2016 match data

[ngreen]

Thanks for providing these. I had quickly developed some capabilities for scouting using the Ranking data between our first and second (last) regional.

I started directly connecting Ranking data to Tableau using a web data connector. The particular connector uses import.io, which would allow even more complex extractions. I had hoped to use this to make everything work in a standalone Tableau workbook, but time and my inexperience made me switch later to more familiar tools.

For scouting, Tableau was pretty powerful and simple to use. Direct from the Ranking data, I plotted a 2x2 graph with Defense and Goal on y-axis and Scale and Auto on x-axis. In a second worksheet, I plotted individual bar charts with team numbers on the x-axis. I liked this worksheet better because hovering over the y-axis allowed for me to sort, showing the top teams easily. I also plotted some calculated fields here, like the sum of Defense and Goal points. Filtering was really simple too, and useful to exclude teams with bad auto scores, etc.

There is some scripting in Tableau, with integration with R. Using it I ran a clustering algorithm, only using four parameters. In the 2x2 plots, I set colors based on cluster number. Here is an example: http://imgur.com/a/QDWpy . I also wrote more on this: https://goo.gl/DTnwjH

From there I wanted to instead calculate team-based scores from the alliance-based scores (basically OPR). I expect this could be done in Tableau with scripting, but I ended up doing this in Excel, building the matrix (by copy/pasting the 3 team alliances for each permutation (1&1,2&2,3&3,1&2,2&1,1&3,3&1,2&3,3&2), and then using a series of countifs to fill the matrix). And then solving Ax=b in Excel for each. I then exported a CSV and opened this in Tableau. This data was useful. Selection seems to follow it rather closely, with a little greater emphasis to goal scoring teams. So in that way it was good, although it doesn't give much tactical information and it does undervalue low goal scorers and their contribution to tower captures.

I only started looking at match data a few days before our regional. Not having the API/JSON experience, I did have a student begin to compile match data manually. However, by lunch Friday I knew we didn't have resources to even make that work. For anyone else in that predicament, here is a Google Sheet I have hastily put together today (https://docs.google.com/spreadsheets...it?usp=sharing). Altering in for other competition, mostly involves changing "code2016" to the correct name for the competition on TBA. I'd planned to use pivot tables to sum the data per team, and then exporting to Tableau. Before that I would fix team numbers (I forgot to account for numbers <1000), and clean the data (e.g. making each defense a header and the value be pts from crossing, and using only numeric or boolean data), likely with a macro.

During the offseason, I want to teach some of this to the team members, all the way from the API to the visualization. It is nice to have these files already compiled so that we'll be able to jump directly into using the data and exploring different ideas. There is a lot of potential to use this data for scouting and also for the team members to learn how to work with programming/analysis tools.