The Art of Listening: Justin Bieber 800 per cent slower

On the ambient potential of a teen pop star.

Children grow up quickly these days but none more so than little Justin Bieber, who has announced plans to publish his memoirs at the tender age of 16. If you've noticed a preponderance of helmet-haired youths in your neighbourhood recently, Bieber may well be the reason. The Canadian teen is a global superstar, popular largely with children, many of whom ape his peculiar hairstyle, in which the hair is brushed forward over the forehead and ears, giving the impression of a man three times his age trying to hide a receding hairline.

It is a grievous journalistic cliché to write about an artist's look for lack of anything to say about his or her music, but Bieber's sheer fame, quantifiable by all manner of digital means, threatens to make normal critical faculties redundant: seven hit singles from his debut album; 314,613,808 YouTube views of his song "Baby"; 5,053,803 followers on Twitter; 10,818,838 Facebook users who "like" Bieber. In the face of this data onslaught, the aggregator website Metacritic is able to muster only the feeble statement that his most recent release, the My World 2.0 album, has had "generally favourable reviews".

Bieber is our latest Art of Listening subject, not for his own music, but for what others have done with it. Fittingly for a global superstar whose fame rests largely in the digital ether, his recent country-tinged ballad "U Smile" has been put through the digital mangle (this is a technical term) by a musician named Nick Pittsinger and stretched so that it plays 800 per cent more slowly than the original.

Using a piece of software called PaulStretch, Pittsinger maintained the song's pitch so that what results, rather than a turgid lower-end growl, is a surprisingly pleasant collection of ambient noises. Some listeners have compared the new track favourably to the music of the Icelandic band Sigur Rós - but that only goes to show how music that is marketed as ambient or "experimental" can often be based on conventional chord progressions and song structures. One reason for Sigur Rós's popularity is that their songs still have simple hooks and recognisable choruses, despite their slowness.

The salient feature of "U Smile 800 Per Cent Slower" is Bieber's castrato-like wail, extended into a seemingly endless, crystal-clear peal
that arches over the entire 35-minute track and morphs too slowly to form recognisable syllables. It's as if he had been suspended in time - his teenage charm turned into inchoate moans, languishing amid a series of ill-defined whooshes of sound.

But perhaps this is how Bieber, who seems to be moving through life 800 per cent faster than the rest of us, experiences the world around him. Our hurrying to and from work, our moments of panic about how we will pay the next month's rent, or whether our jobs will still be here a year from now, merge into an indistinct, smeary backdrop to the life of this boy who has already amassed more capital than most people on the planet will see in their entire snail's-pace existence.

None of this should be confused with the practice of "i-dosing", which was fearlessly exposed by a recent investigative feature in the Daily Mail. According to the Mail's reporter, i-dosing is a craze whereby American teenagers "change their brains in the same way as [taking] real-life narcotics" by listening to clips of ambient music that feature binaural beats - two tones played at slightly different frequencies in either ear. "The craze has so far been popular among teenagers in the US," the Mail says, "but given how easily available the videos are, it is just a matter of time before it catches on in Brit­ain." Let's hope that young Bieber fans aren't tempted by such nefarious pursuits.

You can read more Art of Listening columns here

Daniel Trilling is the Editor of New Humanist magazine. He was formerly an Assistant Editor at the New Statesman.

This article first appeared in the 13 September 2010 issue of the New Statesman, France turns right

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