OLIVER BURSTON
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How science and statistics are taking over sport

An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others.

In the mid-1990s, statistics undergraduates at Lancaster University were asked to analyse goal-scoring in a hypothetical football match. When Mark Dixon, a researcher in the department, heard about the task, he grew curious. The analysis employed was a bit simplistic, but with a few tweaks it could become a powerful tool. Along with his fellow statistician Stuart Coles, he expanded the methods, and in doing so transformed how researchers – and gamblers – think about football.

The UK has always lagged behind the US when it comes to the mathematical analysis of sport. This is partly because of a lack of publicly available match data, and partly because of the structure of popular sports. A game such as baseball, with its one-on-one contests between pitcher and batter, can be separated into distinct events. Football is far messier, with a jumble of clashes affecting the outcome. It is also relatively low-scoring, in contrast to baseball or basketball – further reducing the number of notable events. Before Dixon and Coles came along, analysts such as Charles Reep had even concluded that “chance dominates the game”, making predictions all but impossible.

Successful prediction is about locating the right degree of abstraction. Strip away too much detail and the analysis becomes unrealistic. Include too many processes and it becomes hard to pin them down without vast amounts of data. The trick is to distil reality into key components: “As simple as possible, but no simpler,” as Einstein put it.

Dixon and Coles did this by focusing on three factors – attacking and defensive ability for each team, plus the fabled “home advantage”. With ever more datasets now available, betting syndicates and sports analytics firms are developing these ideas further, even including individual players in the analysis. This requires access to a great deal of computing power. Betting teams are hiring increasing numbers of science graduates, with statisticians putting together predictive models and computer scientists developing high-speed software.

But it’s not just betters who are turning to statistics. Many of the techniques are also making their way into sports management. Baseball led the way, with quantitative Moneyball tactics taking the Oakland Athletics to the play-offs in 2002 and 2003, but other sports are adopting scientific methods, too. Premier League football teams have gradually built up analytics departments in recent years, and all now employ statisticians. After winning the 2016 Masters, the golfer Danny Willett thanked the new analytics firm 15th Club, an offshoot of the football consultancy 21st Club.

Bringing statistics into sport has many advantages. First, we can test out common folklore. How big, say, is the “home advantage”? According to Ray Stefani, a sports researcher, it depends: rugby union teams, on average, are 25 per cent more likely to win than to lose at home. In NHL ice hockey, this advantage is only 10 per cent. Then there is the notion of “momentum”, often cited by pundits. Can a few good performances give a weaker team the boost it needs to keep winning? From baseball to football, numerous studies suggest it’s unlikely.

Statistical models can also help measure player quality. Teams typically examine past results before buying players, though it is future performances that count. What if a prospective signing had just enjoyed a few lucky games, or been propped up by talented team-mates? An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others. In many sports, scoring goals is subject to a greater degree of randomness than creating shots. When the ice hockey analyst Brian King used this information to identify the players in his local NHL squad who had profited most from sheer luck, he found that these were also the players being awarded new contracts.

Sometimes it’s not clear how a specific skill should be measured. Successful defenders – whether in British or American football – don’t always make a lot of tackles. Instead, they divert attacks by being in the right position. It is difficult to quantify this. When evaluating individual performances, it can be useful to estimate how well a team would have done without a particular player, which can produce surprising results.

The season before Gareth Bale moved from Tottenham Hotspur to Real Madrid for a record £85m in 2013, the sports consultancy Onside Analysis looked at which players were more important to the team: whose absence would cause most disruption? Although Bale was the clear star, it was actually the midfielder Moussa Dembélé who had the greatest impact on results.

As more data is made available, our ability to measure players and their overall performance will improve. Statistical models cannot capture everything. Not only would complete understanding of sport be dull – it would be impossible. Analytics groups know this and often employ experts to keep their models grounded in reality.

There will never be a magic formula that covers all aspects of human behaviour and psychology. However, for the analysts helping teams punch above their weight and the scientific betting syndicates taking on the bookmakers, this is not the aim. Rather, analytics is one more way to get an edge. In sport, as in betting, the best teams don’t get it right every time. But they know how to win more often than their opponents. 

Adam Kucharski is author of The Perfect Bet: How Science and Maths are Taking the Luck Out of Gambling (Profile Books)

This article first appeared in the 28 April 2016 issue of the New Statesman, The new fascism

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Vince Cable will need something snappier than a graduate tax to escape tuition fees

Perhaps he's placing his hopes in the “Anti Brexit People’s Liberation Front.” 

“We took power, and we got crushed,” Tim Farron said in what would turn out to be his final Autumn conference as Liberal Democrat leader, before hastening on to talk about Brexit and the need for a strong opposition.

A year and a snap election later, Vince Cable, the Lib Dem warhorse-turned-leader and the former Coalition business secretary, had plenty of cracks about Brexit.

He called for a second referendum – or what he dubbed a “first referendum on the facts” – and joked that he was “half prepared for a spell in a cell with Supreme Court judges, Gina Miller, Ken Clarke, and the governors of the BBC” for suggesting it".

Lib Dems, he suggested, were the “political adults” in the room, while Labour sat on the fence. Unlike Farron, however, he did not rule out the idea of working with Jeremy Corbyn, and urged "grown ups" in other parties to put aside their differences. “Jeremy – join us in the Anti Brexit People’s Liberation Front,” he said. The Lib Dems had been right on Iraq, and would be proved right on Brexit, he added. 

But unlike Farron, Cable revisited his party’s time in power.

“In government, we did a lot of good and we stopped a lot of bad,” he told conference. “Don’t let the Tories tell you that they lifted millions of low-earners out of income tax. We did… But we have paid a very high political price.”

Cable paid the price himself, when he lost his Twickenham seat in 2015, and saw his former Coalition colleague Nick Clegg turfed out of student-heavy Sheffield Hallam. However much the Lib Dems might wish it away, the tuition fees debate is here to stay, aided by some canny Labour manoeuvring, and no amount of opposition to Brexit will hide it.

“There is an elephant in the room,” the newly re-established MP for Twickenham said in his speech. “Debt – specifically student debt.” He defended the policy (he chose to vote for it in 2010, rather than abstain) for making sure universities were properly funded, but added: “Just because the system operates like a tax, we cannot escape the fact it isn’t seen as one.” He is reviewing options for the future, including a graduate tax. But students are unlikely to be cheering for a graduate tax when Labour is pledging to scrap tuition fees altogether.

There lies Cable’s challenge. Farron may have stepped down a week after the election declaring himself “torn” between religion and party, but if he had stayed, he would have had to face the fact that voters were happier to nibble Labour’s Brexit fudge (with lashings of free tuition fees), than choose a party on pure Remain principles alone.

“We are not a single-issue party…we’re not Ukip in reverse,” Cable said. “I see our future as a party of government.” In which case, the onus is on him to come up with something more inspiring than a graduate tax.

Julia Rampen is the digital news editor of the New Statesman (previously editor of The Staggers, The New Statesman's online rolling politics blog). She has also been deputy editor at Mirror Money Online and has worked as a financial journalist for several trade magazines.