The music of horror films

From the lullaby in Rosemary's Baby to Bernard Herrmann's final score in Taxi Driver, an unforgettable episode of BBC Radio 3's In Tune discussed music in thrillers.

An unforgettable episode of In Tune (weekdays, 4.30pm) discussed music in horror films and thrillers, from the curdled lullaby in Rosemary’s Baby to the Wagnerian thrum characterising the best Hammer soundtracks. The BBC’s cross-media “Sound of Cinema” season has been programmed in precisely the right way: as though by obsessives in relentless pursuit of exciting sensations. (Let’s stick on the 1933 King Kong at prime time on a Sunday on BBC4! Let’s have a foley artist snapping rhubarb near a microphone to replicate the sound of catastrophe-shattered limbs!) The composer and silent movie accompanist Neil Brand gave a burst of the “landing at Whitby” scene from Nosferatu on a piano, relishing his role as both jukebox and magician – you could hear the audience fizzing.

The Tippett Quartet played music from Psycho, so intricately full of hostile power that you found yourself wondering why its composer, Bernard Herrmann, bothered using an entire orchestra. And here was Herrmann’s widow, Norma, gossiping about her long-dead husband (whom she still dotingly called Benny) and his final score, which was for Taxi Driver. She confessed that when Martin Scorsese first asked him to consider working on the movie, the caustic Herrmann had replied: “I don’t do cabbies.”

It was a personal relief to hear this lady speak. In the brilliantly useful and contumelious 1991 Hollywood memoir You’ll Never Eat Lunch in This Town Again, Julia Phillips describes her work as a co-producer of Taxi Driver and the inconvenient moment when Herrmann “woke up dead”, aged 64, hours after completing the score.

“His wife freaks out,” Phillips writes breezily, “not least because she literally has not a penny to her name.”

I’d often wondered what had become of this wife – in that weird way that one aside or even half an aside in a book can act like a stone in your shoe – and here she was, not dead in a ditch somewhere, but on BBC Radio 3, happy as a person sitting with a large bowl of Miracle Whip and a spoon, admitting that she really ought to get round to seeing North by Northwest one day because Benny’s music was rather good, don’t you think?

Brand played some of it and the audience went through the roof. This was the definition of euphoric radio.

Michael Phillips receives the Palme d'Or for the movie Taxi Driver during the closing ceremony of 1976 Cannes film festival. Image: Getty

Antonia Quirke is an author and journalist. She is a presenter on The Film Programme and Pick of the Week (Radio 4) and Film 2015 and The One Show (BBC 1). She writes a column on radio for the New Statesman.

This article first appeared in the 23 September 2013 issue of the New Statesman, Can Miliband speak for England?

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