What the judges said about the Datafest’s ten teams

We had six amazing judges this past weekend at the Datafest. They had to make very difficult choices. Here is a collection of some of their comments about the teams that participated in the event:


“Cool to see them try to answer the question: can you buy votes? A very important question to address, very intriguing. Nice to see a model tested, even if it was simple.”

“Great questions to ask. Liked the machine learning angle, as well as the conclusion. Liked the hotspot analysis/visualization.”

“Really liked how Team Awesome undertook a real research question in such a short time. Nice combination of statistical modeling and visualizations.”

“Charts had no scale. Even if maps were not conclusive they could have been shown with a better spin. Introduction was good but should have been illustrated by charts to be really impactful.”

More information about Team Awesome here


“This group was the most altruistic in that they made a tool to help others investigate their data and share the data’s story. Very cool implementation, with good potential.”

“Super useful tool for journalists, interactive and reusable. You can imagine journalists exploring their own questions around timing of votes on controversial issues in specific states and how that affects donations. One of my favorites in its power as a tool potentially serving a broad pool of people with varied interests.”

“Liked that Team Black Cobra created a tool that could be used by journalists. The emphasis on seeing a particular day’s contributions is unique and potentially useful for generating stories.”

“Great tool. Easy, simple. Should become an app. And on top of that, they could find some nice factoids about donations.”

More information about Team Black Cobra here


“I liked the variety of approaches and visualizations that they took to the same problem.”

“Liked how the use of MicroStrategy Clound meant that new data is extremely easy to incorporate. This was a very important point, since it made the work adaptable, reusable and interactive.”

“Felt like technology took over and didn’t leave much room for data analysis.”

More information about Team Donkeys vs Elephant$ here


“Potential for the most interactive, most user friendly, most exploratory.”

“Best design and best potential to have a visually appealing, interactive and inviting app to explore the data. Great UI elements such as using faces & various visual elements to represent the data.”

“Team Eagle was the only team to focus on a consumer oriented app, which I thought was a very promising direction.”

“Should become a classic for Ipad. Too bad the proof of concept could not illustrate the details from the slide that were very appealing and clear.”
More information about Team Eagle here

TEAM FRIENEMIES (Best Overall Project)

“While it is straightforward to show a network graph, this group took us step by step through different views in that graph, showing us valuable insights and really giving us a feel for how you can explore and dig in a data set.”

“Best overall! While visualizing graphs they way they did is standard practice, what I liked was the stories and insights they found by closely looking at the data, playing with thresholds and enabling visual classification of entities with no declared affiliation. The collaborative filtering-based recommendation engine sealed the deal.”

“While several teams presented network visualizations, Frienemies was the only one to show how real insights could be derived from such an analysis. I was particularly impressed with their use of the analysis to show partisan alignment of non-partisan organizations.”

“Nice intro and story supported by a very dynamic and appropriate visualization. Nice findings. Could be useful for many people when in doubt about political network.”

More information about Team Frienemies here


“The only team that developed an interactive visualization that allows you to answer questions about the data. I particularly liked the insight that the top states donating money were not the swing states. The insight about the fact that most donations in Nevada were done by a couple.”

“Interesting anomaly detection and geographical exploration.”

“Very nice visualization to show the money donation. Very appropriate. Too bad they didn’t find a great info nugget out of it!”

More information about Team G8tor here

TEAM GOPHERS (One of the four winning teams)

“Liked the specific question about Lockheed Martin. Visualizations were very effective in conveying the insights they found. Very interesting insight that whoever is in power is the one who gets the money.”

“Liked that they’re focusing on a specific issue, which allowed them to tell a coherent story and find a memorable (and non obvious) insight.”

“Liked the notion of diving deep into one company as a way of exploring what’s possible with the data. Liked that they got to the correct -but non-obvious- conclusion that companies give primarily based on committee membership.”

“Nice start: topic positioning and results. Very clean visual. Simplicity made easy to represent ideas.”

More information about Team Gophers here

TEAM KEYSTONERS (One of the four winning teams)

“I liked the way they carefully chose the question. They chose a case study that is not as emotional or biased as other topics that they could have shown.”

“One of my favorites. Coherent story, well chosen, specific question. Interesting insight about islands being easier to flip, and interesting conclusion around how a less divisive issue was very susceptible to financial influence.”

“Liked how KeyStoners chose to focus on a single policy issue as a way of illuminating general issues. Their final conclusion, that the biggest explanatory factor in Democratic member’s switching votes was their presence in Republican states, was very strong.”

“Very nice way to set the stage. Clear process and visualization supporting a great conclusion about flip votes.”

More information about Team Keystoners here

TEAM MOST EXCELLENT (One of the four winning teams)

“Most compelling insight. Helped proved that Scott Walker is not an anomaly in Wisconsin. I liked that they are creating a mapping tool as well.”

“When working with data, often people forget to normalize it and measure percentages and relative changes. This team stood out by using *ratios* of out/influx of campaign money and visualizing their change through time. In addition to the very useful tool that others could use to explore the data, they found several stories and insights.”

“I like that Team Most Excellent set out to answer a concrete and relevant question: Is the influx of out-of-state money in Wisconsin’s governor’s race unusual? The team was able to convincingly answer that question using data and write a couple stories from it.”

“Nice process description, nice but classic visualizations. Could have been a little bit more creative around the fusion table usage.”

More information about Team Most Excellent here


“I liked the fact that they looked at the individual behaviours of donors, and how they changed over time. Great example of somebody’s behavior changing dramatically. We didn’t see that level of detail from many other groups.”

“Great tool to identify potential big donors – huge ROI for campaigns. Liked the specific examples they found.”

“Liked the integration of Influence Explorer data in Google Docs–would love to reuse that. Also liked how any of the particular individuals examined could be turned into a story for a local paper.”

“Liked the process description of what they went through. Lots of charts but not much insight.”

More information about Team Z here

More information about the Datafest here

Add Comment Add yours ↓

Your Comment