Reviewed: Fifty Shades of Feminism

Woman’s hour.

Fifty Shades of Feminism
Edited by Lisa Appignanesi, Rachel Holmes and Susie Orbach
Virago, 336pp, £12.99

In 2013, feminism is at a crucial moment. In the west, the dreaded “30 per cent problem” is looming: because some gains have been made, there are fewer stark, staring injustices to stir the troops to action. (It’s named after the idea that once female representation in a particular area reaches a third, many people feel that that’s fair – or even that there are too many women around.) In countries such as Tunisia and Egypt, meanwhile, there is a struggle to articulate a women’s rights movement with its own identity, one that cannot be dismissed as an imperialist import. And for God’s sake don’t even mention pornography or prostitution: ask three feminists for their views on those and you’ll get four opinions.

On to this battlefield strides Fifty Shades of Feminism, a book that is resolutely unembarrassed about taking its name from an oldfashioned romance novel, albeit one with lashings of BDSM and terrible dance-based metaphors. I should say that I love the idea of this book and I love that it got published. It feels as though there’s a greater energy to the feminist movement now than I’ve experienced before in my adult life; there’s a critical mass of women who just won’t shut up about the things they care about.

That said, there are a few, perhaps inevitable, problems with a collection of this kind. First, there are several references to how quickly it was pulled together and the book seems to have lost count of its contributors somewhere along the way. Instead of 50 shades, the back cover lists 56 names and there’s a further essay by a young, feminist prizewinner tucked away at the back. Hey, who cares? Maths is for dudes, anyway. (This is a feminist JOKE. Don’t write me letters.)

The bulging list of contributors suggests that the editors might have had to cope with some high-level ego-management; and, because of the format, there are some crunchy gear changes. (Try going from Camila Batmanghelidjh ending a piece with “I’m a drunken whore with alternative boobs!” to Bidisha’s stern list of woman-hating behaviour such as “belittling and victimblaming” for a taste of the varying tones of contemporary feminist discourse.)

There are also occasional chapters that a harsher editor would have rejected: Shami Chakrabarti’s disjointed list of heroines and Liz Kelly’s technical, footnote-heavy description of the cases of Jimmy Savile and Julian Assange are the most obvious. That Kathy Lette has been enlisted to provide the “funny bit” also tells its own story.

But enough carping. Where this book excels is where its contributors approach the topic from an oblique angle: where they show, rather than tell. In this vein, Meera Syal’s reflections on playing Beatrice in a Bollywood-inspired Much Ado About Nothing are exquisite. The Chinese author Xinran’s chapter, showing the sexist assumptions behind the construction of five Mandarin written characters, is revelatory. Ahdaf Soueif’s bittersweet story of her Sri Lankan housekeeper’s return home undermines the easy narrative of the developing world’s aspiration to be more like the west. I also loved the novelist and video games writer Naomi Alderman’s comparison of the unabashed male domination of the tech world with the subtle sexism of publishing – but then it could have been written specifically for me.

It is intriguing that although the book is filled with quotations and illustrations, there is relatively little formal experimentation in the texts. A rare example comes from Jeanette Winterson, who juxtaposes her misgivings about porn with quotations from X-rated websites. The other surprising experimental highlight was the long free verse by Laurie Penny, of this parish.

Previously, I would have said that a feminist poem sounded about as appealing as a Vogon one but Penny’s scalpel-sharp observation is here complemented by some rhetorical fireworks: “There are more of us than you think, kicking off our high-heeled shoes to run and being told not so fast . . . who dared to dance until dawn and were drugged and raped by men in clean T-shirts and woke up scared and sore to be told it was our fault . . . who were told all our lives that we were too loud too risky too fat too ugly too scruffy too selfish too much . . .” It could have been excruciating; instead, it’s intoxicating.

Overall, the three editors of Fifty Shades – Lisa Appignanesi, Rachel Holmes and Susie Orbach – have made a conscious effort to keep their feminist church broad and their contributors are a diverse bunch in terms of age, race, sexuality and nationality. (Although, given their inclusiveness, the absence of a transgender writer does seem pointed.)

What does this book tell us about modern feminism? That it can be angry and warm and witty and wise; that there are more feminists than you might think and they care about an astonishingly broad range of topics; and that, as all women know, there aren’t enough bloody hours in the day.

Meera Syal’s reflections on playing Beatrice in a Bollywood-inspired Much Ado About Nothing are exquisite. Photograph: Getty Images

Helen Lewis is deputy editor of the New Statesman. She has presented BBC Radio 4’s Week in Westminster and is a regular panellist on BBC1’s Sunday Politics.

This article first appeared in the 01 April 2013 issue of the New Statesman, Easter Special Issue

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