Let's not act like selfies and food pics are 21st century phenomena

No, taking a photo of your brunch isn't a "revolutionary" act. Taking a selfie isn't one, either. We've been doing them both for centuries.

Instagram held a press conference today to announce that it was adding a messaging service to its app. That's all. Messaging.

Just to make that clear:

Kevin Systrom is the co-founder of Instagram, and his presentation contained some choice cuts of ludicrous Silico-speak. At one point he literally described the act of taking a photo of one’s brunch as “revolutionary”.

We can only wonder what he makes a painting like this:

(Image: Wikimedia Commons)

That's Caravaggio's Still Life with Fruit (1601-1605), a painting of some brunch (or lunch, maybe breakfast). It's food, is the point. The art galleries of the world are filled with boring pictures of food - it's a topic that has sustained artists for centuries. There is nothing new about fixating on food. The animals on the walls of Bhimbetka and Chauvet might even count as food portraits.

Ancient human-like figures, like these ones painted onto rock in the Cederberg region of South Africa, might even be selfies:

(Image: Wikimedia Commons)

That's a generous interpretation, I realise, but the self-portrait is one of the defining artistic subjects of human art, throughout the world. There are 141 self portraits in the National Gallery's collection, for example. It makes the response to the Oxford English Dictionary's decision to name "selfie" word of the year utterly baffling - there is nothing new about us documenting ourselves.

Think pieces that talked about the selfie's "screaming narcissim" that "sits at the excess of the ultimate theatricalising of the self" seem to treat something rather mundane as something that's - here's that word again - "revolutionary". Smartphones and digital cameras have made it easier to take photos of ourselves and our foods. They've also made it easier to take pictures of landscapes, but you don't see that getting parodied or turned into a Time cover story about the self-obsession of a generation. The difference between now and the Renaissance is the barrier to entry for those who couldn't afford paint and canvas.

The question it feels more worth asking here is this: why do we use new technologies the same as our old ones? Why is that we keep picturing the same things, again and again, but faster and faster? When is a technology amplifying something in our society, rather than actually changing it? And will every technology always end up, inevitably, a thing for porn?

It rather feels that focusing on the method, instead of the motive, misses the point a lot of the time.

Rembrandt pouting for a selfie, c.1630. (Image: Wikimedia Commons)

Ian Steadman is a staff science and technology writer at the New Statesman. He is on Twitter as @iansteadman.

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