Film in 2013 - the year of DiCaprio unchained

What to look out for on the big screen this year.

Since the dawn of time, mankind has been guided through life by the passing of the seasons, the phases of the moon and the patterns of the film release schedule; 2013 is no different. There is the usual January logjam of awardsseason heavyweights: you shall know them by their extravagant length. One, Quentin Tarantino’s slavery revenge thriller Django Unchained (18 January), is one of three new films starring Leonardo DiCaprio. Having at last shed his formerly foetal demeanour, DiCaprio is up to the job of playing his first villain, a sadistic plan - tation owner. The actor will be a good fit, too, in the title role of Baz Luhrmann’s The Great Gatsby (May), opposite Carey Mulligan as Daisy. Before the year is out, he’ll be seen as the jailed stockbroker Jordan Belfort in The Wolf of Wall Street, his fifth collaboration with Martin Scorsese. (There’s an extra DiCaprio treat: a Valentine’s Day rerelease of Luhrmann’s febrile Romeo + Juliet. A new film of the play, adapted by Julian Fellowes, follows in October.)

Carey Mulligan and Leonardo DiCaprio in "The Great Gatsby"

The appetite of Park Chan-wook for Jacobean excess far outstrips mine but I am looking forward to Stoker (March), the Korean director’s forthcoming horror film starring Nicole Kidman and the translucent Mia Wasikowska. Oldboy, the gruesome thriller that made Park’s name, will rise again in October in the form of Spike Lee’s US remake. Anyone fancy putting a few squid on whether its star, Josh Brolin, will replicate the original film’s unsimulated liveoctopus- eating scene?

Ben Affleck and Rachel McAdams in the upcoming Terrence Malik film "To the Wonder"

We can no longer look to Terrence Malick for tantalising hiatuses between projects – a mere two years after The Tree of Life, he offers To the Wonder (February), featuring Ben Affleck, Rachel McAdams and very little dialogue, and has been shooting his next two movies simultaneously. Fortunately Wong Kar-wai is available to take up the mantle of Elusive Genius. Hopes are high for The Grandmaster, his first film in five years, starring Tony Leung as Yip Man, the martial arts master who trained Bruce Lee. The picture opens the Berlin Film Festival in February. The wait has been even longer for fans of the British director Jonathan Glazer. Nine years will have elapsed since his last film (Birth) by the time we see Glazer’s adaptation of Michel Faber’s novel Under the Skin. Scarlett Johansson wears a brown wig to play an extraterrestrial on a killing spree in Scotland.

Blockbusteritis prevents me from mentioning 2013’s numerous sequels. However, the film about which I am most excited is also technically a sequel, albeit a belated and idiosyncratic one. After ambling through Vienna in 1995 as twentysomethings in Before Sunrise and breezing around Paris nine years later in their thirties in Before Sunset, Jesse (Ethan Hawke) and Celine (Julie Delpy) hit middle age in Greece in the conclusion to Richard Linklater’s freewheeling trilogy. One wag has pointed out that the new film’s title, Before Midnight, surely refers to the hour that Jesse and Celine have to be in bed these days.

Ethan Hawke and Julie Delpy in "Before Sunrise"

As someone the same age as the characters, I can chuckle knowingly at that. I’ll be seeing the movie – just not at a late show.

Leonardo DiCaprio in Quentin Tarantino's "Django Unchained". The actor stars in three big releases this year.

Ryan Gilbey is the New Statesman's film critic. He is also the author of It Don't Worry Me (Faber), about 1970s US cinema, and a study of Groundhog Day in the "Modern Classics" series (BFI Publishing). He was named reviewer of the year in the 2007 Press Gazette awards.

This article first appeared in the 07 January 2013 issue of the New Statesman, 2013: the year the cuts finally bite

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