Warren,
Thanks for posting your sheets. I’m using your effective score method this year. I think it will be a great fit for this game.
What values are going to set for aquire, pass, and catch? I was thinking about 10 for acquire and 15 for catch.
Also, have you considered counting cycles? In my opinion, average contribution to a cycle plus pace of play have been overlooked by most scouting systems this year.
First, BEN35678 the spreadsheet is delayed in being posted by CD. I put in a request last night. It is in the works.
Second, D.Allred you will be able to find the values that we baselined for each item in the Effective Score equations in the ESCoefs tab of the spreadsheet, when it gets posted.
We talked about counting cycles, but cycles are alliance dependent, not individual robot dependent. We rely on our “super-scouts” to gather subjective data on teams, such as do they take a long time to pick up a ball.
Hmmm - “Super Scouts” sound like an interesting idea. Can you describe more about how you use these? Oh - and BTW - Team 3337 graciously accepts your invitation to use your scouting program. It completely lowered the stress level for our Scouting Captain - who is also a Chairman’s Award presenter
Thanks! 1523 has a minor problem where we don’t have any returning members the stands and this will help us hugely. O and thanks to mecanum phobia we are alliance captains allot.
Our strategy team is structured in a hierarchy. All SPAM members at the event (minus the drive team and pit crew) work in data collection. Our actual strategy team members act as shift managers. We sprinkle them into the shift rotations so there’s always an expert on hand to answer questions and for quality control. Our strategy team leaders are our super-scouts. They look at the data, draw conclusions, and then research why those things are true.
For example, our super-scouts might notice a team has a particularly high standard deviation of their effective score. They would then do more research to find out why that is the case. Is their effective score trending upward because they’ve been adding functionality to their robot? Was there a dip in their middle matches because something broke? Has it been fixed?
Most teams do a good job of collecting useful data. It’s the sort of problem that can be solved with sufficient manpower. What will set you apart is analyzing that data, drawing useful conclusions.
[edit]Does it normally take 2 days for a file to be approved? I can see about hosting the spreadsheet somewhere else if it takes too much longer…[/edit]
I’ve updated the Scouting Database spreadsheet. I found a hack in the Standard Deviation equation. If you have already downloaded a copy and do not wish to re-download, be sure to fix the equation. PM me and I can show you how.
Will entering a value other than 0 or 1 in the “Ball Preloaded” category cause any issues with the spreadsheet? I’m hoping to fully track teams with multi-ball autonomous modes, including the shots they miss.
We’re also implementing a similar ‘Super Scout’ system for our strategy team after testing it out last year at IRI. Over the years our students have done a great job collecting data, but when it comes down to actually talking about the teams during pre-match strategy planning and pick list meetings, they have struggled to remember much about the teams other than the data and the few comments on the scouting sheet. So, our strategy team will serve as Super Scouts and will be responsible for 10-20 teams at the event and record qualitive information (how quick is their pickup, how is their passing, where do they shoot from, where do they fit in an alliance…). The rest of the team (other than driveteam, core pit crew and chairmans) will serve as regular scouts and take shifts recording data. I think this system will give the students more owernship of our scouting/strategy while providing some accountability and better results.
The Low Goal Score coefficient is -2 to offset the +3 coefficient for acquiring a ball. You will notice that the High Goal coefficient is 7 instead of 10.
It just helps balance the books.
These coefficients were established as a result of a considerable amount of discussion among the scouting and strategy crew.
Feel free to modify these coefficients in whatever way you want. That is why we set it up this way.
Whatever your scouting and strategy team think is the right way to evaluate a single robots contribution to a match is what you should use.
The latest version of the Poor Man’s Scouting System has been added to the whitepaper.
V2.1 has some fixes to a few bugs that were found during use at the Orlando regional. Additionally, you can now input comments for a team and have it show up on the strategy sheet.
Another enhancement is the ability to input drivetrain type and number of drive motors into columns located on the Team List. this information will be copied to the Summary tab whenever you update the team averages.
Enjoy, and good luck to all teams in the coming weeks.
Especially those who use the Poor Man’s Scouting System.
