Why are there so few penises on television?

There's an insidious double standard in operation on the small screen - naked breasts abound, but we never get to see a man's sexy parts.

Let’s look at a couple of moments in recent influential film and television. The first moment is that bit in Judd Apatow’s Knocked Up (2007) when Ben (Seth Rogen) and his feckless housemates are engaged in “research” for the business they’re trying to get off the ground – an online database collecting the exact timings of occurrences of nudity in films. They later find out such a service already exists: Mr Skin, a real website and, as expected, “NSFW”.
 
The second moment comes in the first season of HBO’s fantasy drama series Game of Thrones. Littlefinger, “the pimp” (Aidan Gillen), delivers a monologue in which he explains his childhood and, by extension, his character. Throughout the lengthy scene, there are two naked women in the background, vocally having sex. On and on Littlefinger’s monologue goes, and on and on go the women. The TV writer Myles McNutt coined the term “sexposition” to describe such a device and I’ve found myself using it with gratitude (“Thank God this term now exists!”) and resignation (“Oh God, I can’t believe this term exists”).
 
These two moments say a few things about the state of sex on our screens. Breasts are a symbol, a signpost and a shorthand for all that is “sexy”. Breasts, sometimes useful for feeding children, are also secondary sex characteristics (the same as facial hair and Adam’s apples – and yet entirely different). Naked breasts are the universal bloodtype of the screen: show them and everyone gets it.
 
Most societies operate a “no naked breasts” rule in most public spaces, while granting men permission to go shirtless if they want: a man’s chest is not equal to a woman’s. It follows that the corresponding “sexy” part on a man would therefore be his penis, yes? Yes. So why are there so few penises on television?
 
I am not the first person to query this. Mhairi McFarlane’s hilarious essay on a blog called The Flick is my favourite piece on the subject, and recently the American comedy website CollegeHumor released a video, featuring four female comedians and entitled “HBO Should Show Dongs”, which asked the same question.
 
“Hi, HBO. It’s us – your female viewers,” they begin. “From the brothels of Game of Thrones, to the brothels of Boardwalk Empire, all the way to the . . . brothels of Deadwood. . .” says one woman, “. . . you’ve shown us a whole lot of boobies,” says another. “It’s time to even the score. We’re not saying ‘no more boobs’, we just think that you should show . . . dongs.”
 
Why don’t we get to see that many penises on screen, outside porn? I’d wager that, for all the usual arguments – penises are not “aesthetically pleasing”; they’re comical, unsexy; viewers don’t want to see them – the reason there’s such a dearth is that women’s sexuality, and how they express it, is still clothed in centuries-old fear and misunderstanding.
 
That and the fact that TV is still largely the domain of straight men making content for other straight men. It’s why in the HBO series Hung – specifically about a man with a large penis – we never even get to see it. And as for the “Women don’t want to look at that!” argument, I offer you two words: Magic Mike. The excitement caused by this 2012 movie about male strippers cannot be overstated. We spent more than $160m at the box office trying to see unclothed penises in Magic Mike – and even then, there weren’t any.
 
As the poet Bridget Minamore had it on Twitter, “Shout out patriarchy for forcing male objectification movies for straight women to be smart and well shot to get anywhere!” Sometimes we just want penises – no bells and whistles, and no plot. As Daniel Bergner tells us in his book What Do Women Want? Adventures in the Science of Female Desire: “More than anything, though, as an isolated, rigid phallus filled vaginal blood vessels and sent the red line of the plethysmograph high, niceties vanished, conventions cracked; female desire was, at base, nothing if not animal.”
 
The ladies of CollegeHumor nailed it with their proposal: “For every topless background extra, every actress that bares her bouncies but doesn’t even get a line, every minute we have to sit through this dumb double standard – you owe us an inch of Grade-A manmeat.” Seems fair. 
Bared breasts have been a regular feature of Game of Thrones. Image: HBO

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 26 August 2013 issue of the New Statesman, How the dream died

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