The perils of Friday night drinks

An unconvicing take on the office romance.

Tom is quite a bloke. He has never met a girl yet that he couldn't make chuck him by means of passive resistance, "like a romantic Gandhi". We gain privileged insight into this miracle of unreconstructed maleness through his asides, when we are invited to be co-conspirators in his puerile, even murderous imaginings: he tells us confidentially that he understands the mentality of the sex killer, and can "see the appeal of hacking them to pieces and putting them in bin-bags afterwards."

It's fair to say that I didn't exactly warm to the hero of My Romantic History (played by Iain Robertson), though many in the audience at the Birmingham Repetory studio theatre found the disparity between his inner voice and his public one quite hilarious. Fortunately, if belatedly, some 35 minutes into the show, we are then given access to the inner thoughts of the object of his musings, Amy from the office (Alison O'Donnell). Events are replayed from her perspective, and she proves to be every bit as unconvinced and even disgusted by the relationship as Tom. "He smells like bums" is her comment on waking up with him.

Playwright D C Moore sketches a familiar breed with the male commitment-phobe (it's Friends, by way of Peep Show out of High Fidelity), but in comparison, Amy's motivations seem both obscure and contrived. She's apparently dating him ("like fucking Americans") to prove to her co-worker that she can. But there is comedy capital to be made all the same from the unreliability of perspective: Amy recalls things in a rather different way from Tom. Speeches are attributed to different people, and the emphasis of scenes subtly shifts. Her recollection of his chat is along the lines of "blah blah blah, pretty serious about my music back then, blah". And nowhere is Tom and Amy's view more faulty and corrupted than the retrospectives on their first loves, the idealisation of which scuppers their chances of present day romance.

Nominally an office rom-com, My Romantic History doesn't, in truth, explore the office environment except to give the play an appealingly quirky setting, courtesy of designer Chloe Lamford. The office notice board gradually becomes a scrapbook collage of former loves and significant articles, like the Polaroid of a tattoo, or the manga cartoon of boyfriends past. An ancient slide projector is recommissioned to give low-tech, nostalgic presentations on the couple's love affairs, and the filing cabinet does a turn as portal to an outside world - at one point beautifully illustrating Tom's depressing ubiquity, as he appears to teleport in from various locations holding by turns coffee cup, lunch-tray and a clutch of photocopies.

Cardboard boxes are stacked to vertiginous heights; some are suspended from the ceiling, and jettison objects relating to the romantic narratives - a Magic Tree car freshener here, or a phone there. Lamford's ingenious crates suggest not only memory storage, but also a feel of pro tem making- do, and of movement between places. As the play states repeatedly, nothing lasts for ever, and it's as if Tom and Amy's relationship, by rights a throwaway and short-lived affair, has been accidentally given a lamination job and acquired a habit-hardened carapace of permanence, through motives ranging from cowardice to inertia.

Moore's office is a workless and, it must be said, joyless place, with none of the camaraderie, intimacy or shared experience that make the office such fertile ground for colleague-coupling. Amy and Tom's liaison is a purely contingent one, an anthropological likelihood based on sharing the same space. The doubling up of roles only serves to emphasise its arbitrary nature: Robertson and O'Donnell, clad in the cheap suits of junior office staff - all crackling polyester and sensible shoes - jump nimbly in and out of roles as they supply the bit parts in each other's drama. In this they are abetted by a protean Rosalind Sydney, whose main part is awful colleague Sasha, with her moon cups and her Sunday samba drumming.

But where the relationship between the lovers may be bloodless to the point of being perfunctory, under Lindsey Turner's direction the actors generate a real and unexpected warmth with a challengingly small audience. This adds considerable charm to a light-hearted memo on the ways in which we settle for each other, our partial takes on past and present, and the perils of Friday night drinks. Like a day at the office, there are lots of shared jokes, and it is a little too long.

OLIVER BURSTON
Show Hide image

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