Colum McCann: "What could be worse than being called a historical novelist?"

The Books Interview.

TransAtlantic moves back and forth between Ireland and the US. You haven’t written much about Irish history before now – are you becoming more interested in writing about the past?

I didn’t want to write a historical novel. Jesus, what could be worse than being called a historical novelist, as if you’re preserved in amber? Despite their complex and reasoned arguments, people like Peter Carey and Hilary Mantel run up against that assumption all the time. It’s the idea of becoming an alternative historian that really interests me: a historian of the smaller, more anonymous moments. It’s a privileged position for the fiction writer, one that opens up a lot of pores – and sometimes wounds, as well.

And, of course, there is a narrative element to any work of non-fiction.

I’m interested in the idea that these categories don’t really exist. Aleksandar Hemon says that, in Bosnian, there is no word for either “fiction” or “non-fiction”: there is only “storytelling”. He inserts himself into much of his work but it’s a construct. To put it another way, what Google or Wikipedia says about you might be an utter fiction. The storyteller must at least be responsible to a textural truth: not so much the dates and facts but the textural contradictions that he or she finds.

One great historical event in the novel is the arrival of Frederick Douglass, the former slave and orator, in Ireland. How did you come by it?

To tell you the truth, I can’t remember. Academics began to write about it in the late 1980s and early 1990s, probing the relationship between “the black and the green”, class, culture, slavery – asking questions like: when did the Irish become “white”? But I think most people had forgotten until Obama came to Ireland in 2011. He quoted Douglass: “I am covered with the soft, grey fog of the Emerald Isle. I breath and lo! The chattel becomes a man.”

Ultimately, the novel works around these events, through the lives of women.

I knew I wanted to write about Douglass and George Mitchell [the US special envoy for Northern Ireland from 1995-2001] but there needed to be something in between them. I thought, what is the matrix here? I became interested in Douglass’s housekeeper, Lily, and slowly it became a novel about women, about the line between non-fiction and fiction, the male narrative and the female narrative, the anonymous and grandly historical lined up side by side and working with each other.

I noticed in the acknowledgements that you spoke to Tony Blair as part of your research.

Yeah. Believe it or not, I got a half-hour face-to-face with him in New York. I told him first off: “I’m not going to talk about politics.” Or, “I’m not going to talk about other politics” – I was thinking, what if he googles some of my articles about Iraq. I told him I’m going to ask only about Northern Ireland and what the peace process was like. I asked him what he thought of Mitchell and, fair play to him, after a few minutes of dancing around each other, he said, “Look, I’ll be honest with you. That process did belong to Mitchell. A lot of us came in and stood on its back and it carried us a lot of different places. That’s politics. But it was people like Mitchell on the ground who did a lot of the work.” When I told Mitchell, he was very happy.

Colum McCann’s “TransAtlantic” is published by Bloomsbury (£18.99)

Colum McCann in Paris earlier this year. Photograph: Kenzo Tribouillard/Getty Images.

Philip Maughan is Assistant Editor at the New Statesman.

This article first appeared in the 01 July 2013 issue of the New Statesman, Brazil erupts

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