MMR, Andrew Wakefield and the Independent

On middle class exceptionalism and why despite his intervention in the Independent, Andrew Wakefield is still wrong.

The Independent has been criticised for ushering Andrew Wakefield - the discredited surgeon who linked MMR to autism and bowel disease - into its coverage of the recent measles outbreak in Wales. Wakefield was struck off the medical record in 2002. His claims have been proved false in countless studies. Yet the former medical researcher says he retains his position, arguing “the Government’s concern appeared to be to protect the MMR programme over and above the protection of children”.

So why, despite Time heralding Wakefield’s Lancet report as among its “Great Science Frauds”, has the Independent given front page coverage to the former doctor?

A spokesperson for the Department of Health was forced to doggedly reiterate the facts: “Dr Andrew Wakefield’s claims are completely incorrect,” he said. “Measles is a highly infectious and harmful disease. If your child has not had two doses of MMR, whatever their age, we urge you to contact your GP surgery and make an appointment.” Ben Goldacre was a little more candid:

Richard Ashcroft, Professor of Bioethics at Queen Mary University of London, pointed to the erroneous use of the denomination "Dr":

Public Health Wales has estimated the number of unvaccinated at over 40,000. Drop-in clinics have been established across south Wales, offering free MMR immunisations - which begs the question: why would the Indie put Wakefield on the front page at such a critical time? A spokesman from Public Health England, speaking on BBC Breakfast this morning, laid the blame on "middle class" parents having believed they knew better than the state, exempting their children from nationally available MMR jabs. Around 95 per cent of the population is generally required to be vaccinated in order for immunisation to be 100 per cent effective.

Katherine Clarke, a researcher at University College London, told me: "It was misinformation in the press that led to the decline in uptake in vaccinations." The Lancet report was only a case series, the second in a number stages - later ones include clinical trials which test the propositions made in case studies and series - which may later lead to health recommendations. "So many people still do not realise there is just no evidence whatsoever to support Wakefield's claims."

A nurse draws an MMR vaccination. Photograph: Getty Images.

Philip Maughan is Assistant Editor at the New Statesman.

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