Propagate, propagate: a Worcestershire gardener uses army surplus metal pyramids to force rhubarb in 1962. (Photo: Getty)
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Who knew rhubarb had a dark side?

The dark underworld of West Yorkshire rhubarb forcing.

Even the kindest of souls would struggle to describe Jonathan Westwood’s West Yorkshire farm as a rural idyll. Instead of rolling hills and bleating sheep there are long, low sheds and the drone of traffic from the M62 and M1; just past the pigs and cabbages lie slip roads and raw new housing estates.

Yet inside those sheds, it’s still 1880. The smell hits me as I duck through a crooked Victorian doorway: warm and vegetal, almost rainforesty. Westwood switches on his torch, and rhubarb looms out of the gloom, ranks of spindly pink stalks and acid yellow leaves stretching back as far as the eye can see.

It’s a surprisingly unsettling experience for anyone who read The Day of the Triffids at an impressionable age and so I’m pretty keen to get out again, though not before he has explained that growers started raising the plants in the dark in the late 19th century, to keep the stems tender and sweet. With enough warmth and water, he says with enthusiasm, they can put on two inches a day. Shudder.

Slimy green tendrils of stewed rhubarb never held any fear for me at school, but I find these slender pink stems, shooting up in the silent darkness, strangely eerie. Legend has it that you can even hear “forced” rhubarb growing; its buds bursting, leaves unfurling, stems creaking. Fortunately, I don’t.

The farm sits in a triangle of land between Wakefield, Leeds and Morley which has been a hotbed of indoor rhubarb production for a century and a half, thanks to good soil, a frosty microclimate and (once upon a time at least) a ready supply of fuel from the mines to heat the sheds. Even the fertiliser is unmistakably local; as we drive round the farm Westwood points out great soggy piles of the “shoddy”, or wool waste, that he buys from a mill in nearby Dewsbury.

In the triangle’s heyday during the Second World War, when the government fixed the price of rhubarb to feed a population starved of fruit, there were more than 200 growers in the area. Many folded once bananas returned to Britain, and by the 1980s, Westwood recalls, sales were so poor that the biggest rhubarb grower in the area was ready to pack it in altogether – “and then we managed to get it into the supermarkets”.

This coup, coupled with a revival of interest in classic British cookery, saved the few growers that remained. Westwood sells everything he can grow under the Red Tractor badge and is hoping next year to extend the indoor season, which runs from November to April, to compete with the Dutch.

But it’s still not an easy business: the fuel and labour costs make forced rhubarb an expensive crop to grow. Westwood employs almost 40 people year round and some of them have been working for him for nearly 50 years. Everything, from watering to harvesting and packing, is done by hand.

With only ten growers still operating in the triangle, it’s little wonder the Leeds and District Market Gardeners Association, now in its 94th year, has had to give up the Best Sticks competition at its annual rhubarb dinner-dance for lack of interest. “I think I won it four years in a row – it was just embarrassing,” Westwood says, looking not at all embarrassed.

Indeed, the certificates are still proudly pinned up on the walls above his head in the cluttered farm office where he works with his sister and cousin. “Best box of Timperley”, “Best six sticks of Fenton”, they read – tributes to a vanished world where the Rhubarb Express ran nightly from Wakefield to the London markets.

I ask him what keeps him in the business. “Madness. Sheer madness!” he sighs, shaking his head ruefully. Yorkshire through and through, just like his rhubarb.

Next week: John Burnside on nature

Felicity Cloake is the New Statesman’s food columnist. Her latest book is The A-Z of Eating: a Flavour Map for Adventurous Cooks.

This article first appeared in the 05 March 2014 issue of the New Statesman, Putin's power game

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