My other favorite NFL team is the local Chicago Bears. There have been some big changes this year for the Bears, as the visualization here shows. Player are represented by individual rectangles, with the area of each rectangle being proportional to the player’s salary for that year. The players are grouped into player positions and colored similarly. Then the positions are grouped into Defense, Offense and Special Teams which are shown as three stacks of position groups from left to right. Dead money from departed players and injured reserve are shown towards the right side of each row, along with open salary cap space. Finally, all of the above are collected in to two major rows for the 2013 (top) and 2014 (bottom) season. Click on the image to see a full size version of the image with greater detail.
A few things jump out from looking at this visual.
Overall impressions: the Chicago Tribune had an article in the Opening Week Sunday edition pointing out that three recent Superbowl champs made it there with an offense ranked 8th or higher and a defense ranked 21st or lower. The Bears certainly look capable of repeating that formula this year. Last year that only got them an 8-8 record though, so it is obviously no guarantee of success. I am a bit concerned about the age of the players and potential weakness at LB and Safety, but if they can hold together with the upgraded defensive line then there is a good chance the overall defense could improve this year. I haven’t done a schedule analysis yet, but the information here is telling me that 10-6 is a good possibility this year.
Football season is finally here, and with it begins my annual hope and frustration with the Dallas Cowboys. The Cowboys had a great run back in the 90′s, but the last time they won a playoff game was in 2009 vs the Eagles and it’s been close to 20 years since they went any deeper into the playoffs. Which is not to say that it has not been entertaining following them over the recent years. There have been many thrilling comebacks and heartbreaking last minute losses. This year there have been some big changes in the defense, and the salary cap has played a larger role than in years past.
I created this visualization to provide a graphical overview of the differences. You can click on the image to see the full size version. The area of each player’s box is proportional to their salary. Similarly, the players are grouped into positions, and then into Offense/Defense/Special Teams. A few things jump out right away:
There’s my quick visual analysis of the changes for the Cowboys this season. My prediction for them this year: it could be a thrilling offense to watch, but unless some of the unproven defensive linemen can step up in a big way the could be in big trouble trying to stop opponents. As a lifelong Cowboys fan I would like to think they are a 11-5 division champion, but as a betting man I can’t give them much better than a 9-7 record with a possible wildcard spot.
[UPDATE: replaced visual with a better one having a horizontal layout and better color map]
The longer I work in the field of information visualization, the more I come to appreciate the simplicity, versatility and effectiveness of basic bar charts. They are not attention grabbing, but by encoding the information on the most powerful visual channels (size and position, with color as an option) they are remarkably accurate. They can fit anywhere from primary elements in basic infographics to small-multiples bar chart tables, and work well in different orientations and aspect ratios.
While experimenting with the paper.js graphics library recently, I used bar charts to create this date/time bar chart clock. All of the bars will fill up completely at the end of the century. This seems like such a simple idea that I am sure that someone else must have done this before, but quite a few minutes of googling and flipping through books did not turn up anything. If you are aware of another variation on this date/time clock please contact me and let me know.
I recently wrote a whitepaper for IBM on ways in which visualization can be used to effectively understand big data. Obviously, that’s a “big” topic. For this whitepaper I focused more on the complexity of big data than on the sheer scale, and on the ways in which visualization can be used to capture complex facets of the data in a “Customer 360″ scenario where an organization may be looking to get a more complete picture of various aspects of their customers including time, social networks, key social influencers and customer relations. It starts out with a simple stacked bar chart, and builds from there to increasingly complex visualization examples. The whitepaper is available @ ibm.biz/bigdatavis.
IBM just posted a quick blog post of mine on their Business Analytics blog site. The post addresses the general theme of visual navigation, comparing old school approached with external controls vs. integrated approaches that combine the visual views and navigational controls within a single metaphor. To illustrate these concepts I compare the use of traditional dashboard tabbed hierarchies with a single hierarchical visualization that shows multiple levels of detail simultaneously. Read the short article for more details.