An email regarding the last post, answering some of the questions about success and popularity of management games. Andrew Walkingshaw writes:

The short answer to this is “yes” – football (soccer) management games are a very big deal here in Europe. One of the major developers is Sports Interactive, (or at Wikipedia) with their Championship Manager/Football Manager series: they’ve been going over fifteen years now.

And apparently the games have even been popular since the early 80s. I found this bit especially interesting:

Fantasy soccer doesn’t really work – the game can’t really be quantified in the way NFL football or baseball can – so it could be that these games’ popularity comes from filling the same niche as rotisserie baseball does on your side of the Atlantic.

Which suggests a more universal draw to the numbers game or statistics competition that gives rise to fantasy/rotisserie leagues. The association with sports teams gives it broader appeal, but at its most basic, it’s just sports as a random number generator.

Some further digging yesterday also turned up Baseball Mogul 2008 (and the 2009 Edition). The interface seems closer to a bad financial services app (bad in this case just means poorly designed, click the image above for a screenshot), which is the opposite direction of what I’m interested in, but at least gives us another example. Although this one also seems to have reviewed better than the game from the previous post.

Via News.com, the peculiar story of MLB Front Office Manager, a sports simulation game in which you play the general manager of a major league baseball team. Daniel Terdiman writes:

The new game — which is unlike any baseball video game I’ve ever seen — has perhaps the perfect pitchman, Oakland A’s General Manager Billy Beane. For those not familiar with him, the game probably won’t mean much, since as the main subject of Michael Lewis’ hit book, Moneyball, Beane has long been considered the most cerebral and efficient guy putting contending baseball teams on the field.

This caught my eye because of its focus on the numbers, and how you’d pull that off in the context of a console game.

A “first look” review from GameSpot notes:

As you may imagine, FOM’s interface is menu heavy, providing access to the various statistical metrics and trends to keep you apprised as general manager. What is surprising is that FOM manages to bring this depth to the console as well as the PC. While other console-based franchise management titles have struggled to create effective navigation tools, FOB’s vertical menu interface is both clean and intuitive without compromising the depth one would expect from a game in this genre. Top-level categories include submenus (many of which include further submenus) similar to navigating a sports Web site.

Other reviews seem to be less charitable, but I’m less interested in the game itself than the curiosity that it exists in the first place. GameSpot describes the audience:

By 2K’s own admission, the game targets a specific niche: the roughly 3.5 million participants of Fantasy Baseball leagues. It is 2K’s hope that this hardcore baseball audience, many of whom spend two to three hours every day managing their fantasy rosters, will see FOM as a convenient alternative (or even a complement, assuming those individuals forgo sleep).

So it’s a niche, as would be expected. But I’m curious about a handful of issues, a combination of not knowing much about gaming, mixed with a fascination for what gaming means for interfaces:

• Could this be done properly, to a point where a game like this is a wider success? The niche audience is interesting at first, but is it possible to take a numbers game to a broader audience than that?
• Has anyone already had success doing that?
• Are there methods for showing complex numbers, data, and stats that have been used in (particularly console) games that are more effective than typical information dashboards used by, say, corporations?

The combination of having a motivated user who is willing to put up with the numbers suggests that some really interesting things could be done. And because the interface has to be optimized for the limited interaction afforded by a handheld controller (if played on a console) suggests that the implementation would also need to be clever.

If you have any insight, please drop me a line. Or you can continue to speculate for yourself while enjoying the promotional video below with the most fantastically awful background music I’ve heard since Microsoft Songsmith appeared a little while ago.

Computers are really good at repetitive work. You can ask a computer to multiply two numbers together seven billion times and not only will it not complain, it’ll probably have seven billion answers for you a few seconds later. Ask a person to do the same thing and they’ll either walk away at the outset, realizing the ridiculousness of the task, or they’ll get through the first few tries and lose interest. But even the fact that a human can recognize the ridiculousness of the task is important. Humans are good at lots of things—like identifying a face in a crowd—that cannot be addressed by computation with the same level of accuracy.

Visualization is about the interface between what humans are good at, and what computers are good at. First, the computer can crunch all seven billion numbers, then present the results in a way that we can use our own perceptual skills to identify what’s important or interesting. (This is also why the design of a visualization is a fundamentally human task, and not something to be left to automation.)

This is also the subject of Luis von Ahn’s work at Carnegie Mellon. You’re probably familiar with CAPTCHA images—usually wavy numbers and letters that you have to discern when signing up for a webmail account or buying tickets from Ticketmaster. The acronym stands for “Completely Automated Public Turing Test to Tell Computers and Humans Apart,” a clever mouthful referring to Alan Turing’s work in discerning man or machine. (I encourage you to read about them, but this is already getting long so I won’t get into it here.)

More interesting than CAPTCHA, however, is the whole notion that’s behind it: that it’s an example of relying on humans to do what they’re best at, though it’s a task that’s difficult for computers. (Sure, in recent weeks, people have actually found ways to “break” CAPTCHAs in specific cases, but that’s not important here.) For instance, the work was extended to the Google Image Labeler, described as follows:

You’ll be randomly paired with a partner who’s online and using the feature. Over a two-minute period, you and your partner will:

• View the same set of images.
• Provide as many labels as possible to describe each image you see.
• Receive points when your label matches your partner’s label. The number of points will depend on how specific your label is.
• See more images until time runs out.

Prior to this, most image labeling systems had to do with getting volunteers to name or tag images individually. As you can imagine, the quality of tags suffer considerably because of everything from differences in how people perceive or describe what they see, to individuals who try to be a little too clever in choosing tags. With the Image Labeler game, that’s turned around backwards, where there is a motivation to use tags that match the other person, thus minimizing the previous problems. (It’s “Mechanical Turk” meets “Family Feud”.) They’ve also applied the same ideas to scanning books—where fragments of text that cannot be recognized by software are instead checked by multiple people.

More recently, von Ahn’s group has expanded these ideas in Games With A Purpose, a site that addresses these “casual games” more directly. The new site is covered in this New Scientist article, which offers additional tidbits (perspective? background? couldn’t think of the right word).

You can also watch Luis’ Google Tech Talk about Human Computation, which if I’m not mistaken, led to the Image Labeler project.

(We met Luis a couple times while at CMU and watched the Superbowl with his awesome fiancée Laura, cheering on her hometown Chicago Bears against those villainous Colts. We were happy when he received a MacArthur Fellowship for his work—just the sort of person you’d like to get such an award that highlights people who often don’t quite fit in their field.)

Returning to the earlier argument, algorithms to identify a face in a crowd are certainly improving. But without a significant breakthrough, their usefulness will be significantly limited. One commonly hyped use for such systems is airport security. Bruce Schneier explains the problem:

Suppose this magically effective face-recognition software is 99.99 percent accurate. That is, if someone is a terrorist, there is a 99.99 percent chance that the software indicates “terrorist,” and if someone is not a terrorist, there is a 99.99 percent chance that the software indicates “non-terrorist.” Assume that one in ten million flyers, on average, is a terrorist. Is the software any good?

No. The software will generate 1000 false alarms for every one real terrorist. And every false alarm still means that all the security people go through all of their security procedures. Because the population of non-terrorists is so much larger than the number of terrorists, the test is useless. This result is counterintuitive and surprising, but it is correct. The false alarms in this kind of system render it mostly useless. It’s “The Boy Who Cried Wolf” increased 1000-fold.

Given the number of travelers at Boston Logan in 2006, that would be two “terrorists” identified per day. (And with Schneier’s one in ten million is a terrorist figure, that would be two or three terrorists per year…clearly too generous, which makes the face detection accuracy even worse than how he describes it.) I find myself thinking about the 99.99% accuracy number as I stare at the back of heads lined up at the airport security checkpoint—itself a human problem, not a computational problem.