In the Critics this week

Jonathan Lethem on Philip Roth and Steven Poole on David Hendy's latest book.

In the Critics section of this week’s New Statesman, American novelist Jonathan Lethem pays tribute to Philip Roth, who turns 80 this month. Reading Roth as a young man, Lethem writes, illuminated “something aggravated and torrential in my voice” – something specifically Jewish. “As Roth points out, the books aren’t Jewish because they have Jews in them. The books are Jewish in how they won’t shut up or cease contradicting themselves, they’re Jewish in the way they’re sprung both from harangue and from defence against harangue, they’re Jewishly ruminative and provocative.”

In Books, Steven Poole reviews David Hendy’s Noise: a Human History of Sound and Listening. “Hendy’s emphasis is on championing noise as a vehicle of sociality,” writes Poole. Hendy devotes a chapter to the noise of stadium crowds but, Poole notes, “does not mention the most notorious instrument of sporting mob dictatorship. I mean the vuvuzela, the plastic horn whose aggregated cacophonous buzz-farting ruined the auditory atmosphere of the 2010 World Cup”.

Also in Books: Alwyn W Turner reviews Mod: a Very British Style by Richard Weight (“Born in the affluence of Harold Macmillan’s Britain, mod was a cross-class coalition of youth, bringing together the art school and the assembly line …”); Lucy Wadham reviews Marcela Iacub’s novelised “memoir” of her affair with Dominique Strauss-Kahn (“There are moments when I feel that as long as I live … France will remain forever a mystery to me. Reading Marcela Iacub’s books Belle et bête … was one such moment”); Sarah Churchwell reviews O My America! Second Acts in a New World by Sara Wheeler (“Wheeler wants to claim more significance for these women than perhaps they merit”).

Elsewhere in the Critics: Ryan Gilbey reviews Danny Boyle’s Trance and Ken Loach’s The Spirit of ’45 (“That The Spirit of ’45 survives its simplifications is due to the sincerity and urgency of Loach’s argument. And, regrettably, to its pertinence”); Antonia Quirke listens to the first episode of David Hendy’s 30-part history of noise on Radio 4 (“this sounds like the most sub-avant-garde and brilliant new programme on BBC radio”); Rachel Cooke is disappointed by the BBC’s adaptation of The Lady Vanishes, though she concedes that the performances are “universally lovely”; Ollie Brock visits an exhibition of the archive of the writer Roberto Bolano in Barcelona (“For true Bolanistas … the most interesting items will be glimpses of … the ‘possible books’ to come, the unpublished manuscripts …”); Kate Mossman reviews What About Now, the new album by Bon Jovi (“Hair metal … has had a bit of a reassessment in the past few years …”.)

PLUS: “King Vulture”, a poem by Joe Dunthorne, and Will Self’s Madness of Crowds.

 

Philip Roth photographed by Eric Thayer. (Photo: © Eric Thayer)
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