often intertwined with a love of numbers. There are the match results, the famous dates, and the special joy of sitting in a coffee shop with your phone on a Sunday morning ‘reading’ the league table. Fantasy football leagues are, at bottom, numbers games.
In this book we want to introduce new numbers and new ideas to football: numbers on suicides, on wage spending, on countries’ populations, on passes and sprints, on anything that helps to reveal new truths about the game. Though Stefan is a sports economist, this is not a book about money. The point of football clubs is not to turn a profit (which is fortunate, as few of them do), nor do we get particularly excited about any profits they happen to make. Rather, we want to use an economist’s skills (plus a little geography, psychology and sociology) to understand the game on the field, and the fans off it.
Some people may not want their emotional relationship with football sullied by our rational calculations. On the other hand, the next time their team loses a penalty shoot-out at the World Cup these same people will probably be throwing their beer bottles at the TV, when instead they could be tempering their disappointment with some reflections on the nature of binomial probability theory.
We think it’s a good time to be rewriting this book. The amount of information available is expanding exponentially. In recent years the world has entered the era of ‘big data’. The phrase describes the unprecedented mountain of information that is now collected every day. This information comes mostly from the internet (from innumerable search terms, Facebook pages and emails), and from sensors that are attached to ever more physical objects – among them, footballers during training sessions. We have much more data to help us understand events than human beings could gather using only their eyes and ears. Moreover, all this data can be stored and faithfully reproduced, without the annoying tendency that humans have to misremember or just plain forget. Computing can identify patterns in datasets that would not be visible to a person ‘reading’ the data. We believe the data revolution enhances the capacity of humans to make good decisions. Note that we say enhance, not replace. Cyborgs replacing humans is, for now, still science fiction. But humans can make better decisions if aided by data analysis.
That’s true in football too. For the first time in the game’s history, there are a lot of numbers to mine. Traditionally the only data that existed in the game were goals and league tables. (Newspapers published attendance figures, but these were unreliable.) In 1979, after Steve Daley was transferred for a then record £1.43 million, from Wolves to Manchester City (where he flopped), the Treasury considered a tax on football transfers. The problem was that it couldn’t find any reliable financial data on the topic. In the end a young civil servant had to page through the Rothmans Football Yearbook to work out more or less how much clubs had spent on transfers the previous season.
Now the game is drowning in information. Data companies such as Opta can collect millions of observations (facts) about a single game. Clubs, which used to rely on gut alone, now use the new stats to analyse games and players. Every day, data analysts collect ever more information about every player’s every move on the field, the training ground, and even in bed – they know how well he slept last night.
Academics are pitching in as well. At the end of the 1980s, when Stefan went into sports economics, only about twenty or thirty academic articles on sports had ever been published. Now countless academics work on football. Many of the new truths they have found have not yet reached most fans. Much of what we argue in the book – for instance, that a club’s wage bill is an excellent predictor of its league position – is taken from Stefan’s academic work. Other insights come from his colleagues’ work. Generally speaking, we are more confident of what we assert when it is backed up by research that we believe is credible: meaning that the methods are clear, the data is adequate, and the results carefully explained and preferably peer-reviewed. You could still disagree with the work,
