My love-hate relationship with Spoken Word

Spoken Word is a frustrating art form. Its historical roots run deep, but in its present form it fluctuates between being vibrant, engaging and socially active - to pretentious and dull.

When Gill Scott-Heron wrote "The Revolution Will Not Be Televised" I’m pretty sure he wasn’t talking about a revolution in spoken word. What he was talking about was a civil, social and financial revolution, a revolution where "Women will not care if Dick finally got down with Jane...because Black people will be in the streets looking for a brighter day". Scott-Heron used spoken word to simultaneously critique capitalism, endorse change, and create a wonderful hymn of political and social disenfranchisement.

Spoken word grew out of a desire to comment on the status quo, using an unconventional free verse style to evoke unconventional thoughts. From ol’ Gil, to Allen Ginsberg, and, arguably, all the way back to Walt Whitman, it was a form that fundamentally didn’t conform in either style or content. An anthem for those who didn’t agree with the norms in their society. An aural/oral middle finger to those in charge.

It makes sense, then, that spoken word resonates with a young contemporary audiences. The internet has done wonders for the form, allowing the angry, intimate words of the speaker to reverberate out of your laptop as you nod your head emphatically. "Yes!" you say (in your head) "we *should* liberate [insert oppressed group] and not stand any longer for [insert outrageous act]." Often you will share it on your [insert social media forum] and feel just a little bit better about yourself.

It is usually personal and evocative. Sometimes it’s quite cool. Scroobius Pip, an Essex born hip-hop spoken word artist, seems to have become a necessary part of gaining middle-class hipster accreditation. Scroobius Pip became famous after his collaboration with Dan le Sac on the track "Thou Shalt Always Kill", which is, ironically, a kind of perceptive, cynical deconstruction of what would soon become hipster identity. "Thou shalt not stop liking a band just because they’ve become popular" he articulates, in his low-budget video, "Thou shalt not attend an open mic and leave before it’s done just because you’ve finished your shitty little poem or song you self-righteous prick."

I deeply wish spoken word culture had listened to Scroobius and Dan. I have attended too many circle-jerk university spoken word nights; winced as my contemporaries ruin their poetry using over-chewed rhetorical flourishes. Pauses. For effect. Clichés, that cause your heart burn with the fire of injustice. A false culture of imagined oppression - a self-obsessed anthem of inflated victimhood.

Spoken word is an art form that walks a fine line between being compelling and contrived, and more often than not, people don’t fall on the right side. However, no matter how cynical one may become, there are artists that use spoken word to introduce young people to poetry, or promote feminism. The ones who use themselves as a subject only to critique and inspire, not to self-aggrandise. Poems such as Mark Grist’s "I Like a Girl Who Reads" or Kait Rokowski’s "How to Cure a Feminist" are perfect examples of spoken word which doesn’t make me want to bash my head against something heavy. They are funny, genuine and intelligent.

It’s only those writers who manage to steer away from pointless rhetoric, who don’t hide the flaws of their writing with saccharine phrases, that manage to successfully convey their message. Poetry is an art form on the wane, but the culture of spoken word has reinvigorated it - which is anything but bad. Even if it does mean putting up with a few wankers along the way.

The spoken word artist Scroobius Pip. Photograph: Getty Images.
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