"We screwed up": Tim Cook on Apple Maps

Apple CEO explains what went wrong there.

In order to drum up publicity for Apple's new range of US iMacs, Tim Cook gave a long interview for Bloomberg Businessweek, in which he explained how Apple "screwed up" over Apple Maps, and what they plan to do about it:

So what are we doing? We’re putting all of our energy into making it right. And we have already had several software updates. We’ve got a huge plan to make it even better. It will get better and better over time. But it wasn’t a matter that we … decided strategy over customers. We screwed up. That’s the fact.

What were they thinking in the first place, though?

The reason we did Maps is we looked at this, and we said, “What does the customer want? What would be great for the customer?” We wanted to provide the customer turn-by-turn directions. We wanted to provide the customer voice integration. We wanted to provide the customer flyover. And so we had a list of things that we thought would be a great customer experience, and we couldn’t do it any other way than to do it ourselves.

Scott Forstall, Apple's former mobile software head also gets a mention. What went wrong there? Cook is a little evasive:

The key in the change that you’re referencing is my deep belief that collaboration is essential for innovation—and I didn’t just start believing that. I’ve always believed that. It’s always been a core belief at Apple. Steve very deeply believed this.

So the changes—it’s not a matter of going from no collaboration to collaboration. We have an enormous level of collaboration in Apple, but it’s a matter of taking it to another level. You look at what we are great at. There are many things. But the one thing we do, which I think no one else does, is integrate hardware, software, and services in such a way that most consumers begin to not differentiate anymore. They just care that the experience is fantastic.

So how do we keep doing that and keep taking it to an even higher level? You have to be an A-plus at collaboration. And so the changes that we made get us to a whole new level of collaboration. We’ve got services all in one place, and the guy that’s running that has incredible skills in services, has an incredible track record, and I’m confident will do fantastic things.

Looks like Scott Forstall wasn't so great at collaboration then.

The company have invested over $100m in their new range of iMacs. Speaking to NBC's Brian Williams, Tim Cook said that the company had been aiming for the US for some time, and that several Apple components are already manufactured there:

"The engine in [the iPhone] is made in America... but engines are made in America and are exported. The glass on this phone is made in Kentucky. We've been working for years on doing more and more in the United States."

Tim Cook. Photograph: Getty Images
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