December 8, 2008 § 1 Comment
As an avid sports fan and watcher of ESPN, I’m enthralled by ESPN’s new sportsticker wayfinding device. In the clip below, watch the rolling information at the bottom. (My apologies for the clip of a show I find annoying.) Underneath the NFL label on the bottom left-hand side, you will see a small yellow bar. As the news passes through, the bar reduces in size exactly in proportion to each bit of news. So, if there are 20 news bits, the bar reduces by 1/20th after each news bit. Anyway, take a look (the image becomes clearer around :30).
To many people, I’m sure this is no big deal. But, trust me, for sports dorks the world over, it is a big deal.
Why? I’ve been watching ESPN for about 25 years. The ticker is a mainstay on ESPN, predates the Internet, and is a common source for game updates, even with the Internet. I have flipped over to the ticker way more times than I could count (guessing, I’d say 18,000 times). Yet, it’s one of the most often overlooked experiences in the world of a sports fan. And until now, one of the most frustrating.
The reason the ticker has been so frustrating is because the time spent on each sport varies widely with no way for the viewer to know how much time would be spent on each sport. For example, if Major League Baseball is in season, but only 3 games are being played that day, then the amount of time the ticker spends on baseball is short. But, unless I follow baseball (read: “care”), I, the viewer, have no idea how long the ticker will spend reporting on baseball before moving on to, say, the NBA. 30 seconds? 2 minutes? 5 minutes? Longer?
Since I didn’t know how long the news would last, I had no sense of where I was in the flow of information I often cared less about. This made it so I either had to watch intently for an undetermined amount of time or take a chance on turning my attention elsewhere, hoping to catch it at the right moment. (Perhaps the most frustrating thing was waiting patiently, getting distracted for a few seconds, and in the process, missing your favorite team’s score that you were waiting to see.) In response, the reality is that as time went by, I would often change the channel or turn the TV off.
What is really nice about the ESPN ticker is how simply they added such useful information. They used the information structure already in place and gave it one extra feature that, like Lebowski’s rug, really ties the room together.
Consider the ticker’s underlying information structure already in place:
1. Alphabetical through different sports league title (MLB, MLS, NBA, NCAA, etc.);
2. News (about 5 seconds per bit, depending)
3. Scores (about 4 secs per game)
And now: 4. The line segment.
That simple line segment uses humans’ ability to make quick spatial judgments to give micro-level information that, in turn, guides the macro-level information. For example, if the line is moving in little chunks, based on a constant starting point of line length, we can quickly estimate how many news bits there are. And given the underlying structure of alphabetical ordering and a fairly constant time for scores and news, we can quickly estimate how much time will be spent before moving on to the next sport.
Seriously, this was the simplest little thing that I applaud for its, well, simplicity. And yes, this is completely inconsequential in the grand scheme. But it’s little things like this that show audience consideration that help make design what it is.
August 29, 2008 § 2 Comments
Last week, I did an analysis of the Olympic Medal Count, using a weighted system for the medals (Gold = 4 points, Silver = 2, Bronze = 1) and dividing it by the Top 20 medal winner’s population numbers. In that analysis, Jamaica won hands-down, only needing about 87,000 people for each medal.
This week, having taken my analysis as far as I’m going to take it (for now, anyway), I created something called the “Efficiency Ratio”. The Efficiency Ratio is how well each country did in relation to what percentage of medals they should win based on their percentage of the world’s population. In this analysis, a score of “1.00” means a country won the exact percentage of medals that they should have based on the size of their population.
Breaking it down, I calculated the Efficiency Ratio by taking the overall percentage of medals won by each country (Weighted or Total Medals) and divided it by that country’s percentage of the world’s population. For example, China won 12.64% of all Weighted Medals and has 19.83% of the world’s population, giving them a 0.64 Efficiency Ratio. The only country in the top 25 medal winners who performed more poorly than China was Brazil at 0.46. Interestingly, these were the only two countries in the top 25 that performed below an Efficiency Ratio of 1.00. The rest of the countries are overachievers, with Jamaica once again leading the way with a 36.37 Efficiency Ratio. The top 5 are rounded out by Norway (15.49), Australia (14.85), New Zealand (13.71), and Belarus (11.46). The U.S. comes in 21st at 2.59, more than four times as efficient as China, but only about half as efficient as South Korea (5.12).
One of the other interesting statistics to note is that about 40% of the world’s population won about 80% of the total medals. With China being roughly 20% of the world’s population and winning 10% of the total medals, taking them out of the equation leaves us with 20% of the world’s population winning 70% of the total medals. And if you take the U.S. out of the equation as well, then about 15% of the world’s population won about 60% of the total medals. What to conclude from this? If nothing else, the rest of the world does pretty well at the Olympics, even some of the poorer countries.
Other areas for exploration here would be GDP and other economic figures, but what would be really interesting to look at would be the total number of medals awarded not by event, but by individual. For example, instead of counting basketball as only one gold medal, it would count for 12, since it’s a team sport with 12 players. Relays in track and swimming wouldn’t be just one gold medal, but four. Honestly, I’m not sure why it’s not counted this way somewhere and who knows, it may be, but I’m done for now.
Download the spreadsheet here. (Sorry, but for some reason wordpress doesn’t allow the uploading of Excel files, so I had to save it as a pdf. Lame, I know.)
August 22, 2008 § 1 Comment
Things on my mind that I hope to explore more soon:
• Driving around Indianapolis this summer, you see a lot of highway construction going on. Those highways are funded by the gas tax, which is $0.47 per gallon (a few cents of this goes to mass transit as well). If drivers begin driving less to save fuel, keep switching from trucks and SUVs that get 15 miles per gallon to cars that get 25 mpg, and others switch to hybrids and electric/plug-in cars, where will the government make up the lost revenue to keep our highways in good shape? Will they increase the taxes on the electric utilities?
• This August in Indiana has been unseasonably cool, which brings out those “So much for global warming” snickerings. People are funny. Even President Bush admits that the climate is changing and that human activity is a factor in it. This is somewhere on par with the tobacco companies admitting that smoking is addictive and causes cancer. While there actually are subtleties in the argument for why people deny climate change, what does it hurt — really — what does it hurt to try to avoid such a potentially calamitous fate? God forbid we take care of the planet a little better.
• When I look at the medal count for the Olympics and see that the United States is leading the overall medal count but losing the gold medal battle to China, does that mean we are “winning” or “losing” the Olympics? Shouldn’t this be weighted somehow? So, let’s give this a value and see how we do. Let’s say that a Gold Medal is worth 4, Silver is worth 2, and Bronze is worth 1. (You know a gold is valued at least twice as much as a silver and four times as much as a bronze, right? Good. Glad we agree.)
Setting up a quick Excel spreadsheet, the values make the order switch around a little. Of course, China and the US are way up top, basically doubling third place Russia’s total. However, China takes over the top spot from the United States due to their gold medal count from their insane dominance of diving, men’s gymnastics, table tennis, and surprisingly, weightlifting. The United States comes in a close second.
But, what happens when we add population to the mix? After all, the Olympics seems to favor those with large populations. For the sake of time, I’ll only look at the Top 20 Total Weighted medal winners. I’ve divided their population by Total Weighted medals to establish how many people it took to create the “medal value”. This will determine the most efficient Olympic country.
And if that’s the case, the country is… Jamaica. They only needed 87,548 people per each point of their weighted medal total (2,714,000 people divided by 31 total weighted medal points). Second is Australia at 232,547 and third is Belarus at 302,813. China drops way down, needing 5,367,028 for every weighted medal, while the United States drops way down as well, needing 1,320,026 for every weighted medal.
Take a bow, Jamaica, you “won” the Olympics.
(And in case you’re wondering, they win in the non-weighted medal category as well. More analysis like this next week after the Olympics is over… )