Violent response: a woman demonstrating against the Soma mining disaster flees riot police tear gas, 22 May. Photo: Getty
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When safety gets privatised: Soma marks a new low for the Turkish government

Despite Erdogan’s claims that the disaster was on a par with any other international mining accident in the world since 1862, Turkey’s rate of mining deaths is shocking. 

Much of the anger that has enveloped Turkey since the Soma mining disaster has been directed at the government. “Prime minister, resign!” shout the crowds of protesters marching all over the country. In Istanbul, the day after the blast, I saw a young woman with a coal-smeared face holding a placard that read: “So it seems coal isn’t free.”

Here was a cynical message that got to the heart of Turks’ anger. It referred to something deeper and more serious than the spectacularly botched PR job of the prime minister’s visit to Soma, his insensitive cataloguing of 19th-century European mining disasters, his alleged slapping of a Soma local, the use of force by riot police on mourning relatives and the absence of apologies, resignations or explanations.

“Coal isn’t free” is a darkly significant statement in today’s Turkey. Recep Tayyip Erdogan’s government has made itself popular over its 11 years in power by declaring itself the champion of the masses and giving out bread, macaroni and coal to poor families – often in the run-up to elections.

At the same time, Erdogan’s Justice and Development Party (AKP) has thrown itself into an accelerating programme of privatisation. While government spokesmen boast of the billions of lira generated by these sales, the party’s critics accuse it of selling assets cheaply and strategically to sole
bidders and failing to check on workers’ standards afterwards. A statement from the four main Turkish unions shortly after the blast accused the government of complicity, for even privatising “the safety supervision in the workplace”.

The Soma mine was sold off in 2005 and Soma Holding now pays royalties to the government in the form of 15 per cent of its coal production. The mine still technically belongs to the state, which guarantees it will buy all the coal it produces, giving every incentive to ramp up output while cutting costs. In 2012, the owner of Soma Holding, Alp Gürkan, reportedly boasted that he had reduced the cost of extracting coal from £77 per tonne to £14. This was achieved through measures such as making electric transformers on site rather than importing them. Miners also say that the company employed cheap technical specialists who were not union members and failed to replace outdated equipment. When asked why the mine did not have a refuge chamber, Gürkan replied that it was not required by law.

Two weeks before the blast, the AKP majority rejected the opposition’s parliamentary proposal to look into safety standards at Soma, saying that the mine was perfectly satisfactory: “God willing, nothing will happen – not even a nosebleed.” The energy minister, Taner Yildiz, visited the Soma mine nine months ago and branded it “an example for other mines in Turkey”.

Despite Erdogan’s claims that the disaster was on a par with almost any other international mining accident in the world since 1862, Turkey’s rate of mining deaths is shocking: seven lives per million tonnes of coal, compared to China’s one life per million tonnes. In terms of general workplace deaths, Turkey is the third worst in the world.

The Soma disaster has been compounded by Erdogan’s clumsy response to public anger and the AKP’s zero-tolerance approach to criticism. A Turkish lawyer, who asked not to be named, said: “What has Turkey become? It feels like living in a central Asian dictatorship. It feels like Borat.”

Alev Scott is the author of “Turkish Awakening” (Faber & Faber, £14.99)

This article first appeared in the 21 May 2014 issue of the New Statesman, Peak Ukip

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