Review: Lose #4

Alex Hern reviews an unexpected "fashion issue" of Michael DeForge's comic.

Lose #4
Michael DeForge
Koyama Press.com, 44pp, CDN$8.00

When you buy a comic described as "the fashion issue", normally you know vaguely what to expect. If it's not actually stories about clothes, then it is stories involving fashionable people, stories about the world of fashion, or just lots of pictures of people looking good. With Michael DeForge, you can be certain that you won't get what you expect.

Lose #4 — the fashion issue — is lead by two stories. The first shows at a teenage boy's literal metamorphosis into a leather-and-studded punk; the second is an examination of the lives, fashions and mating habits of the Canadian royalty. Neither of them start, or finish, or do any of the in-between bit, quite like any other short story I've read before. I mean… look, the very first page of the book features a couple having sex as they watch a porn film featuring newspaper comic stalwarts Dilbert and Nancy. It doesn't get any more conventional from there.

The first of the stories, "Someone I Know" is most reminiscent of other works, particularly Charles Burns' coming-of-age classic Black Hole. David, a young film-school student, takes a girl to a new club, Grand Room, to show off. He realises his mistake when he gets in and sees the leather everywhere; Grand Room is a sex club. But when David wakes up the following morning, there's a metal stud poking out of his arm, and it won't come off.

The cover of Lose #4

"Someone I Know" is followed by the stranger still Canadian Royalty: Their Lifestyles and Fashions. Presented as an anthropological guide, DeForge explains the life of the Canadian royalty. Not, mind you, Queen Elizabeth and co. The Canadian royalty are, instead, a semi-human race with their own customs, physiology and, above all, fashions. "If a royal ever undresses, he or she is stripped of his or her title. A famous example of this is Princess Charlotte's public disrobement on national television."

The common thread between the two stories is the freedom they give DeForge to show off his wonderful sense of design. The studded, buckled and leathered outfits of Grand Room, and the ludicrously elaborate and malproportioned robes of the Canadian Royalty, are both things which you can get lost in, mentally mapping every seam, every change in texture, every safety pin and fold of fabric. For good reason, the Canadian royalty section in particular is broken up with galleries of the royals themselves — Margrave Blunder (1945-2001), Prince Theodore (1987-present), Viscountess Mary Pillow (1952-2009) and so on.

The names should give a hint as to the sort of humour DeForge employs. He has much time for silliness — not just the weirdness of the stories, but also things which would be more at home in a Python sketch. A character, handed an x-ray by his doctor, points out that it's actually an ink drawing. The doctor ignores him and carries on. The lives of the royals are ghoulish, but Princess Charlotte flopping on the floor after disrobing for the first time in her adult life has a dark edge to it.

But the best piece in the book is the one which is played far straighter. "The Sixties" is the beginning of something out of the Twilight Zone. A teenage girl narrates her perfectly normal life in a town where everything has a disease called stacyface. It only has one symptom: your face becomes Stacy's. It starts with typical DeForge weirdness, as she meets a deer in the forest with the same face as hers. But the sheer normalcy of so much of the rest of the story — the lack of the over-the-top oddities of the others — gives it a very different, and far more unsettling, feel.

It's still weird seeing, like, old man bodies with stacyface. Babies, too. Fuck this fucking town!

A page from Lose #4. Image: Koyama Press

Alex Hern is a technology reporter for the Guardian. He was formerly staff writer at the New Statesman. You should follow Alex on Twitter.

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