Re: The Plus-Minus stat in Lunacy
 

As super cells count 7.5 times as much as moon rocks, scouting could be simplified by just reporting empty cells upgraded and super cells scored. Super cells will be match deciders when it comes to the elimination rounds.

Re: The Plus-Minus stat in Lunacy
 

Indeed, one would be hard pressed to find a better practicable method of grading the Lunacy bots. OPR-type methods may or may not be as useful this year. Personally, I like the +/- method myself.

Notice the last sentence. Robots of Lunacy is the operative term there. This method would seemingly take no measure of scoring done by humans players. In this year's game, the scoring/shooting capabilities of a team's HP may turn out to almost as important as those of the robot.

One possible way to account for this is to have the scouts track one trailer per person. Then, they could count the number of balls/points scored by each source. Putting the six sheets together should give plenty of data on which to base any metrics.

Re: The Plus-Minus stat in Lunacy
 

I disagree. This year, there aren't even things like penalties, bonuses, multipliers, or autonomous to muck things up. Your score is simply how many balls are in your opponent's trailers, which means it should be fairly easy to calculate in an OPR-like style how many balls your team (on average) puts in your opponent's trailers. The best part is that you can (probably) run the opposite, so you can also find how many balls your team receives in its trailers and have that be a useful statistic to figure out which teams are better at avoiding getting scored on. Assuming that it does work both forwards and backwards, that means you can approximate +/- data this year, which you typically haven't been able to in the past.

Very little beats having people sit in stands tabulating data, but as a proxy (or addition) to scouting, OPR will be quite useful this year.

The stuff that really screws up OPR is exponential scoring or other non-linear scoring styles, because with exponential scoring, you end up with nearly meaningless results. Super cells might throw a small wrench into things, but I don't think they will, seeing as they are essentially equivalent to scoring 7.5 moon rocks.

Anyway, we'll see when the first set of scores comes out if an OPR ranking compares nicely with observed data.

Re: The Plus-Minus stat in Lunacy
 

I ran through a list of the rules with penalties... there's quite a few. The good news? They shouldn't be called very often and they're mostly just 10-pointers. The bad news? They're still there.

This type of ranking system could be very interesting to implement/calculate.

Re: The Plus-Minus stat in Lunacy
 

I think +/- is a great stat since it show how good a team is at scoring and avoiding be scored upon. It is very important to take HP into account because their impact could be significant. The problem comes in trying to find 12 scouts (1 for each robot and 1 for each HP) who will be able to consistently be able to show up to matches (especially for small teams). But this could be one of the best scouting methods because it separates it's own score from it's alliance score.

Re: The Plus-Minus stat in Lunacy
 

Good thoughts, people, and keep it coming!

I agree that the human player is a huge factor this year, and that they need to be scouted as well. Unfortunately there is no way that I can think of to track everything going on without a large number of scouts per round. That said, I think that scoring will be much slower and more controlled in general than people think (I think a good robot this season will score maybe a dozen balls in a round).

As far as special cases go (penalties, miscounting, etc.), I wouldn't get too hung up on them. We acknowledge that statistics have inherent error, and that they only capture a small subset of the "game experience". While we're at it, what about robot failure? I've seen great teams throw a chain early in a match and sit idly by. It happens. This could destroy any sort of averaging stat (especially this year).

All statistics are necessarily incomplete for any nontrivial challenge. Not all RBIs are equal in baseball. Not all touchdowns are equal in the NFL. Not all goals are equal in hockey. But stats are easier to express colloquially. They are meant to supplement our understanding of team performance - NOT be the bottom line.

Re: The Plus-Minus stat in Lunacy
 

Wow, I just posted a +/- stat as a scouting basis in another thread, before I noticed this one.

Is +/- perfect? No, absolutely not? Is Blake Wheeler the best player in the NHL? Hell no! Is Alexander Ovechkin 2.4x better than Sidney Crosby? Yes, but that's a different story. :cool:

But the core idea is what actually matters here. Four stats should be tracked to get an ideal measurement (one of which can be done after the match by looking at scores), "team" +/-, "robot" +/-, alliance +/-, human player balls scored. Potentially you can even have a super cell +/- category for each of those if you wanted.
Additionally each of these stats should be tracked for the event and each individual match.

Robot +/- is how many balls the robot scored against how many are in it's trailer. Team +/- is the robot +/- with the human player balls scored (by their HP) added to the "+" column.
Alliance +/- for an individual match would simply be the score (before penalties, possibly removing super cells if you wished). For an event you may replace this with an "OPR/DPR" style calculation if you prefer.

The ideal tracking solution would be 12 scouts per match. One scout is tracking each robot to see how many balls it scores. At the end of the match they note how many balls were scored in the trailer. That scout also notes some basic traits of how the match was played (robot was pinned, played defensively, etc.).
One scout is also assigned to each human player to track how many balls they score (accuracy can also be noted if you wish). General notes here also apply (throwing into field vs. "feeding" robots).

Both the average +/- and the individual match +/- are going to be very important data. The average should (theoretically) give a fairly imbiased estimation of which robots are best against the field of competitors. The individual match one will show outliers. What happened in those outliers is quite possibly the most important data of all.
Find what went right and what went wrong (based on the quick notes taken) and you ca figure out which teams are reliable, what strategies work for/against them, and what types of robots they match up well against.
That data is going to be a quicker and less subjective method of estimating the abilities of teams.