Quote:
Originally Posted by nickyflash
This explains our situation, but for us it wasn't that people had nothing better to do than scout, but that they couldn't do anything but scout.
I'm quite interested in hearing a response from a smaller team and how they manage to make scouting entertaining. For our small team, we have a scarcity of people; consequently, people are divided into the pit crew and the stands crew with almost no breaks. In the stands, six people are necessary at all times and unfortunately we don't have enough people for a full 2-set rotation.
We're still using a paper scouting system.
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We had to take a different approach at our second regional because of small team burnout at the first. The principles were different, following a KISS approach and also recognizing some data was already available to all teams at very little time/resources cost. It really was about being more efficient and avoiding the very tedious data entry load on the handful of students in the stands.
Our stands scouting became mostly qualitative, we only had 3 sheets (each with all the teams) with 2 to write down good things and 1 for bad things robots did in matches. This limited it to 2-3 students needing to watch each match. These comments were compiled into scouting reports for the driver. It certainly wasn't the best system, but it allowed for students to have breaks, do more pit scouting, and they didn't burn out.
For quantitative scouting for a small team (or big team) learning how to gather and analyze data being made available through the FIRST API (or even potentially scouting databases from larger teams) is very promising. Start simple, then branch out. First, download Tableau and get the tabular ranking data from the event, and make some plots. Learn how to sort/filter this data. There is a lot more you can do here, especially when you can also gather match data. Of course, this data is based upon alliances rather than teams, which means it is less accurate than the scouting databases. However, it is much more fun to learn and implement something in this way, and is less reliant on having people recording data in the stands.
In retrospect, I would rather be emphasizing and teaching the higher level skills of using some programming (API/JSON) to get data, to visualize and analyze it (Tableau, Excel, etc), and strategy to the team. Some of this needs to be done even before build season (using previous years data). The students with aptitude for this I think will quickly learn the gaps and limitations, and will be more likely to put in efforts to improve it. Maybe by additional programming or by changing what is being emphasized in qualitative scouting, and having students with a little less aptitude for the data crunching doing that part of scouting.
While it is certain that students will be motivated by scouting if it is effective (and the robot is doing well), it is also is motivating to learn something and have some say in how it is done.