Scouting and numbers 101

With Chezy Champs tomorrow I was looking back on some of the scouting databases from the 2022 season and this weekend’s schedule strengths chart and it got me thinking. The 2022 season was the season (my grade 12 year) I learned the most about robots with respect to numbers, like learning what OPR was, how important a schedule is, and how to measure it with a schedule strength program. As someone who is now going into mentorship and going to be the person who will be teaching students what these things are I was hoping to reach out to Chief to not only one of the most important parts of the scene but one with lots of hurdles to get into, the scouting/strategy community. I was hoping all scouting/strategy people of the first share what they use/what can be used to look at a robot’s performance. I don’t only want this to be helpful for me so feel free to post and explain some of the things I have mentioned already. I would love for this to be more than basic and really go into detail on how you use some of the resources…excel I’m looking at you. I wish the teams competing this weekend all the luck! but for now, let’s get statistical!

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Some resources/threads that you might like to investigate:

Your scouts hate scouting and your data is bad: Here’s why
Compass Alliance Scouting Development Pathway

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My thoughts about this (among other things) are detailed in Gettin’ Picky.

Some more concise advice:

  • Start with deciding what type of alliance you’d like to build and then choose metrics that will help you identify which robots can help you best put together that alliance. A metric/data point might sound cool or useful but if it doesn’t help you make a picklist or match strategy then you’re wasting your time
  • Taking average and max game pieces scored and success rates for endgame will get you 70% of the way there most years, and a line graph of performance over time is even better. I’ve tried collecting all sorts of fancy metrics but I almost always find myself focusing most on the basics
  • Know when you have to make a risk pick to win a tough match vs when to pick a consistent robot who won’t drop the ball on you, and have metrics that will help you tell which robots go in which category (high maximums are good for the former, high averages for the latter)
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I may post a more detailed post later.

But I wanted to echo @Brian_Maher. Stick to basics. The more things you scout, the easier it is for scouts to get overwhelmed and the more likely your data is subpar.

Find out what you actually want to know and scout those things. I’ve always had the philosophy of quantative concise data for scouting.

Qualitative is hard to teach younger students. We will do that, but usually a mentor with a more advanced student.

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