Disability, a New History on Radio 4: We can't get enough

We’ve hear diaries of the disabled from all centuries, discarded flyers for freak shows, letters between aristocrats disfigured by smallpox and grappling with wooden limbs, and an account of Samuel Pepys visiting a lady with a beard (“It was a strange sig

Disability: a New History
Radio 4

Invariably, the shorter the programme on Radio 4, the more creative and appealing it is. Tweet of the Day, my current obsession, recording the facts and sounds behind British birdsong, is less than 90 seconds long and more memorable than anything uttered for the following 180 minutes on the Today programme. The recently broadcast A History of Noise was captivatingly brief and loopy, featuring a French anthropologist crouched in a cave summoning, with groans, painted bisons. And now a run of 15-minute programmes – rather sonorously called Disability: a New History (weekdays, 1.45pm) – comes on with a wit and variety that nobody first tuning in could have anticipated in a million pious years.

We’ve heard diaries of the disabled from all centuries, discarded flyers for freak shows, letters between aristocrats disfigured by smallpox and grappling with wooden limbs, and an account of Samuel Pepys visiting a lady with a beard (“It was a strange sight to me, I confess. And pleased me mightily”). In one episode, an essay by the 18th-century politician William Hay, who was born with curvature of the spine, recalled the self-loathing that accompanied feeling obliged to make a joke of his disability before others got in there first – as though the act of first ridiculing oneself provides firm inoculation against any pain caused by others.

It is, of course, a standard impulse, whatever the era. I was born with only half a left hip, and the subsequent operations and scarring and occasional limp can usually be hidden but when they are not (if I’m tired, or on a beach) I do feel a nagging need to make reference to it and never like myself for doing so. The scars seem to be the least of the problem. I don’t think I mind them much at all.

The presenter Peter White – ever confident and eloquent – openly laughs at some of the more appalling facts discussed. “Are you really telling me that this was supposed to be a medically based thing propounded by scientists?” You can feel his interlocutors (historian, psychologist) immediately loosen, feeling the breeze in the room provided by a presenter at the top of his game. This ten-part series could run all summer and still never be enough.

 

The presenter Peter White. Photograph: BBC

Antonia Quirke is an author and journalist. She is a presenter on The Film Programme and Pick of the Week (Radio 4) and Film 2015 and The One Show (BBC 1). She writes a column on radio for the New Statesman.

This article first appeared in the 10 June 2013 issue of the New Statesman, G0

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