I would generally regard “gut” data as being a tiebreaker at best. Something like “Data says these two are about the same, which do I think will work better?”
First, focus on WHY you are scouting? Are you expecting to be an alliance captain or a first pick? Do you want to use your scout data for alliance strategy or just for draft picks? Make an honest assessment of your team’s likely success and position in the competitions. Being an alliance captain demands much more than being a draft pick, but you can still bring substantial value if you have a good means of identifying a 2nd alliance member that fits well with your robot. For alliance strategy focus on what your robot does and what are the other skillsets that other robots need to have to be successful.
Once you figured that out, then focus your scouting on the information you need. Try to keep it from being overwhelming for your scouts.
And as EricH said, pull in mentors and parents. We’ve done that in the past, even having junior mentors testing out specialized scouting skills, and I’ve sat in for students when they need a break. Really make it a team effort.
First to cover a few things on this thread. Using Google spreadsheets while not at an event could be a useful tool; however, since most events will not allow you to run your own WIFI, using Google spreadsheets is not a viable option while in the stands. Secondly for the person that asked, yes Scouting apps, many teams develop their own or use ones published by other teams. There is a whole thread on this topic Scouting the Scouting apps.
Secondly what has been mostly ignored is that in order to get the best data possible there needs to be a mix of both qualitative and quantitative data. Having one or the other is a very limiting factor and you may miss a key aspect of a certain robot that makes it either a great choice, or a horrible choice. That being said if your understaffed and you have to pick one over the other go with the quantitative. Quantitative data is unbiased and irrefutable. What did a team actually did, not what can the robot potentially do, not what the team said there robot can do, quantitative data tells you exactly how the robot performed. While qualitative data/ gut scouting is by no means bad there will always be some elements of human bias in this thinking and that can lead to problems when making a pit list and selecting people on the filed.
Great points in here mainly figure out what/why you want to scout. Aside from collecting numbers what I find more important is being able to look at a field of robots and determine what type of robots we want on our alliance and then rank the robots we see on the field that would best fit those roles.
Prioritize robots that have strengths you don’t have. Pick robots who will work well with you. If all three of you need to go to the landfill to get totes you’ll all be in each other’s way. The same goes for the HP station.
The same can be said for autonomous: pick robots that have routines that work well with yours to achieve the highest score.
Keep an eye on the field for consistent performers who can perform tasks reliably from match to match. Keep an eye on teams progressing as the weekend goes on. Some teams increase their performance near the end of their rounds and can be huge dark horses in alliance selections. Find out why teams are under/over performing by talking in the pits.
Don’t just look at the top robots on the field the more important partners are the ones who will be around for the last 8 picks and can often be the most important. Last year at early events it was hard to get a robot in the third round who could quickly and consistently gain possession for a three assist cycle or consistently score a ball in autonomous but a few were out there. Most of the time teams won’t be in a position to pick the one of the top 2-4 robots at an event so don’t dwell too much on them.
Having the numbers on who scores more totes/rcs in auto and teleop is very important but don’t just focus on getting numbers because you need to examine each robot through a variety of lenses that numbers might not show. In previous years teams who had a low average score could have been slaughtered with defense in their matches while your higher average teams never had defenders on them. Defense isn’t applicable this year since alliances are separate but whenever a scout tells me “This team is really good they scored xxx on their own” I want to know if they were defended.
Scouting with small groups isn’t easy but its doable. You can throw 20 people at scouting but if they don’t understand how to analyze a field of robots all the data in the world can’t help you.
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1261 is at Palmetto (a 1st week regional) but is also sending 3 or 4 people to Perry (also a 1st week regional) who will be scouting in collaboration with team 2974 Walton Robotics. We will share data from Palmetto and they will share data from Perry, it is a win-win for both of us.
I agree that the perfect combination for scouting is a mixture of quantitative and qualitative data. However, few teams actually have a good enough scouting system to collect meaningful quantitative data, much less qualitative data. I’ve reviewed data from teams (including my own) after events, and at least half of the data sets I have seen are absolute garbage. I believe that almost all teams should focus first on improving their quantitative scouting before even thinking about qualitative scouting.
Basically, if your scouts can’t reliably count the number of totes a team can stack in a match, why would you trust that their “qualitative” assessments of teams mean anything at all?
With a small team, it may be smart to avoid collecting a large number of repeats. Have a list of the teams you would like to collect during which matches to maximize the number of teams covered. Yes, you will miss some matches, but it’s better to collect some data on every team than skip some teams that could have a vitally important mechanism.
Additionally, take pictures during your pit scouting operation. Pictures will help your scouters/drivers with recalling the specific bot, so having those available will help tremendously.
Finally, as many others have mentioned, work with other teams in the area to scout effectively. Almost every team has some sort of scouting operation, and many rookie/small teams will be understaffed. Combining resources will be like coopertition, beneficial for all simultaneously.
We’ve had our issues with bad data at during events as we have worked our way through various scouting methods: pen/paper input into Excel and our tablet scouting from last year.
That has put us in positions where we have no accurate data during an event which has been caused by technology failing or the input/collection failing. Either way I can still rely on my scouts to give me and our pick list makers a good evaluation of robots. Last year this played a critical role at two of our events and many before then where we had to say, “Data aside how do we feel about this robot from what we’ve seen”.
Like many have said in this thread you need to prioritize what you are scouting and why which is why we work hard to make sure our scouts know what to look for and why. If our data collection fails we have over half a dozen students who can offer their honest opinions regarding what they saw and work through their thoughts as a group to get a good feel for the field of robots.
I think too many teams overlook the aspect of having a serious discussion regarding scouting and why teams need to do it which is why they get poor results. If you want to be successful at an event you need to treat scouting like you do the drive team in terms of importance and not a separate group in the stands.
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BrendanB’s discussion of consistency in pick lists reminded me of this, so I’ll just leave it here.
I see where you’re coming from, but there are also a lot of different reasons for “garbage in, garbage out” scouting. Quantitative data can be really, really bad in weak scouting systems–but is it because scouts are untrained? Unmotivated? Overtaxed each match? Exhausted?
Understaffed quantitative scouting has a tendency to be simultaneously boring and utterly exhausting, while not feeling particularly useful. It takes a lot of bandwidth to count game pieces and even more to track important quantitative movements. Qualitative scouting is like that, except it can be worse. Because (as you pointed out), if you’re not well enough trained to count totes, it’s unlikely that you’re good enough with FRC to give meaningful qualitative input. That’s the usual route. But–and I’ve been down this route as well–maybe you’re understaffed but it’s with trained, experienced scouts. It can be better to use those skills and keep them engaged with qualitative discussions than to bog down some of your best strategists in tote counting when you’re not going to get good coverage anyway. It’s all resource-dependent.
By the same token, if you’re trying to make a team culture that wants to scout, selling it as “sit here and count totes” can be rather trickier than “sit here and talk to me about matches. What do you see? Was that a smart move? What will they do in match 34? What should we ask them in their pit?” I struggled for years to build up a quantitative scouting system in a very anti-scouting environment. Qualitative is sometimes an easier first step. Not always, and it’s not necessarily more effective in the short-term–in fact it probably isn’t, but very little is effective in weak scouting systems anyway. But it’s a way to fix some of the “just make up numbers” plague in match scouting.
You’re going to want both sides eventually. Scouting isn’t just about how many totes someone scores. It’s about predicting opponent’s match strategies and individual play responses. Making a pick strategy isn’t about ranking the highest scorers. It’s about strategic decision trees and adaptability. In the end, blue banners aren’t won by numbers on a page. They’re won by allies that work well together, know their opponents and can manifest their work in their scores.
Here’s one scouting app that might be useful. Look for others as well.
http://www.chiefdelphi.com/media/papers/3098
Also, “gut” comes into play for pick lists no matter the scouting system. The arguments at our draft sessions are epic and famous on our team. We’re all passionate and have great observations (just mine are better…
). Our final draft list may look little like our initial list, but we had a good starting point that we trust.
4473 Delta Prime Robotics, has also had problems recruiting scouts, for there was a lack of interest in the subject. We found that if you make it a competition between the scouts for correctness of scored matches, they would be able to work harder and longer.
I agree with EricH. Partner with another team.
This does 2 things
1> gets you the scouting data that you need, and
2> gets you to know (and hopefully become friends with) another team
We too are an understaffed team in regards to just about everything. On our good days we have 10 kids. We are looking at having 2 kids scouting at a time and rotate them. I have attached the scouting sheet I came up with that allows you to scout each side of the field with a single person. I then have a Google form we will dump the data into that will take averages and show summaries for each team.
MatchScoutingLakerBots.pdf (23.4 KB)
MatchScoutingLakerBots.pdf (23.4 KB)
We plan to do “pit” scouting up front to decide which teams to watch closely. As we’ve built a landfill-miner, we will be looking most closely at the RC specialists, and somewhat less at the chute loaders. Rather than try to track each point scored (how do you score a stack with six totes from one team and an RC from another)?, we may depend on rankings and OPR from TBA for this data. Our match scouting will focus on reliability and clumsiness issues, and identify teams with effective vs ineffective littering.
Nice layout on the form! However, you did not include any reporting of coopertition or littering. These have been responsible for more than half of the points in many of the week 1 matches. At Dallas, 38 teams had Coop + litter > Auto + totes + RCs, only 10 were lower. Admitted that “litter” total includes litter scored in an RC, but in the matches I saw, it was less common than unprocessed or even processed litter.
At Dallas, the mean score for an alliance was:
46.1 total
11.0 auto
2.5 RC
13.0 Coop
16.1 Litter
5.3 Totes
Hey you guys are local and are going to be at Mount Vernon next week. Skunkworks will be there (as will we). Skunks have a scouting app, and I know that they are doing a data sharing group with a couple of other teams that do not have the ability to scout on their own
At Auburn I’d suggest talking to NRG or 360, but I’m not so sure about getting outside help there (I dont remember any of those teams sharing scouting data in the past)
Good Luck, and I’ll see you next week 
Did someone say Skunk Works and scouting app?
I’ll spare you the entire description because (*(http://www.chiefdelphi.com/forums/showthread.php?t=135218)), but here is the down-low:
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FRCscout.com lets you share data with other teams. So you could spit scouting responsibilities between as many teams as you want at competition if you were organized enough.
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I will personally help you set up whatever you need to learn how to use Tableau (or just wait a week or two for the built-in graphs to be implemented if you don’t like doing your own analysis).
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We will be at Mt. Vernon anyway and the data is public so if you REALLY can’t figure out to scout, just use our data. My offer to help still applies.
If you PM me, I’ll help you out some more.
Oh, and 360 is also using FRCScout.com and their lead scout is totally nice. He could also hook you up.*
We only watch one side of the field (red). With each team playing 12 times, we get 5-7 views of each team which is enough to make an informed opinion - plus we can get by with only 3 scouts.
Here is the sheet we’ve been using this year. We just log each match - draw the robot path on the right, graph the stacks and times on the left, and make any other notes on the top. It worked pretty well our first event, and we’ll be using it with another team at INKOK.
Both litter throwing and Co-Op stacks will be noted down at the bottom in the comments section along with any observations. Trying to keep it simple and uncluttered for our scouts. Here in the PNW from watching the matches last week the Co-Op stacks did not come into play as often. Our first match isnt for another 2 weeks so we still have time to tweak based on the matches the next two weeks. Definitely good to hear feedback from another region and see how scoring is going there.
The purpose of scouting is to be able to pick an alliance for Eliminations.
Since Coopertition does not exist in Eliminations, there is no reason to scout it.
As I see it, the ideal alliance is: 2 stacking bots, and 1 utility bot.
One Chute stacking bot, and one Chute or Landfill stacking bot.
You know what your bot can do, so scout for the other two bots.
You also want an alliance team that can do the following in autonomous:
- Move to the Autonomous zone;
- Bring along an RC (worth more than totes, and those you want to leave off the field to keep them out of the way).
- If a team can stack the yellow totes in autonomous, that is a bonus. Don’t try for a tote set. Not worth the hassle yellow totes in the way.
For the utility bot
- Can place the RC high (level 4 to 6)
- Can move litter out of the way, but ideally to the landfill (each litter into the landfill is a 5 point swing).
Litter:
You want a person that is really good at throwing litter. If you find that person, make a note of which team they are on. Bonus points to that team.
Given the slow pace of the game, it is possible for one person to scout all 6 teams at once. You may not have all the detail, but you can assign a robot to a role, and give it a score for how well it does that role.