During the 132 minutes of Pacific Rim I failed to have a single thought - not always a bad thing

Director Guillermo del Toro has spoken with open passion about this ludicrous, ludicrous film. In fact, he's right: it's pretty good.

“Entertaining” has become a euphemism for “crap, but with pretty set-pieces": a pleading entreaty offered by sci-fi apologists to save face when discussing yet another underwhelming summer movie we were foolish enough to get excited about.

It becomes an ever weaker descriptor as the years go by, cheapening in value with every genre movie that doesn’t quite work. The rush of excitement when the BBFC card appears on screen deflates quicker and quicker each time, leaving only a distracted internal voice that gnaws on plot holes and wonders when it will all be over.

Sitting through Prometheus last year, and then World War Z and Star Trek earlier this summer, that voice was as loud as popcorn. I began seriously wondering whether satisfying genre movies were even possible anymore or whether – worse yet – my brain was burnt out on effects-driven movies, jaded to spectacle and doomed to overthink any piece of simple fun.

During the full 132 minutes of Pacific Rim, however, I don’t think I actually had any thoughts.

Before a single critical neuron could fire, the film grabbed my mental wrist like the ancient mariner and gruffly set out its pitch with a relentless opening montage. “Here” it said, “is a story about people getting in huge robots to have fights with monsters, and it’s going to be loud”. Leaving no time for me to digest this, it proceeded to launch into the most astonishing fight between a robot and a monster.

Then there was more, and more, and more. The film stuck with its preposterous internal logic with complete attention to detail, and was paced in such a way as to never really leave time for reflection.

Director Guillermo del Toro has spoken with open passion about this ludicrous, ludicrous film. His single-mindedness triumphed in the finished product. Rather than the lumpen, episodic structure so familiar from the design-by-committee approach to blockbusting, Pacific Rim successfully maintained a constant escalation of pitch.

The visual storytelling was superb, with fights choreographed and shot more in the manner of a sports movie than a typical effects-led effort. There was no shaky-cam, no loss of spatial awareness or sense of scale, and no confusing, staccato smash cuts to flailing metal of the kind that the Transformers movies were so rightly pilloried for.

The world of the film was relentlessly imaginative, from the way city streets were built around the skeletons of fallen monsters, to the alien skin parasites collected and sold by black marketeers, right down to the patches and insignia on the uniforms of the heroes (yes, heroes. Pacific Rim is not the kind of film that has ‘protagonists’).

Everything could have been generic and still have contributed to something that was just as marketable, but instead reeked of hours and hours of careful design. 

In fact, the only thing to give my suspension of disbelief a wobble was Burn Gorman’s demented parody of a bookish scientist. Even though his role was written with the same level of operatic lunacy as the rest of the film, it felt awkwardly off-the-shelf in a way that nothing else really did.

In general, the human drama in Pacific Rim was inevitably going to be its weak point. But everyone involved could act, and the attempt to tackle real emotion was far enough in advance of the genre standard to make it seem mean-spirited to make a meal of the issue.

Certainly, Idris Elba bellowing “we are cancelling the apocalypse” came pretty close to unpardonably silly, but after two hours of gigatonne punching, that level of cheesiness seemed genuinely necessary in a way that I can’t honestly explain after the fact.

And that is, essentially, where any conversation on this movie ends for me. I can’t necessarily explain how del Toro got away with it, nor do I feel any real need to understand why. I simply really, really enjoyed how it felt to be watching the film. As I understand it, that’s entertainment.

I am robot. A still from Pacific Rim.

By day, Fred Crawley is editor of Credit Today and Insolvency Today. By night, he reviews graphic novels for the New Statesman.

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