Did the NYT fake a breakdown of an electric car?

Incongruities revealed in the logs.

Last week, the New York Times' John Broder took a Tesla Model S — the luxury electric car which its manufacturers hope will change the image of green driving forever — for a test drive.

The drive was supposed to test the new network of "Superchargers" which the company has installed along the east coast of the US. These docking stations use high-voltage DC current to charge the battery of the car in a fraction of the time it would take through mains power, and the idea is that they allow drivers to take long-distance trips which would normally be unthinkable with an electric car.

Broder planned a trip between Washington DC and Newark, Connecticut, taking in two charging stations on the way. But, he wrote, the cold weather dramatically shortened his loan-car's battery life, leading to a litany of problems and an eventual tow-truck call-out due to a flat battery:

Tesla’s chief technology officer, J B Straubel, acknowledged that the two East Coast charging stations were at the mileage limit of the Model S’s real-world range. Making matters worse, cold weather inflicts about a 10 percent range penalty, he said, and running the heater draws yet more energy. He added that some range-related software problems still needed to be sorted out.

The company initially responded to the story with regret, which Straubel telling Broder that "it’s disappointing to me when things don’t work smoothly". But Tesla also had some doubts.

As the company's chair, Elon Musk, writes:

Our highest per capita sales are in Norway, where customers drive our cars during Arctic winters in permanent midnight, and in Switzerland, high among the snowy Alps. About half of all Tesla Roadster and Model S customers drive in temperatures well below freezing in winter.

The company has had bad experiences with reviews before. Notoriously, an episode of Top Gear gave the car a favourable test drive calling it "an astonishing technical achievement", but ended with Jeremy Clarkson saying "it's just a shame that in the real world, it just doesn't seem to work" over footage of the Top Gear crew pushing the car back into the garage. When Tesla got the car back and ran diagnostic programs on it, though, they found that at no point did either of that cars used drop below 20 per cent charge. Clarkson had presented the story he wanted to tell, and the actual facts of the matter were not allowed to get in the way.

Since then, Tesla has installed tracking software on all cars loaned out to journalists, and when it checked the car used by Broder, it found discrepancies in his story.

While some were relatively minor — Broder says he set cruise control at 54mph, while the logs show the car travelled at closer to 60mph for the same period; he says he turned the heater down, the logs show he turned it up — even they are the sort of errors an experienced reporter ought not to make. But others raise questions over whether he, like Top Gear, had a story he wanted to tell regardless.

The last two incongruities Musk highlights are the most concerning:

For [Broder's] first recharge, he charged the car to 90%. During the second Supercharge, despite almost running out of energy on the prior leg, he deliberately stopped charging at 72%. On the third leg, where he claimed the car ran out of energy, he stopped charging at 28%. Despite narrowly making each leg, he charged less and less each time. Why would anyone do that?
The above helps explain a unique peculiarity at the end of the second leg of Broder’s trip. When he first reached our Milford, Connecticut Supercharger, having driven the car hard and after taking an unplanned detour through downtown Manhattan to give his brother a ride, the display said "0 miles remaining." Instead of plugging in the car, he drove in circles for over half a mile in a tiny, 100-space parking lot. When the Model S valiantly refused to die, he eventually plugged it in. On the later legs, it is clear Broder was determined not to be foiled again.

If Tesla's logs are correct, Broder didn't drive the route he said he did, didn't set the temperature to the level he said he did, and didn't drive the speed he said he did.

On the third charge, at least, Broder has a reason for only charging the battery to 28 per cent. He writes:

The Tesla people found an E.V. charging facility that Norwich Public Utilities had recently installed. Norwich, an old mill town on the Thames River, was only 11 miles away, though in the opposite direction from Milford.
After making arrangements to recharge at the Norwich station, I located the proper adapter in the trunk, plugged in and walked to the only warm place nearby, Butch’s Luncheonette and Breakfast Club, an establishment (smoking allowed) where only members can buy a cup of coffee or a plate of eggs. But the owners let me wait there while the Model S drank its juice.

Clearly sitting in a members-only establishment waiting for your car to charge is unpleasant; but even Broder admits that when he set off from Norwich, the displayed range wasn't as far as the distance he actually intended to travel. He never explains why he thought breaking down on the highway was preferable to spending a further hour in Butch's.

Before Musk published the logs, Broder gave his own pre-buttal, attempting to address what he thought the complaints might be, including the detour into Manhattan and the reason why the first charge was only to 90 per cent capacity. He did not address the reasons why the second charge was only to 72 per cent capacity, nor why he knowingly left Norwich without enough power to make it to the next charging station.

Jalopnik, looking at the story, also finds a plausible reason for why Broder may have "driven in circles" in the Milford garage, noting that:

The Milford station is on an off-ramp and it isn't at all small. A single loop around the station is nearly a 1/3rd of a mile, and if you make a wrong turn (or even hunt for the charger) and make one turn around you're at 1/2 mile.

Doubtless, we will hear something from the New York Times or Broder himself eventually. Until then, it behooves all reporters to bear in mind that sometimes, what you report on can talk back.

Photograph: Getty Images

Alex Hern is a technology reporter for the Guardian. He was formerly staff writer at the New Statesman. You should follow Alex on Twitter.

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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