This a Red or Black world. And I'm stuck in it.

If you're going to have a random show, make it a random show.

Chance is such a funny thing. Only the other day, I was politely written to by a potential employer and told that, while I had qualified to be shortlisted for a job, they'd picked the interviewees at random, and sadly I hadn't made the cut. My bingo ball hadn't come up. Such is life. This is the world of Red or Black?, the gameshow that everyone's talking about this week.

We're not really talking about it in a spectacularly good way, though. We're talking about it, saying "My god, I never knew television could be so bad." I had thought that, with Epic Win, the BBC had succeeded in doing the impossible - making an updated version of You Bet! that was even worse than the days of Brucie's sofa-chewingly execrable "don't fret, get set" rap, but no, this was worse.

This is everything about gameshows that vaguely involves skill, or knowledge, and boils it down to a binary choice: red or black, 0 or 1, on or off. "The show where luck, and luck alone, can win £1m," chirps Dec, as if it's something to be proud of. People cheer the lucky (or unlucky) wheel, which has its own, somewhat sinister, rococo leitmotif.

Luck, lucky, luck. That's all it is. It's not just me, surely, who finds something a little unsatisfying about that, something that verges on an insulting whiff of pointlessness.

When you're watching some gimp blunder through a gameshow's multiple choice with guesswork, at least you know there's something slightly better than total and utter blind chance deciding whether they're going to progress or not. They're making educated guesses. With Red or Black, you could just submit your guesses before the show. Red black red black black red. Save time.

It's easy, I suppose, to call a turkey a turkey. If it looks like a turkey, it's probably a turkey. And for the avoidance of doubt, I'd say this turkey is a turkey. Gobble gobble. But I'm more interested in the odd debate that sprung up this week about the morality - or otherwise - of letting a convicted criminal win a million pounds. The first winner was revealed to have been previously convicted of an assault, allegedly against a female victim, which led to a bit of red-top mock outrage about whether he should be allowed to have his cheque. That led to more background checks being done on contestants, and others being sifted out.

I suppose we want to believe, wrongly, in some kind of natural justice. We don't like stories like the one about 'lotto rapist' Iorworth Hoare and we want to think that only the deserving will be winners, or should be allowed to be winners. But an awful lot of undeserving people luck out all the time, every day, in every field. It might be unpalatable, but there it is. Luck doesn't morally censure.

Personally, I think if you're going to have a random show, make it a random show. Don't hone it down to a few contestants who are spotless enough not to have embarrassing things in their pasts; open it up, wider, to people who've really done wrong. Robbers, muggers, paedophiles, all sorts. Imagine one of them with a big beaming grin as their lucky numbers come up.

That's luck. It doesn't care who you are; it just rewards the lucky.

 

Patrolling the murkier waters of the mainstream media
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