Pretending to eat pasties

A hot pie is just a hot pie; it's not a cultural totem of the working classes.

A hot pie is not a basic human right. Wrap that up in your soggy grease-lined paper bag and take a big bite.

It's taken the possible slight increase in price of mechanically reclaimed sludgemeat in pastry to wake us from our slumber. Now we care. Now it's about our RIGHT to stuff our hungry fat faces with minced-up pigs for the lowest price possible, we've decided it's a very big deal indeed.

This isn't about class warfare, although it's an understandable mistake to make, since most of the things this Government does are about redistributing money from people they don't like (the public sector, people on benefits, people in general) to people they do like (anyone who can afford a £250,000 supper round at Dave's gaff). But this isn't one of them.

Look, I like a pie as much as the next person -- probably more than the next person, judging by my ever-expanding waistline. As a self-confessed problem eater, I am here to tell you that pies are nice.

But for God's sake. A hot pie is just a hot pie; it's not a cultural totem of the working classes. It's a treat, it's not a basic foodstuff. It's not something that people should be seeing as a staple of their diets; it's a fatty, greasy, meaty, sloppy load of bad food. Delicious, sure, but come off it: there are alternative foodstuffs available, which are better for you, and which cost less.

Why are we even talking about pasties? Well, there are a lot of very wealthy people who stand to lose a bit of money if their production-line pastry becomes less enticing; entirely concidentally, they don't appear to have paid for a rather more nutritious dinner at No 10 Downing Street, though of course that wouldn't have affected policy towards their industry in any way whatsoever.

Additionally, some of these companies have a substantial advertising presence in newspapers, which are coincidentally taking up the sausage roll baton to fight for the right to have a hot pastry at lunchtime.

And then there's Ed Miliband. Watch the footage of him in Greggs, if you can, shuffling up in the queue as Ed Balls, finger in his jacket hook-loop, orders sausage rolls.

Not so keen to batter David Cameron on things like privatisation, which his party broadly supports, he's on safer ground when it comes to pasties and pies. It's just an easier thing to do. Come along to Greggs and stand by the meat slices -- it's a sure-fire winner.

This, then, is our political discourse at a time when immeasurable change is being done to the country: posh men arguing about which one of them is more "down with the voters" by pretending to eat pasties.

I could make a tedious analogy between the rather tasteless, homogenised produce in your local bakery branch, and the kind of unpalatable pale slabs of meat who shout at each other in the House of Commons, but what's the point?

This is the way we want it; this is what we have created, and what we respond to. This is the crap they're serving up -- and it's not going to get any better.

 

Pasties: not a human right. Credit: Getty Images
Patrolling the murkier waters of the mainstream media
OLIVER BURSTON
Show Hide image

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