The great burqa/niqab/hijab debate

To ban or not to ban? And what to ban?

From the Independent:

The parliamentary leader of the ruling French party is to put forward a draft law within two weeks to ban the full-body veil from French streets and all other public places.

Extreme? Right-wing? The article continues:

Some senior figures on the left have supported the idea of a legal ban. So has Fadela Amara, a left-wing campaigner for the rights of Muslim women who entered Mr Sarkozy's government in 2007 as minister for urban development.

Most moderate Islamic leaders have sharply criticised the burqa but suggested that it was such a limited phenomenon in France that legislation was unnecessary and might alienate moderate Muslims.

The burqa, per se, is an Afghan tradition allowing a woman only a narrow gauze-covered eye-opening. It is little found in France. The Arab equivalent, the niqab, which has a narrow opening at eye-level, is only slightly more common.

A study by the French internal security services last year suggested that the total number of women wearing both types of full-body veil in France was around 2,000 -- out of a total French population of adult, Muslim women of about 1,500,000.

Two questions immediately come to mind:

1) In the middle of the worst economic crisis in living memory, how can France's ruling conservative party justify focusing its legislative energies on banning an item of clothing worn by 0.1 per cent of the French population of adult Muslim women (or 0.003 per cent of the French population as a whole)?

2) Why did the "French internal security services" commission a study on the burqa/niqab? Is it now deemed to be a national security risk? Do French intelligence agencies have nothing better to do with their time? No other threats to deal with, apart from 2,000 Muslim women with covered faces?

Then there is the matter of the clothing itself and distinguishing between the various types. I'm no fan of the burqa or the niqab myself, and have yet to be convinced of the Islamic legal reasoning behind either garment, but I do recognise the difference between the burqa and the niqab, on the one hand, and the hijab on the other.

Does Yasmin Alibhai-Brown? In her short comment piece on the Indie's news story, and in support of the French ban, she writes:

The use of the burqa has grown like a virus across the continent. Children as young as four are now dressed in hijab.

I like Yasmin Alibhai-Brown. I admire her columns and the clarity and passion of her arguments, even if I don't always agree with her. But if even she cannot distinguish between the burqa and the hijab, two very different garments, how then can she criticise journalists and politicians, on other occasions, for misunderstanding Islam and Muslims?

Yasmin says she endorses the French approach:

I don't like the way the French state or its right-wing parties operate but sometimes there are some good unintended consequences.

I would ask her: isn't this exactly what pro-war liberal lefties said when they got into bed with George W Bush over the Iraq war and the removal of Saddam? And we all know how that turned out . . .

Mehdi Hasan is a contributing writer for the New Statesman and the co-author of Ed: The Milibands and the Making of a Labour Leader. He was the New Statesman's senior editor (politics) from 2009-12.

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