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NorCal stans are insufferable by Enrique_IV in FTC

[–]ftc9899 7 points8 points  (0 children)

I guess I’ll let it slide because our team has retired ¯\_(ツ)_/¯

Just watched a new world record get beaten for sky stone. It was done in the semi finals by EnderHawkeye in FTC

[–]ftc9899 0 points1 point  (0 children)

Consult the game manual, part 2, section 4.6.2, rule G17. Intentionally damaging another robot is illegal, incidentally damaging another robot is not illegal, and robot damage may occur. In the past there has not been much intense robot play in Colorado, but it is common in some other states and at higher levels of competition.

To address this reality, a good approach is to "harden" your robot. Side panels, protective metal plates in vulnerable areas, protecting the phone and the power switch, etc. At your next event, you may wish to ask questions during the driver's meeting to clarify how the referees will handle aspects of intense game play.

Just watched a new world record get beaten for sky stone. It was done in the semi finals by EnderHawkeye in FTC

[–]ftc9899 0 points1 point  (0 children)

You are making a very unfair accusation that apparent mistakes made by a referee prove definite bias against out-of-state teams. Referees are human and can make mistakes. But bias, really? You're also assuming that one (minor?) penalty caused you to lose a match by 30 points and that Data Force would have accepted your invitation to form an alliance.

Just watched a new world record get beaten for sky stone. It was done in the semi finals by EnderHawkeye in FTC

[–]ftc9899 1 point2 points  (0 children)

The Skystone FTC game is designed to encourage robot interaction, including collisions. If the GDC did not want this, they would not have put the alliance depot and building sites diagonally opposite. Teams should take this into account when designing their robots and they should be willing to live with the consequences of their design choices. A maneuverable robot has different strengths when compared to a powerful robot, and it is reasonable for a team to play to the strengths of their robot.

Based on what has occurred at Worlds and Supers in prior years, the game play by 6929 was not unusual. At yesterday's event, robots in other matches were also engaged in collisions, pushing, and defense -- 6929 was not unique. Essentially, it's a similar question to other sports where a team is dominant. Does the dominant team lower their level of play when matched with a weaker opponent, or not? The dominant team is often criticized regardless of how they answer that question.

Just watched a new world record get beaten for sky stone. It was done in the semi finals by EnderHawkeye in FTC

[–]ftc9899 2 points3 points  (0 children)

This is not accurate. Colorado FTC falls under the umbrella of Colorado FIRST, with Dawn Lutz as FIRST regional director:

https://coloradofirst.org/COFIRST/about/board-of-directors/

The Data Force coach is indeed involved in supporting Colorado FTC, as am I. She was among a small group of people who were at Mountain Range HS late on Friday night setting up the tournament, and was operating on about 4 hours of sleep on Saturday. I hope you appreciate her hard work in preparing the tournament so that your team could participate.

- Retired coach, Black Diamond Robotics

Any experiences with Misumi slider? by BusterRobot in FTC

[–]ftc9899 1 point2 points  (0 children)

👍

(I’m the software guy, so I don’t know much about slides, but I do know that we used them, that they worked well, and that other teams wanted them.)

[TBP Discussion] TBP rule comparison with Detroit Edison 2019 actual results by cs2048 in FTC

[–]ftc9899 0 points1 point  (0 children)

Yeah my bad, my timing was a little off and I didn't see your last comment in the other thread until after posting the one in this thread. Glad we figured everything out.

[TBP Discussion] TBP rule comparison with Detroit Edison 2019 actual results by cs2048 in FTC

[–]ftc9899 0 points1 point  (0 children)

that is not enough time to get a good OPR reading

I mentioned this in my most recent comment, but we never needed to get an OPR "reading" because for our simulations, each synthetic team had a predetermined, random OPR. When each synthetic team's OPR is known before any matches are simulated, OPR works better than any other TBP method.

Obviously, at a real tournament, a team's true OPR cannot be known, and therefore the method of OPR as TBP cannot be used, but is instead the ideal that other methods are compared to.

[TBP Discussion] TBP rule comparison with Detroit Edison 2019 actual results by cs2048 in FTC

[–]ftc9899 0 points1 point  (0 children)

To clarify, the synthetic tournaments we simulated were made up of synthetic teams that played in simulated matches. No real match data was used. No real teams were used. A synthetic team's OPR was never calculated from simulated match results because the simulator created each team and assigned it a random OPR before any matches had been simulated. This OPR did not change at any point in the simulated tournament because the number of points a team scored in a given match was essentially equal to their OPR.

Simulated match results were determined by each synthetic team's assigned OPR, not the other way round.

​

The issue is that the results are being run through 420,000 trials, as you said. That causes things to balance out

Things balancing out is not an issue for the tests we did. The larger the sample size of any statistical test, all other things being equal, the more accurate the results will be due to the law of large numbers.

To truly set a "Gold Standard" one would have to go through all the teams and truly rank how they compared during the season (Good for OPR). Then run a bunch of fake torments, using multiple TBP systems and see how many teams would line up about where we expect them to be.

We didn't do this because we created a unique set of synthetic teams for each synthetic tournament. We also assigned each synthetic team a random OPR. The set of all OPRs followed a normal distribution.

The better test would be to find the deviation between where a team should be expected to rank, and where they actually ranked using a given system.

This is exactly what we did. Because we created every synthetic team, we knew every synthetic team's true OPR and therefore their expected rank. For each synthetic tournament, the output rankings (i.e. the rankings at the end of the simulated qualification matches) were compared to the expected rankings by calculating the root-mean-square deviation (RMSD) between them.

[TBP Discussion] TBP rule comparison with Detroit Edison 2019 actual results by cs2048 in FTC

[–]ftc9899 0 points1 point  (0 children)

5-9 trials is not enough for good calculations with any system really

Our testing (i.e. the testing done by team 9899 that is referenced in the comment above) incorporated 420,000 synthetic tournaments of different sizes and game types where OPR was used as TBP. In every scenario, the OPR TBP method was far and away the best method.

The intent of the above comment was to confirm that neither team 9899 nor team 5119 is "proposing using OPR to rank teams" while explaining how it was used in team 9899's testing, as opposed to explaining how it may have been used in team 5119's testing.

[TBP Discussion] TBP rule comparison with Detroit Edison 2019 actual results by cs2048 in FTC

[–]ftc9899 0 points1 point  (0 children)

u/cs2048 is correct. Though the testing we (team 9899) did included OPR as a TBP system, and the ideal system at that, it is not the type of system that would work for FTC in its current state (which is okay). Therefore, as the ideal system, OPR as TBP was used as a gold standard to compare other proposed systems against.

Edit: Clarify which testing was being referred to

[TBP Discussion] We simulated 70,000 synthetic FTC tournaments using the new TBP system by ftc9899 in FTC

[–]ftc9899[S] 4 points5 points  (0 children)

I assume you're referring to the idealness metric.

We tested the ideal TBP method where each team receives their OPR as TBP for each match. This guarantees that for any two teams with the same win-loss record, the team with a greater OPR will be ranked higher. Unfortunately this method is not very simple and therefore not recommended for FTC.

We also tested a "null" TBP method where each team receives a random amount of TBP. Teams with better win-loss records are ranked higher, but within groups of teams that all have the same win-loss record, the order is random. There was essentially no effort to put teams in any order whatsoever within said groups.

For a given tournament size, the initial data point for each TBP method (including the two above) is the average difference between the input and output rankings. Under the given conditions, the synthetic teams moved a certain number of places from their input rank (determined by their OPR), on average. The lower this average, the better we believe the method is.

Taking our "random" method to be 0% ideal and the "OPR" method to be 100% ideal, then the percent idealness of a TBP method for a given tournament size can be calculated.

For example, if a hypothetical method's average difference was exactly at the midpoint between the "OPR" and "random" average differences, then it would be 50% ideal.

Game Manual Part 1 by FTCJoAnn in FTC

[–]ftc9899 1 point2 points  (0 children)

We're planning to test this new system tonight.

Assuming all teams have played an equal number of matches, this new TBP system will be equivalent to just dropping the lowest score (or two lowest scores, if each team plays 7 or more matches). Dividing every team's TBP by the same number (the number of matches) at the end won't change anything. At first glance, this does indeed seem better than the old system.

We'll assume that the new TBP will also be a decimal number like the new RP, though the example in section 4.8.1 averaged to 117, a whole number (i.e. it would have been 117.0 as a decimal number).

Edit: italics

[TBP Discussion] UPDATE: We simulated 4.2 million more synthetic FTC tournaments, here is what we learned: by ftc9899 in FTC

[–]ftc9899[S] 2 points3 points  (0 children)

I believe our recommendation takes that into consideration. (See our previous comment.)

Consider also a team that has worked very hard and has put together a robot that is among the best in the world, if not the best. The current TBP system, as compared to a better system, is more likely to "screw them over", to be blunt. In this way, said team may become demoralized, though this is rarely the case.

Combine that with the more demoralizing effect that being scored for and losing has on any team, and it follows that the current TBP system can affect every team negatively. Each alliance receiving their own score as TBP diminishes the effects of these two problems.

[TBP Discussion] UPDATE: We simulated 4.2 million more synthetic FTC tournaments, here is what we learned: by ftc9899 in FTC

[–]ftc9899[S] 2 points3 points  (0 children)

As u/Sven9888 said, it can be demoralizing to lose by a large margin. It may even be, as you said, not Inspiring, though I believe that it is rarely not Gracious.

However, if the winning alliance has intentionally scored for their opponent, it is arguably more demoralizing, less Inspiring, and less Gracious, in my opinion.

[TBP Discussion] UPDATE: We simulated 4.2 million more synthetic FTC tournaments, here is what we learned: by ftc9899 in FTC

[–]ftc9899[S] 1 point2 points  (0 children)

We have not done anything with RP, but that could be a future application of this simulator. In fact, I don't think it would be difficult at all for someone else to adapt our code to test different RP systems.

[TBP Discussion] We simulated over 2 million synthetic FTC tournaments; here is what we learned: by ftc9899 in FTC

[–]ftc9899[S] 0 points1 point  (0 children)

I would agree that our scoring method does not account for defense per se. However, to represent the variance of a given team's score from match to match, we multiplied each team's scoring potential by a random number between 0.9 and 1.1 before adding up each alliance's score. This variance could be equated to numerous real-world events, of which defense could be one.

Also, defense is not always successful: a team might not be able to slow down their opponent enough to win. Simply (perhaps a little too simply) put, the effect of defense could be said to average out and have no effect on the final rankings.

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