Former EDL leader Tommy Robinson jailed for 18 months for fraud

Tommy Robinson, real name Stephen Yaxley-Lennon, plead guilty to mortgage fraud last November.

The English Defence League's founder and former leader, Tommy Robinson, has been convicted and sentenced to 18 months in prison for mortgage fraud, reports the BBC. The fraud amounted to £160,000 over six months. The 31-year-old, from Luton, was sentenced at St Albans Crown Court after pleading guilty in November 2013.

Robinson - whose birth name is Stephen Yaxley-Lennon, and who goes by a range of different aliases - left the EDL in October 2013, to collaborate with the Quilliam Foundation think-tank on the subject of "counter-extremism". Medhi Hasan wrote for the NS at the time about Robinson's decision:

Can a fascist renounce fascism? Of course. Can he do it overnight? I’m not so sure. On 6 October, two days before his “defection” to Quilliam, Robinson tweeted that “sharia legalises paedophilia”; on 4 October, he claimed that Islam was “fuelling” a “global war/Holocaust on Christians”. On 2 October, he tried to intimidate a critic of the EDL by turning up unannounced at what Robinson (wrongly) believed was his home.

Forgive me my cynicism. At a press conference on the day he quit the EDL, the 30-year-old sunbed shop owner from Luton did not apologise for or acknowledge his previous anti-Muslim remarks; nor did he renounce, denounce or disown the EDL. So far, he seems only to have rebranded, rather than reformed, himself. Robinson, however, is an irrelevance. So, for that matter, is the EDL. The hate-filled antics of these balaclava-clad thugs have distracted us from a much bigger issue: Islamophobia went mainstream long ago, with the shameless complicity of sections of the press.

Look at the numbers. A Cardiff University study of 974 newspaper articles published about British Muslims between 2000 and 2008 found more than a quarter of them portrayed Islam as “dangerous, backward or irrational”; references to radical Muslims outnumbered references to moderate Muslims by 17 to one.

Look at the little-noticed conclusion of Lord Justice Leveson’s November 2012 report into the “culture, practices and ethics” of the press: “The identification of Muslims . . . as the targets of press hostility . . . was supported by the evidence seen by the inquiry.”

Look, above all else, at the way in which headlines, stories and columns reflect much of what Robinson says – without being tainted by the fascist whiff of the EDL.

“There is a two-tier system, where Muslims are treated more favourably than non-Muslims,” Robinson claimed in a speech in Leicester in February 2012. Consider, however, the lurid headline on the front of the Daily Express, in February 2007: “Muslims tell us how to run our schools”. Or the Daily Star’s splash in October 2008: “BBC puts Muslims before YOU”.

Spot the difference?

Read more.

Robinson at an EDL protest near Downing Street, May 2013. (Photo: Getty)

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