Let's not pretend that Diane Abbott's comments were genuine racism

The MP was stupid to refer to "white people", but her tweet has been taken out of context.

Another day, another Twitterstorm - this time a "race row" involving Diane Abbott.

The Hackney MP tweeted "white people love playing "divide and rule". We should not play their game #tacticasoldascolonialism".

Conservative blogs have called for her resignation. Over at ConservativeHome, Paul Goodman writes:

Imagine how the Guardian or the BBC would react if a Conservative MP said that "black people love playing 'divide and rule' ".

They would be right to do so. Such an MP would be maligning their fellow citizens on a racist basis. This is exactly what Abbott has done.

I'm sorry, but this is disingenuous for a number of reasons.

Firstly, let's take the facts. As is standard practice in any good Twitterstorm, the comment in question has been completely divorced of its context. Abbott did not make a cup of tea, sit down at her computer, and think: "Do you know what? I think I'll malign white people now."

As the hashtag referencing colonialism shows, the comment was made in the context of a political discussion: namely, criticism of black community leaders. The use of the term "white people" here is distinguishing from "black people". She was responding to this tweet: "I find it frustrating that half the time, these leaders are out of touch with black people they purport to represent." Black people/white people.

Abbott's choice of words was clumsy , and as an MP she should be more careful. But in this discussion, she is clearly referring to "white people" as a political force in the context of colonialism, not making generalisations about the behaviour of individual white people. Her comments aren't equivalent to, for example, Lauryn Hill supposedly saying that she didn't want "white people" to buy her records.

There is no question that she shouldn't have used such a generalised term, which is highly open to misinterpretation. However, the ConHome blog goes so far as to say she has "deliberately provoked hatred of a racial group, and is therefore in breach of the 1986 Public Order Act."

Quite apart from the fact that the comment is clearly not inciting racial hatred, the hypothetical white Conservative MP referring to "black people" cannot be a direct comparison. When one racial group is so dominant, both numerically (in Britain) and politically (worldwide), pejorative language simply does not have the same power or resonance. Hence words like "honky" or "goora" (a Hindi word for "white") do not have the same brutal power as words like "nigger" or "Paki". Most of those tweeting outrage are white and will not have experienced the pain that such words and the assumptions that go with them can inflict.

Abbott's choice of wording was stupid. It has offended people, and she should apologise, particularly given her role as an elected representative. Indeed, ethnic minorities have a duty to make sure they don't fall into the same trap as the racism they are working against by making lazy generalisations about "white people". But that legislation exists not just because of the words -- "black people", "Asians", "Jews" -- but because of the centuries of oppression and huge tide of contemporary racism that those words, and the way they are used, represent. This outrage has a hint of tit-for-tat -- "we're not allowed to say these things, so why should you be allowed to?" Let's not pretend, though, that what Abbott actually said is as serious as most instances of racism we see in public life.

 

UPDATE: Abbott has apologised:

"I understand people have interpreted my comments as making generalisations about white people. I do not believe in doing that. I apologise for any offence caused."

She's also tweeted: "Tweet taken out of context. Refers to nature of 19th century European colonialism. Bit much to get into 140 characters."

Let's hope that is the end of that.

UPDATE 5.35pm: I debated this subject on BBC News 24 with Harry Cole earlier this afternoon. Here's the clip:

Samira Shackle is a freelance journalist, who tweets @samirashackle. She was formerly a staff writer for the New Statesman.

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