Artist anonymous

Nick Cave, soundtrack-maker

The forthcoming film adaptation of Cormac McCarthy's post-apocalyptic novel The Road is the third proper movie for which Nick Cave has composed an entire soundtrack (working with his fellow Bad Seed/Grinderman, Warren Ellis. (As well as The Proposition (2005) and The Assassination of Jesse James by the Coward Robert Ford (2007), Cave and Ellis have also scored two relatively obscure documentaries, The English Surgeon and The Girls of Phnom Penh , released in 2007 and 2009 respectively. A selection of their soundtrack work, White Lunar, was released on Mute in September of this year. As Pitchfork put it in their review, "it's good to have it all in one place."

That record (and, indeed, Cave's excellent second novel,published earlier this year) offers yet more evidence for something the New Statesman's film critic, Ryan Gilbey, was arguing back in March of 2006: "The diversity of this singer-songwriter-actor-novelist-poet is almost unprecedented in the music industry. Dylan's memoirs were sparkling, Captain Beefheart can paint and Tom Waits is a wonderfully minimalist actor. But few performers spread themselves across so many media without spreading themselves thin. Cave is different."

Cave's work on The Road represents, I think, more than just another addition to an ever-expanding body of work: it amounts to a real breakthrough. Having seen the film last week, I can testify that for possibly the first time ever, Cave has succeeded in making his contribution to a project almost entirely anonymous. There is no unnecessarily-distracting cameo here (see the saloon singer in The Assassination of Jesse James by the Coward Robert Ford); no barely-disguised manifestation of Cave himself (see Bunny Munro in The Death of Bunny Munro); no multi-disciplinary contribution (Cave didn't just soundtrack The Proposition, he wrote it). Just a simple, relatively sparse score, that didn't receive a single mention in the 26 pages of production notes I was handed.

And said soundtrack is, in fact, itself relatively anonymous. Melting in and out of scenes, it is only explicitly present when accompanying monologues (lifted, I should add, directly from McCarthy's text). And even then, it is Ellis's violin, and not Cave's piano, that takes centre-stage (the opposite was the case with Jesse James) -- indeed, Cave's contribution to the film is limited to an array of elegantly arranged arpeggios. As Geoffrey MacNab rather crudely put it in his Independent review of The Road, "the music . . . is likewise understated. We don't hear Cave wailing out Murder Ballads."

This all sounds like an extremely backhanded compliment. It's not. In many ways, Cave's slightly megalomaniacal approach to creativity represented the only remaining ground for criticising his work (Bad Seeds purists have been known to bemoan the absolute control Cave took of songwriting responsibilities after 1994's Let Love In). The Road proves that the man really can do anything -- even, that is, take a back-seat.

 

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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