Orange Is the New Black gives us a different view of the debate over "privilege"

Netflix's newest production offers nuance and subtle insight into the uses and abuses of power.

In episode four of Netflix’s House of Cards, Congressman Francis Underwood (a knowing Kevin Spacey, whose performance is almost but never quite over the top) asks the wildly ambitious young journalist Zoe Barnes (Kate Mara): “Do you have a man that cares for you? An older man?” Then he asks if she knows that older men hurt women like her before discarding them.
 
“You can’t hurt me,” Zoe replies, almost mockingly. In a certain light, it seems as if she’s in charge: it’s her flat they’re standing in and her tone suggests that this isn’t her first rodeo. She knows Underwood’s power in Washington but, crucially, she is also aware of her own –of her job, her desirability, her clear-sighted understanding of their transaction. All of which is interesting, because although she has shown flashes of initiative up to this point, most of the power on display has been his.
 
Zoe has the weight of popular culture on her shoulders. We know that these women rarely get out alive, metaphorically or otherwise, and we expect whatever control they have in the moment to be fleeting. Young women looking to make something of themselves and older men with the clout to help them do so . . . It’s a cliché for a reason.
 
I was thinking about this as I watched Netflix’s newer production Orange Is the New Black. It’s set in Litchfield, a women’s federal prison in upstate New York; we get to observe its in and outs through the experiences of Piper Chapman (Taylor Schilling), a middle-class white woman doing time for transporting drug money a decade earlier. The set-up has a very clear line on who’s powerful and who’s not. Almost all the women in the show –happily of many ages, races, classes and religions – are incarcerated and almost all the men, with the exception of Piper’s fiancé, Larry Bloom (Jason Biggs), are in charge of maintaining that incarceration.
 
It’s a stark gender divide and every episode sends the message even more forcefully: these women are powerless and the system that has imprisoned them and enforces their passive state has a male face. It takes a little time to scratch the surface, however, and then it becomes clear that as far as this programme is concerned, the most interesting relationships –those that explore the day-to-day dynamics of power – exist between the women.
 
Piper, whose mother has told friends that she’s “doing volunteer work in Africa” for the duration of her sentence, is the newbie, always on the back foot until she has picked up enough prison smarts to get through the next 15 months. On her first day, she manages to offend the long-timer and kitchen head Red (Kate Mulgrew) by insulting the prison food. The gasps that follow show us just how grave a mistake this was.
 
When Piper attempts to make amends, Red says to her, “You seem sweet, honey. But I can’t do shit with: ‘I’m sorry.’ Not in here.” Later, she growls, “March your yuppie ass out of my kitchen.” The apology, when it is finally accepted, comes at a cost and perfectly illustrates Red’s influence over the entire prison. This is reinforced throughout the series by everything from the “elections” to the contraband routes. The system has not beaten Red and she’s holding on to her limited control.
 
Another long-time inmate of interest is Miss Claudette (Michelle Hurst), a woman of few words but great presence – no one knows what she’s in for but they know she hasn’t taken a visitor in a decade because she “won’t do strip-search”. Her power manifests itself in the order in her bunk space – no mess and no noise. She tells her rude new bunk mate: “Watch yourself, little girl. This is not America. This is the Litch and I’ve been here a long while.” It sends shivers down your spine. Miss Claudette knows her power and her backstory shows just how formidable she is.
 
Power, in essence, is relative: that’s what we debate in those endless online conversations about “privilege”. Powerlessness in the wider world does not translate to powerlessness in the microcosm. Orange Is the New Black gives us a different view of this debate, layered with nuance and subtle insight and without the commonplace device of a “great man” and a “naive girl”. It passes the Bechdel test with flying colours and it is, in every other way, a winner.
The cast of Orange Is the New Black. Photograph: Netflix.

Bim Adewunmi writes about race, feminism and popular culture. Her blog is  yorubagirldancing.com and you can find her on Twitter as @bimadew.

This article first appeared in the 19 August 2013 issue of the New Statesman, Why aren’t young people working

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