We are currently implementing a scouting system where data is gathered on paper and then manually input by our scouting lead and a few others into a google sheets with all of the calculations. Is there any other way that other teams input paper scouting data such as document scanning, scantrons, etc?
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Not quite sure this is what youâre looking for, but might be of interest: QRScout: No code, no app, no internet, no problem
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@joeojazz spent some time the past couple of years developing a semi automatic way of doing this involving scantron-esque bubble sheets and scanning software. When I was a student in 2018, I was responsible for manually entering data into a spreadsheet. I loved it, as I am perfectly happy sitting in the stands all day doing nothing but typing in data between matches and watching all the robots, but that is not a job for the faint of heart!
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Iâd highly suggest looking into an actual scouting app over paper entries, because then the process is much more streamlined and thereâs less work inputting the data. Thereâs a lot of solutions out there that come working pretty much out of the box too, such as The Purple Warehouse and ScoutingPASS. Good luck with your competitions though, and hope the paper system works well!
Hats off to you.
Paper scouting can be very powerful, but you CANâT collect everything and you outright need your scouters to sum tally marks in a summary area.
Fundamentally if you are collecting more that 5 numeric fields with paper you are toying with failure. Cleanly written notes are a must.
Our team is running paper scouting with offline laptop aggregating this year, and (having just tested it out at the Colorado Scrimmage) I believe it is working very well. This is due to some very clever suggestions by one of our mentors.
The scouting members have clipboards and pens, with a very simple piece of paper that mostly just has a âkeyâ on it (not that itâs needed after the first match or so). A single scout watches a single robot, and writes down:
- The match number
- The robot number
- A series of single-character letters representing events that occur
- Z : robot left the starting zone
- A : scored in the amp
- L : speaker score from the left (~touching subwoofer)
- M : speaker score from the middle (~touching subwoofer)
- R : speaker score from the right (~touching subwoofer)
- F : speaker score from the far
- | : autonomous period ended
- x : robot penalized
- P : parked
- H : hanging from chain
- T : scored in trap
Thatâs it. The data string for a boring match with a broken robot looks like |, while the performance of a really-great robot might look like MzFF|ARRAFFMHT
After the match, the scout either shows their paper to the computer person, who writes in that string, or sitting nearby they just tell them the string. (Leading to some fun conversations like âLily, howâd 3648 do?â âBar mamaâ âBar Mama?â âYup. Bar mama.â aka |MAMA)
Pros:
- Works with no wifi or cellular connection. And, there are no cables or switches or batteries needed to string up an ad-hoc LAN.
- Scouts do not have to look away from the field to record. Last year, with scouts using phones/computers to enter data, we know that we missed more than a few events because they were looking at their device to enter a previous event.
- Scouts are unlikely to make a âtypoâ. With a device you can tap a wrong key or button and not realize it. With handwriting you are making glyphs that are hard to mess up. Thereâs a paper trail.
- Data entry is very quick.
- Teenagers do not have access to a distracting networked device during competition. When theyâre focused, theyâre good scouts. When they get bored, I saw scouts repeatedly watching videos or playing games during a match.
- The spreadsheet allows for immediate calculation of team performances, aggregation, graphing, etc.
Cons:
- Some studentsâ handwriting is pretty bad. Clarification is sometimes needed.
- We cannot gather hyper-accurate heatmap data for ~exactly where teams are scoring from. (Would be fun to look at; would it change anything about our strategy?)
- We chose not to gather information on misses.
- We do not have timing information; we canât do analyses on fastest cycle time, for example.
- Weâre not (currently) tracking amplification status during shots, or defensive maneuvers, or co-hangs, or spotlit status. We cannot calculate the actual score a robot contributed to a match, we can only calculate our relative scoring.
Here are some screenshots of the spreadsheet; itâs not quite in a state where I feel good about sharing it right now. We have vague plans for more strategy-focused analyses coming up. Let me know if youâre very interested in having me share this.
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We were toying with the idea of an app but we found that our scouters become distracted easily if they are using an app and engagement was higher whenever we used a paper scouting form.
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I was thinking of using a Scantron type system but didnât know how we would implement it for automatic data input into Google sheets.
This is our first year integrating heatmaps into our Viper scouting app. Our drive team is very excited to have robot starting locations for planning where our alliance partners can start and having opponentâs shot location data for planning defense.
Weâll see how much of that data they find actionable once we get through a competition or two. Iâm optimistic that having this data is going to be a game changer for us.
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That heatmap will surely be cool to look at (if someone shares their scouting data later on). Our thinking is that knowing if an alliance or âenemyâ robot scores mostly in left/right/mid/far is probably enough to do something useful. (We were originally thinking of six quantized regionsâleft/mid/right/farleft/farmid/farrightâbut chose to merge the âfarâ for data entry simplicity. Weâll see if we regret that.)
We data-collect very similarly to this. We organize a little differently. We have a physical sheet of paper for each team at the event, and all of that teamâs matches are scouted on the same sheet. When entered into the spreadsheet, itâs divided the same way, so that each team is in its own tab. You can look at the individual team tab to see trends with time, but thereâs also a summary tab with a quick snapshot of every teamâs averages.
I find that this works because when youâre reviewing scouting data, youâre looking at teams, not matches.
Interesting, thanks for sharing! I have some criticism regarding the spreadsheet, if you care to hear it. If not: no worries. 
Of course, you do what works for you.
IMHO, creating separate spreadsheet tabs for each team is a little like creating a separate database table for each product or each customer. Not a good idea, mostly because of the work it will cause you. A single spreadsheet tab is a already database that can be queried. We can use the built-in AutoFilter feature to show only rows on MatchData for a specific team; we can use the Query sheet (third image in mine) to type in a team number and see just their scores and graphs for just them; the TeamSummaries sheet automatically finds the list of unique team numbers and then calculates performance for each.
With this setup, when I import the event schedule for our first event in a week, I wonât need to add new sheets for the 40 new teams we will see. If I want to add or change calculations or graphs for per-team performance, I wonât have to repeat those changes on 60 tabs; I make the changes in one sheet, and then the same correct formulae and graphs work for whatever team I have selected.