‘Chance dominates the game’. This conclusionary remark to C. Reep and B. Benjamin’s 1968 study ‘Skill and Chance in Association Football’ stood, until relatively recently, as received wisdom - the phrase deemed self-evident and its veracity left unquestioned. Predictions based on statistics were a folly they said; the sport was ‘too fluid, too unpredictable’.
Although referencing football – specifically, the English variety - the sentiment was also thought applicable to sport more generally. Because eye-catching qualities like creativity, passion, and flair, are very difficult to quantify, the consensus was that judgement of sporting prowess was best left in the hands of those trained and well versed in the particular sport - in the majority of cases, former players. Certainly, the very notion that statisticians had anything to contribute in terms of team selection, acquisition, or even strategy was considered laughable and given little countenance.
Statistical Analysis, the Roots
In the UK this began to change in the 1990s, when - as documented in the New Statesmen - undergrads at Lancaster University were tasked with an in-depth analysis of the goal-scoring probability in a hypothetical football match. While rudimentary in its technique, the analysis attracted the attention of more senior statisticians, namely Stuart Coles, who then expanded on the students' findings, his work changing permanently the way researchers thought about the game.
Across the pond, a similar revolution was already underway. This one, however, was to be even more transformative, in little time radically altering the way certain sports, particularly baseball, were conceived. Owing to its easily isolated ‘one-on-one’ contests - pitcher v batter - baseball lends itself perfectly to statistical analysis. In fact, the use of sabermetrics, ‘the empirical statistical analysis of baseball’, has been employed in some capacity ever since the sport was first professionalised. Its current use, however, both its universality and sophisticated nature find their roots in Billy Beane’s tenure as General Manager (GM) of the Oakland Athletics and his now famed ‘Moneyball Effect’.
The 'Moneyball' Effect
As GM of the (relatively) cash-strapped Oakland A’s, Beane’s innovative approach to team management was borne from a situation best summed up by the proverb: ‘necessity as the mother of invention’. With no way to meet compete with the spending power of its deep-pocketed rivals, Beane adopted an analytical, ‘sabermetric’ approach when planning the assembly of his squad, bringing into his management team a number of statisticians for the purpose. Through rigorous and incredibly time-consuming analysis, the team learnt that ‘stolen bases’, ‘runs batted in’, and ‘batting averages’ – for many years the yardsticks by which a player’s ability was measured - were not necessarily the best gauges of overall effectiveness. Rather, it was not-so-glamorous ‘on-base percentage’ and ‘slugging percentage’ that were more accurate indicators of players’ potential offensive success.
Historically, scouts overlooked these traits in favour of speed and contact, meaning their market price was undervalued, and players possessing said traits could be acquired on the cheap. It was thus - ignoring his scouts’ ‘expertise’ and relying solely on sabermetrics - that Beane assembled his team. The result dismayed many, the newly assembled roster a motley crew of old, in some cases injured, often unorthodox, has-beens. Their appearance alone drew ire from the purists already angered by the cold scientific approach. How could a team of such visible misfits go onto success?
Well, initially they didn’t. The Athletics fared so poorly during the first part of the season, enduring at one stage a 5-16 streak, that many were quick to declare the method a failure. And yet, as many will know through its dramatisation in the film ‘Moneyball’, their fortunes reversed, the A’s eventually winning the American League West, breaking the long-standing record of 19 consecutive victories in the process.
Statistical Models Spread
Around the world, these developments did not go unnoticed. The sporting community watched avidly as baseball laid down the statistical blueprint for success. It was not long before comparable models were incorporated into the management systems of: football teams, both American and English; Ice Hockey; Basketball; and many others.
One of the highest profile converts was American football coach Bill Belichick. Although denouncing its use publically (to guard his methods), the New England Patriots head coach has long held ‘a Moneyball sort of attitude’. In fact, the whole management team at the Patriots is imbued with a Moneyball-esque philosophy. From the owner Robert Kraft, described by the team website as the ‘biggest believer in data and analytics’; to Belicheck’s most trusted advisor Ernie Adams, the Patriots’ football research director, who previously worked as a municipal bonds trader. Even Belichick himself holds a degree in economics. Such expertise, NBC has commented, could be seen in the team’s recruitment, which was highly suggestive of a sophisticated understanding of ‘hyperbolic discounting’ – the result being the acquisition of high-quality players at bargain prices. On the field this has led to five Super Bowl triumphs - some validation.
Professional basketball teams were also quick to jump on the bandwagon, compiling huge databases of information from games that were then used in the offseason to inform decisions regarding contracts and free-agent signings
Many a romantic will be turned off by the use of intensive, analytical thinking almost instinctively. The deployment of cold, emotionless figures and percentages to make sense of sport, something that by nature is highly dynamic, hot-blooded, and capricious, just will not sit comfortably. Such a mindset, though, actually ignores the creative latitude many of these models allow, particularly in more fluid games like English football and basketball, where a value judgement must still be made as to which parts of the game are prioritised.
But in the End...
Obviously, it will never be possible to reduce human behaviour down to a simple formula - no matter how badly the betting enthusiast may wish for it. Yet, what analysts have proven is their ability to get, through the implementation of these statistical models, teams to punch above their weight - a weight normally dictated by financial restrictions. However, as the A’s experienced, the problem then becomes that once these models are created, they can quickly be adapted, adopted and bettered, by rivals. For instance, the Boston Red Sox employed a sabermetrics model to aid recruitment just two years after the A’s. The result? They won the 2004 World Series, their first since 1918.
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