How to Win a Robotics Competition

[Ed Law]

Originally Posted by JosephC

Scouting isn't just important in Alliance selections; when your looking for the best robot to fit into your alliance, and not necessarily the best overall robot, but also in Match play.

Teams that do not scout will be going into matches blind, with no idea of what to do. Those who have collected data on robots can say, "Robot A is a really good shooter, we need to play defense on them; hey alliance partner B, since our data says your not the very best shooter would you mind playing defense on Robot A for us?"

Now for luck. Personally I believe that luck has very little to do with FRC. I've heard 5-6 teams say after a match, "Team A won because they got lucky." Unless there was a penalty that should of been called and it wasn't; then Team A used something called "Strategy". Strategy is this little thing that weaker teams use to beat stronger teams; and it isn't "Luck"

As for Quals; Teams can get lucky 2 or 3 matches and have some really good match ups; But I've never seen a bad robot be in the Top 8 after 12 matches. They may not be the best scorer, but they might be a great defensive robot.

Everything boils down to this:
FRC is 1% Luck; 40% skill and 90% Strategy.

I completely agree with you. Scouting and strategy are my passion. That's all I do at competitions. However I think you meant FRC is 0.76% Luck, 30.53% Skill and 68.71% Strategy.

[F22Rapture]

Originally Posted by edwardj

I'm not a scouting expert, but I think people tend to overcomplicate scouting with incredible amounts of data.

At a robotics tournament, the main difficulty with scouting is because there's way too much data on everyone's robot, and it's hard to process all of that data.

I agree 100%. While I'm sure this isn't true for everybody, I've always found that the majority of scouting data never really gets used to make a decision.

• Robot's technical ability to complete each of the subtasks of autonomous, scoring, defending, and endgame from 0 to 10, judged to a certain extent on Thursday to be revised later
• Overall performance from 0 to 10 from each of the 10 matches

Each of the subtasks will be weighted by importance to the game (30% for endgame, 40% scoring, etc.) and then averaged together, giving a single technical average to deal with.

The each of a team's performances from their10 matches are averaged together with the first 4 matches weighted 5%, second 2 matches weighted 10%, and the last four matches weighted 15%. This gives a single performance average to deal with.

It's very simple math, it's not much data to go through, and it can be easily done in Excel with no special scouting software. And once we've finished, it's very easy to sort, filter, and manipulate to get a good list of teams. It's perfect for smaller teams since it only takes 2-3 scouts to come up with a single performance score for each robot, and maybe a few comments to change their tech score and note their predominant strategies. And they can spend their time watching the match instead of writing.

It's not fancy, perhaps not Nate Silver levels of accuracy, but it's fast and can narrow a field of 40-80 teams down to only 10 or 15 almost instantly (with little information backlog), meaning that you can then focus your time and efforts on those teams when you start going around the pits to make connections.

[dcarr]

Originally Posted by F22Rapture

I agree 100%. While I'm sure this isn't true for everybody, I've always found that the majority of scouting data never really gets used to make a decision.

• Robot's technical ability to complete each of the subtasks of autonomous, scoring, defending, and endgame from 0 to 10, judged to a certain extent on Thursday to be revised later
• Overall performance from 0 to 10 from each of the 10 matches

Each of the subtasks will be weighted by importance to the game (30% for endgame, 40% scoring, etc.) and then averaged together, giving a single technical average to deal with.

The each of a team's performances from their10 matches are averaged together with the first 4 matches weighted 5%, second 2 matches weighted 10%, and the last four matches weighted 15%. This gives a single performance average to deal with.

It's very simple math, it's not much data to go through, and it can be easily done in Excel with no special scouting software. And once we've finished, it's very easy to sort, filter, and manipulate to get a good list of teams. It's perfect for smaller teams since it only takes 2-3 scouts to come up with a single performance score for each robot, and maybe a few comments to change their tech score and note their predominant strategies. And they can spend their time watching the match instead of writing.

It's not fancy, perhaps not Nate Silver levels of accuracy, but it's fast and can narrow a field of 40-80 teams down to only 10 or 15 almost instantly (with little information backlog), meaning that you can then focus your time and efforts on those teams when you start going around the pits to make connections.

If your final score for each team is based on its overall performance, however, this data isn't particularly useful in discerning the strengths of different teams and how they fit into an alliance.

[F22Rapture]

Originally Posted by dcarr

If your final score for each team is based on its overall performance, however, this data isn't particularly useful in discerning the strengths of different teams and how they fit into an alliance.

Well, my overall point was that you can figure out the overall strength directly after the seeding matches end, and then spend the next 2 hours working out which robots of those are most compatible with your own and making connections with those teams. It's primarily a way of focusing your effort, not doing everything for you.

[dcarr]

Originally Posted by F22Rapture

Well, my overall point was that you can figure out the overall strength directly after the seeding matches end, and then spend the next 2 hours working out which robots of those are most compatible with your own and making connections with those teams. It's primarily a way of focusing your effort, not doing everything for you.

Got it. That makes sense. You want to be choosing a robot that fits your alliance needs from a subset that has been determined to be good.

[Ed Law]

Originally Posted by F22Rapture

I agree 100%. While I'm sure this isn't true for everybody, I've always found that the majority of scouting data never really gets used to make a decision.

• Robot's technical ability to complete each of the subtasks of autonomous, scoring, defending, and endgame from 0 to 10, judged to a certain extent on Thursday to be revised later
• Overall performance from 0 to 10 from each of the 10 matches

Each of the subtasks will be weighted by importance to the game (30% for endgame, 40% scoring, etc.) and then averaged together, giving a single technical average to deal with.

The each of a team's performances from their10 matches are averaged together with the first 4 matches weighted 5%, second 2 matches weighted 10%, and the last four matches weighted 15%. This gives a single performance average to deal with.

It's very simple math, it's not much data to go through, and it can be easily done in Excel with no special scouting software. And once we've finished, it's very easy to sort, filter, and manipulate to get a good list of teams. It's perfect for smaller teams since it only takes 2-3 scouts to come up with a single performance score for each robot, and maybe a few comments to change their tech score and note their predominant strategies. And they can spend their time watching the match instead of writing.

It's not fancy, perhaps not Nate Silver levels of accuracy, but it's fast and can narrow a field of 40-80 teams down to only 10 or 15 almost instantly (with little information backlog), meaning that you can then focus your time and efforts on those teams when you start going around the pits to make connections.

While this method is an easy way to reduce the number of teams that you have to focus your scouting, you may have screened out a team that may be very beneficial to your alliance. One number cannot tell you how to find a robot that has some special attributes that you need.
That's why we collect all the data that we need. We don't scout just to keep people occupied. We use every piece of information we gathered. If there is anything we didn't use, we remove it from the scouting sheet at the next event.

[F22Rapture]

Originally Posted by Ed Law

While this method is an easy way to reduce the number of teams that you have to focus your scouting, you may have screened out a team that may be very beneficial to your alliance. One number cannot tell you how to find a robot that has some special attributes that you need.
That's why we collect all the data that we need. We don't scout just to keep people occupied. We use every piece of information we gathered. If there is anything we didn't use, we remove it from the scouting sheet at the next event.

I tend to feel like that's something that's noticed in-person though, or in a specific note taken during, but not really gleaned from the statistics. If one of our scouts noticed that X team had something special that would really fit well with our team then there's no reason why we can't take note of that.

[JosephC]

Originally Posted by F22Rapture

I tend to feel like that's something that's noticed in-person though, or in a specific note taken during, but not really gleaned from the statistics. If one of our scouts noticed that X team had something special that would really fit well with our team then there's no reason why we can't take note of that.

This season we are doing the same thing the Ed Law does; as in having 6 students scout each match for quantitative data; and we are also having two students scout each match for qualitative data; giving us the best of both worlds. This setup does require 8 students at any given time, but it is by far the best system I've seen so far.

[dcarr]

This is a scouting related question that somewhat relates to this thread: What are successful ways to motivate students to do a quality job scouting (both quantitatively and qualitatively) when they do not think their work will be used in the alliance selection process?

My answer would be: quality scouting data is essential for developing the strategy for any and every match, not just eliminations. Also, even if you are not ranked #1-8, but are within a few places of that, the possibility of getting bumped up as the top teams consolidate, and then needing to choose an alliance, is very real. In addition, when attending multiple regionals with a lot of the same teams participating, having quality data on a lot of the teams ahead of time has huge benefits.

[Golto]

We always did best when we took the emphasis off of winning the competition. The less pressure that is on the team, the better they tend to perform as a whole.

One thing to remember is that the bulk of FIRST competitions are not about the robot's performance, it has a more human touch to it.

Develop your team, get excited, have fun, and the ideas, innovation, and even the execution will flow much better. In short, take the pressure off.

[Alpha Beta]

Originally Posted by dcarr

What are successful ways to motivate students to do a quality job scouting (both quantitatively and qualitatively) when they do not think their work will be used in the alliance selection process?

Let them be fans of the game, not just their own team. My College and Pro football teams are having (had) bad years. Making penny bets with family members or creating fantasy leagues helps keep the interest up. It can work with scouting groups too.

Challenge them to make a pick-list and give bragging rights to the scout who picks the closest to the actual draft.

Once the picking is done have each scout predict the elimination bracket including number of games per match and score (Similar to an NCAA bracket) Cover dinner for the winning scout.

Consider a head scout a 5th member of the drive team. Give it some prestige and have a member of the drive team come to the stands between matches to discuss their thoughts on the upcoming match.

Keep in mind that scouting can have a long-term payoff too. When my non-graduating team members tour the pit and evaluate the success of a design they are also expanding their engineering and best practices knowledge base. We encourage team members to jot down notes and take pictures of interesting robot features. I also encourage them to try to figure out how it works before asking for a tour of the robot features.

Robustness is a key attribute, and generally one or more robots will not survive the elimination rounds. A side bet on which robot will need to be replaced first can be interesting. Rooting for a robot to break is in poor taste, but while looking for candidates you are inadvertently making a list of teams that you might be able to offer mechanical assistance to.

[Siri]

Originally Posted by dcarr

What are successful ways to motivate students to do a quality job scouting (both quantitatively and qualitatively) when they do not think their work will be used in the alliance selection process?

You watch the scouting section of Karthik's presentation.

We're also considering running an internal Fantasy FIRST League: draft your own teams, become the all-around expert on them (prior events, pit scouting and match data).

[Ed Law]

Originally Posted by dcarr

This is a scouting related question that somewhat relates to this thread: What are successful ways to motivate students to do a quality job scouting (both quantitatively and qualitatively) when they do not think their work will be used in the alliance selection process?

When students see that the data they collect are used in every match we are in and how it helped us make better decision, they will try their best to collect accurate data. Another factor that works for our team is since I am the coach and I am intimately involved in scouting data, the students will never turn in a blank piece of paper.

This year we are moving to tablets (discussed in another thread). It makes it easier to add other fun things. As part of the data collection, the students will predict the alliance selection order. We have not decided what the reward is yet for the winner. I will let the team decide.