Calling all scouting data!

I keep meaning to put a data dump on Github, but here’s our OHCL spreadsheet. You can take a look at some of the cool stuff we did this year, and make a copy if you want to interact with it.

  1. Match data was collected through a custom digital form (technically a Google Scripts Web App built on a spreadsheet), which you can see here. Pit data was just a Google Form connected to that same spreadsheet. We did this so that we could have a custom form (see especially the second and third page’s cargo and rocket diagrams, which work great with mobile touch) that the scouters could use on their phones. I’d use team tablets if we had them though, not everybody had data available on their phones, which created (fairly minor, all things considered) headaches.
  2. Everything. We want to collect all the data we can, and then analyze what’s important based on the competition. Anything that happens in a match affects its outcome, so collecting all the data gives us much better insights into matches, and by extension, teams.
    (Edit: I should put a clarifier on this: some things that we didn’t find to be terribly important, like fouls, we relegated to a comment box. Additionally, I don’t think we’ve ever done driver skill except exceptional cases in the comment box because we’ve never found a way for it to work for our team. I think the skill is inferred largely through results anyways, and the extent which is visible on the field but isn’t reflected somehow in the data is, in my opinion, largely not worth worrying about.)
    (Also edit: We didn’t use where they put playing pieces too much, but we I think that was largely the interface’s fault. If I had incorporated that into the interfaces we used more often, I think it would have been more important.)
  3. Slightly. The only changes we had to make were a few lines of text updating the event name and settings. Mostly for fun, I ended up implementing a match prediction engine during our second competition, mostly because at that point things were running smoothly for once and so I had some time to poke around with it, which I had mostly already created during the preseason. We never really used it for anything but to look interesting though. It did provide interest for the scouters sitting in the stands, though, so that’s a benefit I suppose.
  4. As you might have inferred from my first answer, Google Sheets!
    4a. Lots of them! (Mostly good). It was easy to learn (for me, I realize not or everybody) and there’s practically no ceiling. Anything you want to do, there’s a function, add-on (like my TBA Requests), or the ability to make custom functions for. The fact that you start from scratch means that if you want to customize it, you know how to because you set it up in the first place. There’s a time investement, to be sure, but the versatility of it has really convinced me that it’s excellent for scouting. You can do custom UIs, data analysis modeling, really anything, fairly easily.
  5. This is probably the biggest weakness with our scouting at the moment. We have a meeting the evening before the last day to discuss potential candidates, where qualitative observations from mentors and scouters meet custom and simple numerical ratings to generate a pick list. I think the general concept is pretty decent, but it’s unorganized and I don’t think anybody really leaves the meeting happy and confident with the list.

I’m more than happy to field any questions!

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We use paper and record the data in to an Excel Workbook

We looked at every aspect of a robot’s performance. We couldn’t climb at the time so we looked for a robot that was decent with the game pieces, but could make a Lvl 3 climb.

We made a few editiorial changes to the data sheets: combining the abilities during the sand storm with Teleop.

We used Excel.

We made side-by-side comparisons of robots that had complementary abilities. We looked at teams that could perform the most successful cycles - focusing on teams that could place at least 6 game pieces per match (not really differentiating between hatch panels & cargo).

Check out our scouting whitepaper, it talks in-depth about every aspect of our scouting sub-team from data entry to scout analysis. I posted the link of CD should be called FRC 25- Raider Robotix scouting whitepaper 2019

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I haven’t read up on this coversation, so if I’m butting in I apolagize

I’ve run through so many different ways of scouting with my team
Three different paper styles
Two different google docs
Paper and then an excel sheet
Three different styles of google sheet

I’ve found out google sheets are working the best so far, I can link a copy of our final design, it’s still set up for last years game but I can share it if you’d all like

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That would be awesome! I appreciate any and all input I get

These all have some data in them, but these are two different types of spread sheet we have used.

This is the second try, it calculates data percentages and is packed with data.

This is my first try, doesn’t calculate anything, just nicely shows all the data.

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Unless your access is directed only to @Kaitlynmm569, could you please open access for the rest of us?
Or, I could be going about it the wrong way (which is completely possible).
Thank you

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It should be published to the web, so it should work for everyone

If it’s not please tell me

I keep getting this message: You need permission to access this published document.


Hatch Panels and balls at levels 2 and 3, also hab 3 climb

Started with everyone scouting, at DCMP I reviewed and scouted every match on my own at the end of the day with help from a few team members for a list of teams to focus on.

My head

Idk, 5 or so hours of looking at the data and matches and then somewhat arbitrarily deciding what teams I wanted to pick.

To say the least I need a better system.

It’s probably the email I’m using, I’ll have to log into a different email to fix the problem, I can fix it in less then twenty minutes

i highly recommend using the app robot scouter on amazon tablets you have to back door download it but its extremely easy to use its customization and uploads to a google spreadsheet with averages and individual team data. i suggest making a email to link all the tablets but it has been so much easier to do everything of course there is room for it to improve but it has been so simple.

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  1. electronic
  2. the main data points I remember that we made use of were pretty usual things such as cargo/hatch count, rocket 1/2/3 counts, cargo ship counts, climb counts, and per-match defense ratings
  3. once or twice, don’t remember details, probably little functional things in our app
  4. we wrote an Android application to use for scouting, each scouter would record data then send a CSV file to a master device with Bluetooth. We then analyzed the file with Tableau on someone’s laptop. Late in the year, I added a basic analysis screen so that we could take a quick look at scouting data on the fly without deep analysis
    4a. it worked great. I would recommend using a scouting app, and if you don’t want to program it, I think Robot Scouter on the play store looks great. However, I would recommend programming it for the reason that it allowed us to have complete control of the behavior, and it was a great learning experience for the programmers involved.
  5. we analyzed it in Tableau and looked for different criteria, which were a combination of general things we look for and specific things we wanted at the competition.

for some general advice, you should try to get the input of as many people as you can on the team; this way, you can find out things that they noticed that you may have not.

here’s a good paper: Your scouts hate scouting and your data is bad: Here’s why - Competition / Scouting - Chief Delphi

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Scouting Systems Survey relevant thread about how teams scout

Sorry, I’m a bit late to the party.

1.) How did your team scout? (Paper, electronic, etc)?

Our team scouts electronically with cheap Samsung and Lenovo tablets using the Robot Scouter android app.

2.) What was the main data you looked for/found most important? (Cargo, climb, etc)

We calculated the number of points robots scored during matches by tallying their in-game actions. In qualification matches, this was pretty useful for comparing the “point scoring potential” between two alliances. For example, if one team on the opposing alliance is a much better scorer than the others, we might choose to send one of our 'bots over to interfere with their scoring.

Keeping track of HAB climbs was also really helpful for our team. Our robot’s climber wasn’t the most reliable this year so we liked being able to compare climb success rates to determine which one of us would go for Level 3 and get the ranking point.

Rocket stats were another thing we looked at a lot. If our alliance partners want to and the scouting data indicates that it’s possible, we’d try and go for a rocket RP during the match.

3.) Did you change your scouting at all throughout the season?

Not anything major that I can remember. If we were able to pick more often, we probably would’ve liked to alter our scouting sheet to get more specific data on the defensive capabilities of a robot. We made a lot of changes to our scouting data briefings that were sent to our Driveteam though.

4.) What program did you use to take in and look at your data?
This was our first year using Tableau and we really like it! We export our data directly from Robot Scouter and run it through a simple command-line tool that converts it into a format Tableau can understand. The app also exports a JSON file of raw data that you can write your own application to process as well.

Here’s a link to the sheet we made for alliance selections and an example driveteam briefing. Sorry that they’re not as high quality as what other teams have made – we still have a lot to learn.

4a. Opinions on this system?
Overall everything worked pretty well for us this past season. We don’t really have the resources to program a separate data collection/analysis application every year so we like the simplicity and versatility Robot Scouter gives us. We haven’t done any substantial data analysis in the past so we have no way of comparing our current system to others but based off of this year we’ll definitely stick with Tableau in the future if it’s in the KOP again.

5.) How did you use this data to make a pick list for alliance selections?
We first create an alliance selection workbook in Tableau (see above). It’s set up so we can easily find robots that do specific things well (best scorers, hatch panel placers, cargo ship fillers, climbers, etc.). We also look at qualitative notes that scouts have made about the different robots.

Lemme know if you have any questions and I’d be happy to try and answer 'em! We’ve written a detailed introduction to our scouting process here if you want to learn a lot more about our method.

Robot Scouter was also developed by one of our former members so please feel free to ask me if you have any questions about that!

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1. Our team originally used a paper scouting system at our first event but later used a tablet scouting system (electronic).

2. The data that we found most important was whether a robot of interest could effectively place hatches, especially on the rocket.

3. We changed our scouting system during the season.
3a. We went from a paper scouting system to a tablet scouting system. At our second event, we transferred the tablet data by connecting the tablets with wires to a master computer. At States, we experimented with Bluetooth transfer but it didn’t go very smoothly and we ended up having to use wires again to transfer data. However, by Worlds, we used Bluetooth to great effect as we were able to essentially collect data at will. We did this by transferring the files from the tablet to a master computer. We had apps on both the tablets and the master computer to help with this process. We used ASTRO Bluetooth Module and ASTRO File Manager on the tablets and on the master computer, we used an app called BlueFTP.
3b. We changed it due to paper recon being very exhausting for the people who enter the data into laptops. By using electronic scouting, we were able to completely bypass this step and have data instantly.

4. We used Excel to view data.
4a. Excel was great for me since I’m the most familiar with that over something like Google Sheets or Tableau.

5. We sorted this data by descending order (average hatches placed per match) and with input from our drive coach, made a picklist for robots that we could work well with.

  1. My team used a program, called epicollect 5, for data entry on mobile phones

It’s a really simple and easy to use app, super easy survey customisation and works with IOS and Android. It also allows offline entry for later upload. I would highly recommend it

  1. The data we focused on was mostly basic. We did total hatches and cargo separately, maximum height of placement, habitat at start and finish.

  2. Epicollect 5 does some basic tabulation, and you can export it to excel easily. You can do that if you want, but if you want data to constantly be put in an excel spreadsheet or whatever program you are using, the system has pretty simple apis.
    Here’s the basic table:

Here’s the csv format: (look at the web address and you can get your program to do it by entering the name of your survey in the right spot, assuming it’s public)

From the csv format page excel was able to automatically access it and do whatever we wanted with it. Here’s a screen shot of the final table

There’s a few things that I’ve messed with a bit, but the bulk is apparent. We didn’t just rely on this, and looked at this with a combination of communication, and more qualitative data as well.

I’d highly recommend using this system, and I’m happy to help if anyone has questions about it. From my experience, epicollect 5 has the same if not better functionality than custom apps, and is way easier to use.


We mostly used paper and found that it was very inefficient. We tracked basically everything worth points, and things like if they were defended or not. We added the “was defended” box after provincials to help differenciate the data. We also redesigned the sheet after provincials to make it easier to use. We interpreted the data in Excel, which worked really well for us, since we could see the actual numbers. For picklists, we mostly looked at things like high level stuff and their Overall scoring, which was in a sheet called “organized Scouting”

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