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#1
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Re: Alliance Selection Strategy
I would just like to note that if you are picking a robot for the sole purpose of offense OPR is the best system you have you don't already have a scouting system. The OPR ranking has a .9 correlation with our teams actual, quantitative offensive ranking of teams (how many points each team actually scores in a match) so when you say OPR isn't accurate at telling you which robots are good offensive bots you are wrong, it is 90% accurate. For your third alliance partner you need to completely ignore OPR and seeded ranking and just look at what robots fit your strategy best, that might be a defensive robot with nothing but a drive train, or it might be a robot that can feed you disks half-court from the feeder station (if you have a aground pick-up) or it might be a bot that can climb for 30 points and stay out of your way for the rest of the match.
TL;DR: a good scouting system is irreplaceable, however OPR does tell you, with 90% accuracy, which robots score the most points consistently (this may not mean they are the best pick though). |
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#2
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Re: Alliance Selection Strategy
Quote:
Of course, Wisconsin could also be an outlier in its own right as far as OPR correlation. I noticed off hand that the regional tended to be very balanced as far as quality scoring machines were concerned. There was not any clearly dominant scoring team like 987 or 2056 at the regional, instead there were 12 or so top teams that all scored between 40 and 60 points a match on average, then the average slowly dropped down until you had to get probably 24 teams in before teams were averaging less than 20 points a match. |
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#3
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Re: Alliance Selection Strategy
OPR has been within 90% accurate at all of the last 5 events we attended (6 once nationals rolls around) so if you don't have a quantitative scouting system to track every team OPR is your best bet.
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#4
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Re: Alliance Selection Strategy
Don't count out rookies, they can surprise you.
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#5
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Re: Alliance Selection Strategy
What data did you use to determine it was 90% accurate?
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#6
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Re: Alliance Selection Strategy
I have the same gripe. It sounds like they have found a correlation coefficient of 0.9 between the explanatory variable (OPR) and the dependent variable (Avg. Score). In this case it is poor interpretation to say that this is 90% accurate. However, due to an R^2 value of 0.81, it would be acceptable to say that 81% of the variation in Avg. Score can be explained by variation in OPR. Saying it is 90% accurate based on a correlation coefficient of 0.9 is poor analysis of a statistical regression (it sounds like this is the mathematical tool being used).
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#7
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Re: Alliance Selection Strategy
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The other thing that surprised me about the Boilermaker OPR is that there were a couple of cases that demonstrated accuracy. There were 2 single purpose robots in particular - one that hung for 30 points and one that hung for 10 points. OPR after Friday was 29 and 11 respectively. Anecdotal evidence I'm sure, but it seems that this year's game is easy to decompose (even easier than last year). OPR might be good at identifying top pick candidates, but nothing beats old fashioned scouting. My advice is to write down 5-10 attributes you think make a robot "good" for the game. This year, accuracy, distance, as well as how quickly can they hang, and floor pickup are good attributes. Attributes that are pretty universal between games describe robot drive trains, like speed and pushing power. The next step is to find 3 or 6 dedicated students to watch robots from the stands. These students need to focus on a single robot during each match, writing down as much detail as they possibly can. The last thing you might consider is writing down robot *features*. When I was watching matches this year, whenever I saw a battery move within a robot, I advised the scouters to note it. It's a hard lesson to learn, but it sunk my team and our alliance during eliminations in 2006 when our battery wasn't secured properly and knocked open our pneumatic release valve, and disabled the robot through the remainder of the match. There are other things like this that are easy to spot (bumpers dragging on the floor), that can potentially draw lots of fouls or a disabled robot. Hope this helps your scouting effort! |
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#8
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Re: Alliance Selection Strategy
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#9
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Re: Alliance Selection Strategy
Collecting and compiling statistics on individual team contribution can be very helpful. This is the type of system that CORE uses for data collection and it is very labor intensive.
Scouting is a lot like building in that you need to consider the capability of your team in your planning. For smaller/newer teams I would suggest some kind of partnership with a larger and/or more established team. You can get some data from them and then work on your own strategy using their numbers. Otherwise, OPR or FRCminer are good sources of impartial data even if they are not 100% representative of the teams individual capabilities. I hope the OP had a good outcome at Bayou and I would encourage all teams to keep practicing and growing your scout-egy capabilities. If you wait until you are going to need the data you probably will not get up to speed in time. -mister g |
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#10
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Re: Alliance Selection Strategy
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"90% accuracy" was probably the wrong phrase for that. To elaborate, OPR ranking had a .9 correlation coefficient with robots actual offensive performance. The main point that I've been trying to get across is that a good scouting system is irreplaceable, but if you don't have one, OPR is much better than having nothing. I think we can all agree on that. |
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#11
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Re: Alliance Selection Strategy
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Our crude 1-sheet-of-paper-per-team scouting during the AZ regional worked fine, and we we were also able to get corroborating scouting data from a friendly team. |
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