Going underground

A chilling subterannean production of Macbeth.

Rarely have I been more relieved to leave a theatre, and I mean that in the nicest possible way. Belt Up Theatre -- a group of young actors based in York -- have shrewdly commandeered a proper showstopper of a venue for their production of Macbeth: Clerkenwell's House of Detention.

In truth, the dismal catacombs would make even Clitheroe Players' "Charley's Aunt" a special and spooky experience. These vaults banged up the mad and the bad (including children) between 1617 and 1893, and, rather disturbingly, the underground cells sit right underneath a primary school playground. In the splendid tradition of mummers, jugglers and other unsavoury itinerants, Belt Up are not going down well with the locals, for whom the exposure of what lies beneath is somewhat less than welcome.

We begin the performance following the siren wail of the Weird Sisters through the underground black labyrinth. The cells and walkways are illuminated by the odd exposed light bulb, or by guttering candles. This candlelight softens features, but rather thrillingly makes the eyes of the homicidal Macbeth glitter. The audience stumble upon scenes, and are either explicitly welcomed as fellow kinsmen, courtiers and thanes, or made eavesdroppers and voyeurs. It does help to be pretty tall in this spying game, or at least to move briskly and single-mindedly between scenes to get to the front, and I was often left a couple of rows back, seeing only chinks of the action. There are compensatory times, however, when one is so close to the events as to be co-opted into them.

This immersive smudging between actor and spectator makes for some unexpected images, as we group, complicit, around filthy deeds in dark corners. On the night I saw the show there was a large party in attendance from the City of London School, and it was an arresting sight, to see all the shining schoolboy faces framed by the vault arches, or caught haplessly in the cross fire between two actors: Shakespearean extras to all intents and purposes. (They also proved uncannily good at taking off the witches' theme tune in the loos later.) Sometimes the freezing, clammy building itself accidentally enhances the performers' work: when Macbeth is channelling supernatural power by starting up the witches' caterwaul, a big drop of icy water fell on my forehead, right on cue.

Belt Up perform Macbeth with just four male actors, and all the doubling and shuffling around in the dark certainly doesn't make it the most transparent of productions. Rather, it was a mood piece, an opportunity to scry on a serial killer in the stygian gloom. In this, perhaps the House of Detention could have been exploited yet further: as we exited there were tantalizing vignettes of prisoners pacing up and down or pooled on the floor in rags, and perhaps more of these sidelong, atmospheric flashes might have served the actors better than their still-dutiful recitation of text.

Rather neatly Belt Up finish their piece with the witches' chant: "When shall we three meet again?" as though they and their diabolic power of suggestion would simply move on to the next susceptible individual. One emerges, shivering, into the daylight, though by now even the Farringdon tube station works and repairs now look like so many infernal engines.

This theatrical experience is truly dire -- but in the best way.

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