Start Scouting
Subtitles available in English, Spanish, and Simplified Chinese
The Essential Steps to Making a Solid Strategy:
Outreach:
To collect the data we share, we use scantrons and tablets, but any system is valid if it works for you.
We also use a data verification system, which consists of pulling the data from the thebluealliance.com API and using the programming language R to create dataframes to cross check our data.
From there, we transfer the validated data to Tableau, a data visualization program, to create the graphs and charts we publish. Tableau is especially important to our data publishing system because it helps us display the information in an accessible and easy-to-read way.
Tableau is free with a student license. Although it takes a while to learn, there are tutorials that exist online. (We have one too at https://www.cpr3663.com/start-scouting).
If this seems like too much, this system is possible on a smaller scale using Excel or Google Sheets.
The end result of this process is the graphs and charts we publish. However, there are more types of graphs one could make besides the style of graph we publish.
There are many ways to apply the data we (or even you) collect. Our most common application is for our match strategies, but the data also plays a role in the alliance selection process.
We publish this data so that teams can develop a game plan for their own matches.
Our data can also be used to identify specific pieces of gameplay to focus on so that teams can win matches that were previously unwinnable and get an accurate assessment of their successes and focus-points.
We understand that not every team can devote manpower to scouting, so we developed a system for teams to scout with any number of individuals.
Qualitative:
While we’d love for scantrons and tablets to be able to capture every minute detail of a robot, some data can simply not be contained in numbers.
That’s why on top of our ‘quantitative data’, consisting of the scantrons and tablets mentioned above, we also collect ‘qualitative data’ to capture non-numerical data.
For example, while one could potentially collect a robot’s drive speed in quantitative data, it is impossible to do the same for a robot’s drive quality. That’s why we felt the need to establish an entire section to collect qualitative data; it sees past the numbers.
In practice, our qualitative data collection is nowhere as clean or advanced as our quantitative, but that’s because we have to use words, not numbers, to describe robots. All of our qualitative data is filtered in Google Sheets.
Strategy:
We trust our data so much that we use it ourselves.
For our own use, we developed a form that we call the ‘match rubric’. The match rubric condenses the data we have of the 6 robots in our match for efficient reading.
From there, the condensed data goes to our ‘match strategy sheet’, which, as the name implies, contains our strategy for the match. However, unlike our match rubric, we share the match strategy sheet to our alliance partners for effective communication on our strategy for that match.