The red carpet at the world premiere of Far From the Madding Crowd. Photo: Danny E. Martindale/Getty Images
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What would Hardy make of his Bathsheba barrelling past on the side of a bus?

Poor old Tommy-baby. His entire oeuvre, when you stop to consider it, seems like an illustration of Dostoevsky’s dictum: “The more I love humanity in general, the less I love man in particular.”

A film director friend of mine once explained to me the wherefores of successful film distribution in Britain: “It’s all down to your T-sides, Will,” he maintained. “Get your T-sides sorted or it doesn’t matter how many screens you open on, you still won’t get the bums on seats.” I had no idea at the time what a T-side was, but ever since he told me I’ve seen them everywhere. Often a T-side will glide across my field of vision when I’m least expecting it – supplanting my view of the Holloway Road, for example, with the winsome spectacle of a giant Carey Mulligan, red-cheeked and wind-tousled against a Wessex backdrop.

Yes, a T-side is the T-shaped advertising space on the flank of a double-decker bus, and industry types assure me you can’t get a maddened crowd for Far from the Madding Crowd unless your distributor can outbid all the others clamouring for these valuable sites. There seems a compelling irony here when we consider that, despite the enormous success of Hardy’s novel in his lifetime (it first appeared as a serial and then went into four separate bound editions before he died, each one extensively revised), he remained repelled by the new mass culture that emerged in the late 19th century. In John Carey’s path-breaking revisionist cultural history The Intellectuals and the Masses, he quotes extensively from the journals Hardy wrote during the 1880s, when the writer was living in the leafy ­London suburb of Upper Tooting. Haunted by the proximity of the mighty city, Hardy felt he was being watched by “a monster whose body had four million heads and eight million eyes”.

But this wasn’t only a distant dehumanising prospect: the Great Romancer was equally revolted when he encountered the multitude up close and personally. At the British Museum he was nauseated by “crowds parading and gaily traipsing around the mummies, thinking today is for ever . . . They pass with flippant comments the illuminated manuscripts – the labour of years – and stand under Rameses the Great, joking. Democratic government may be justice to man, but it will probably entail merging [with the] proletarian, and when these people are our masters it will lead to more of this contempt, and possibly be the utter ruin of art and literature!”

Poor old Tommy-baby. His entire oeuvre, when you stop to consider it, seems like an illustration of Dostoevsky’s dictum: “The more I love humanity in general, the less I love man in particular.” His novels usually pit the intelligent and – Alan Johnson, take note – aspirational individual against entrenched privilege; yet while inequality may maim a Jude or a Tess or a Gabriel, often what finishes them off is the ignorant prejudices of the yokel mob. Thus Hardy has it both ways: valorising the simple and homespun but simultaneously decrying the herd mentality of the benighted Wessex peasantry. I shudder to think how freaked out he’d be by these crowds of Bathsheba Everdenes and Gabriel Oaks gaily traipsing across towns on the sides of buses.

Still, he must have given permission for the first sale of the novel’s film rights – because there was an early silent adaptation in 1915, while he was very much alive; since then, we’ve had John Schlesinger’s 1967 take on this pastoral of necro-narcissism, a TV movie in the 1990s, and now the Danish director Thomas Vinterberg has brought to the tale the same unvarnished sensibility he applied to his incest-shocker, Festen. The first screen Bathsheba was played by Florence Turner, the so-called “Vitagraph Girl” (after the studio whose movies she starred in). New York-born, Turner pursued a successful career on both sides of the Atlantic, on stage and screen, throughout the Teens and Twenties of the 20th century. As well as acting she wrote scripts, and played a part in directing and producing her own vehicles – so, not an instance of typecasting at all.

In Hardy’s novel the stolid sheep farmer Gabriel Oak is first ensorcelled by Bathsheba Everdene when she lies back on her horse as it trots through a tunnel formed by low tree boughs. It’s an arrestingly sexual image: the beautiful young woman undulating in time with the strong rhythmic movements of the large body upon which she lies prone. Hardy’s crowd-phobia was certainly shared by many of his contemporaries, but you don’t have to accept unreservedly the thesis described in John Carey’s book in order to understand its queered provenance. Like all the great writers Hardy was good at noticing things – and in particular he was good at noticing those involuntary human gestures that reveal our true animality.

Perhaps this is what he really feared: not the prejudices and warped taboos of human society, but the overpowering desires such a culture imperfectly restrains. We all like to separate ourselves off from the mob. It’s they who graze on popcorn and slurp on slushes of ice and sugared water. Their love is bestial – the beast-with-two-backs they make is subject to a geometric progression: first two beasts, then four, then eight, then a multitude. Our love, by contrast, is as pure and soft as one of Gabriel Oak’s newborn lambs; our thoughts as elegant and symmetrical as an arabesque. Which is why it doesn’t matter how many T-sides they get: when we see a madding crowd heading in one direction, we head in the other – together with our own maddening one.

Will Self is an author and journalist. His books include Umbrella, Shark, The Book of Dave and The Butt. He writes the Madness of Crowds and Real Meals columns for the New Statesman.

This article first appeared in the 27 May 2015 issue of the New Statesman, Saying the Unsayable

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