The data I visualized for my project is both NFL Matchup Data and weather data for the cities that host NFL teams.

I’ve created a proof of concept visualization of this data. It's a scatterplot and it shows a teams average home field temperature verse home field win percentage.

Also, this earlier prototype shows only four teams win percentage but using a piechart (angles) as the channel rather than x, y position.

My next iteration allowed for switching between Viz types and different homefield win percentage calculations. This helped me decide on the final direction I would take with the vizualization.

The following tasks and questions will drive the visualization and interaction decisions for this project:

• Is there a relationship between home field temperature and home team advantage?
• Is there a relationship between home field rain/snow percipatation and home team advantage?
• Are there any other patterns in what teams have historically been very good at winning at home but not while away?

This sketch shows my plan to plot the teams according to their geological location and then show their win percentages on a pie chart. The weather would be encoded with "climate lines" that change based on the selection of temperature or percipation.

After doing the scatter plot assignment though, I think having a scatter plot in addition to the map would be helpful. The user can switch between views to compare geological regions (on the map) to comparing teams (on the scatter plot).

• I am worried there will not be a visable correlation, the scatter plot I made shows a slight correlation with temperature I think, but I am hoping that adding more configurations (like percipation or during certain months) will make the home field advantage clearer.
• I have been unable to find really good weather data. So far, I have only been able to piece the average temperature from several different sources and I haven't found average percipation by city yet either.

http://nfl-homefield-advtg-viz.s3-website.us-east-2.amazonaws.com/

• Week 7 - Start finding better weather data and incorporate into the scatter plot
• Week 8 - Implement temperature/percipation switching
• Week 9 - Add month selections
• Week 10 - Add a switch to switch between "Map View" and "Scatterplot View"
• Week 11 - Add teams onto the Map View
• Week 12 - Add "climate lines" to Map View
• Week 13 - Add a way to visualize Home Field Advantage vs How a team does when playing away
• Week 14 - Implement the Home Field Advantage vs away team advantage in some way