Note: The app’s designer, coder, all-around maker, responded in his blog. Some additional comments, responses to this post, in a later post.
I just downloaded the Pennant iPad app. While connected to the internet, this app lets you look at every play of every game of professional baseball going back to 1951. It’s gotten glowing reviews from Wired and other places with praise for its “rich interface” and all the fun they bring to stats. I was stoked to buy it — I like reading about baseball more than watching it, I loves me my data, and I was genuinely happy with the Jazz app, which has a similar visual language and navigation tropes.
Sadly, Pennant is just prettied up data, prettied up so much that it underperforms the highly evolved system of box scores and the recent and insufficiently explored sparklines of Edward Tufte. It’s also loaded with some bad usability posing as visualization.
The first problem comes with the app designers’ attachment to cover flow. I’ve never been a big fan of cover flow, finding it imprecise for task completion, and way too low in information density for exploration of anything larger than 20 items. In Pennant, it’s a real waste of space and a strange distortion of a timeline.
The colors are meaningless and confusing, the covers themselves add nothing to the exploration (might be nice to have the jerseys to differentiate between the different iterations of the Giants, or some basic information about team founding, league, or notable players — anything beyond text to justify a visual treatment). Worse, they don’t even show enough items, requiring extra work to get around. While I dislike it, at least the iTunes version previews information and provides more:
The cover flow fixation obscures the drill-downs in the experience as well. This is a screenshot of the 1981 Pirates season:
The real information is the line across the bottom of the screen, but the cover, which simply confirms the user’s choice and serves as an over-sized title, dominates. The line across the bottom of the screen is also problematic — the individual data points are hard to pinpoint, as an adult fingertip can actually touch three at a time and the finger obscures your sight line. (It’s also a repetition of the cover flow above, but with the added, and admittedly useful though inefficiently executed, depiction of the average.)
This disregard for information and usefulness is pretty much the problem with the whole app. At a brisker pace, some screenshots and critiques:
Maps are possibly the most abused data visualization techniques out there. To make room for the map, you have to shrink the actual data (team names) to pin points. And for what? Spatial relationships? For this particular data set, maps actually create confusion – if you don’t remember that a team moved, or simply want to find the team name in that cluster of points in the northeast, or you don’t really consider the renaming of the Angels as different teams in the same way you think of the Brooklyn Dodger and LA Dodgers as being different.
This is the one that most people praise. It sure looks nifty, and you need code to draw it and do the transition, but why a circle? Usually the stats are listed in proximity and tell a quick story, and are clumped in ways that tell interesting stories. Here, all you have are wedges next to each other, forcing you to spatially assess the relative stats. Worse, they’ve got cumulative stats (number of walks, hits, runs) intermixed with percentage stats (OBP and AVG). Worse than useless, this actually reduces the clarity and usefulness of the data. (The pitching one is a drag too. A standard measure is strike out to walk ratios and you don’t even have the numbers and the shapes that might make for comparison are on opposite sides of the wheel.)
The most difficult thing about the screen is that, at its core, it’s a pie graph. There is a set of wedges indicating some proportional relationship with the other items, further implying that how far out it radiates is an extra dimension. None of these implied relationships, basic knowledge for an adult reader, is delivered on, thus frustrating user expectations.
Quick hit here: the win loss line on the bottom is hard to access (see adult finger stuff above) and the representation makes the Loss look like half of a Win, not the opposite. Compare to a Tuftean style presentation:
Here, the color pops for winning and losing streaks, and you get a sense of home and away performance. And, oh yeah, you have summary data at the end of the line.
I have no idea what they were thinking with this one. And, yes, you can move the bubbles around – and get absolutely nothing out of it.
The most annoying of all. This is the meat of the experience, the play-by-play, the true fan’s recreation of the great moments in baseball. Why a circle? Is there something full-circle about the game? What happens to the already maddening smallness of the lines when you go into extra innings? The metaphor dominates at the expense of the information and the narrative.
It gets weirder when you set it to play:
The weirdness comes in a couple places. First, all of the lines from the earlier screen are made the same length when it turns into a wheel. All you have now is the number of at bats and a preview that the last out is happening. The second is that, by ticking off the plays individually, you actually lose the narrative that would come with the list. In this case, Gary Carter tripled, but there’s no context – how many men on base, how many outs, what’s the score, how long has the pitcher been in? These are the moments that make individual plays dramatic. But, by putting the display at the service of the interface metaphor, the events are reported with no context and no outcome (which a list of plays would have allowed the user to put together, and which newspaper scorecards fill in).
Enough of the hating. A quick note about how good infographics are pleasing to the eye, engage you in conversation and add to the story the data tells.
Tufte shows cleaner, higher-resolution versions in his book, but I chose the larger, jaggier one to highlight the point. This graphic shows the pattern of a teams season, it visualizes, surges and slumps, shows tight races, conveys the hopelessness of being a fan for some teams and it has data.
Pennant is mostly chartjunk. Sad, but maybe there will be other efforts, as the data isn’t exclusive.