We have all this data available here if anyone wants to use it. Click the download button to get a local copy in csv format. We’ll run our own monte carlo analysis later today using various models and post it here, along with some regression analysis on which model gives the most accurate predictions, just for fun.
Thanks!
Wow, thanks!
Just to verify: the “Highest OPR (no co-op)” column includes auto?
I see our team is ranked last in Newton. 
Well, with 118, 1671, and 1678 in Newton, we are definitely a division to watch. All these powerful alliances are going to be looking for a match proven cheese caked can burglar (I hope).
Top alliances with a landfill stacker, a human side stacker and a can burglar are going to be fun to watch. A bonus is a can burglar who can add functionality by stacking or manipulating flipped over totes and cans, or fill in if a top seed malfunctions.
Yes it does; If you’d like to do what CVR suggested you’d take the “Highest OPR (no coop)” and subtract the auto OPR.
Can I just say, thanks to Jeremy for the simulation, 955 for the really nice applet and breakdown OPR for every team and event, and to Evan for his awesome Championship website. All this stuff is really cool to look at, and we appreciate it.
There’s an issue with this method that may skew predictions for alliances with more than one team that does a lot of co-op. Lets say:
R1 has a "platform OPR of 40, and a co-op OPR of 30
R2 has a "platform OPR of 40 and a co-op OPR of 28
R3 has OPRs of 0 for the sake of argument
By the above method, the red alliance would be predicted to score just 110 points, since we would use R1’s co-op average, but not R2’s. But if R2 can usually score 40 platform points and do co-op most of the time, surely they wouldn’t put up their 40 and then spend the rest of the match twiddling their thumbs. They would use the time they normally use on co-op to score more platform points!
(There’s also the issue of whether the opposing alliance has a high enough Co-op OPR to ensure co-op will be successful, but now we’re getting really complicated.)
Is the max coopOPR used for the same event as the Max OPR? If not I see some problems where it may appear that teams score more than they really do, if they coop more at some events, and then stacked more at other events. They would be getting the max points from both.
On the coop OPR I think you need to take a sum of the maxBlue(coopOPR,20)+maxRed(coopOPR,20) as the closest approximation and apply the total to both alliances.
And watching the regionals, the coop points increased with overall scores so that the max coop OPRs should be in an event close to the max OPR.
I think you would want to take a sum of the min(max(BluecoopOPRs),20)+min(max(RedcoopOPRs),20) , as what you wrote would just give every alliance 40 points.
I would like to get a copy of this source code before CMP if possible, so I can play with it while on the plane.
Here is the source Netbeans project that I used to run the simulations along with all of the data files I used. Thanks to Evan Forbes and his website for providing me with a source for the best OPR data.
I may rework the randomization model code tonight as the model used for simulations was a very naive version.
Feel free to PM me with any questions.
ScheduleSimulator.zip (59.8 KB)
ScheduleSimulator.zip (59.8 KB)
So, statistically speaking - it is proven that never in 10,000 years, will 254 not finish 1st in their division. I don’t think that would have happened with any other game under the exact same algorithm.
Does anyone know how to run the same algorithm, for say last year, and see how close it is to actual results?
Only if some very rough assumptions hold. It’s not guaranteed.
I think a more accurate statement would be: if their division played the qualification rounds 10,000 times, 254 would seed first in all of them.
Because of the change from W-L to average, it allows the top teams to stay high easier. 1114 in 2008 was similarly dominate (scoring 50% more then the next best team in the division), however in a W-L scenario they wouldn’t always seed first (see http://www.chiefdelphi.com/forums/showpost.php?p=735425&postcount=165)
If someone did want to run the same algorithm, the Galileo 2008 schedule is here: http://www2.usfirst.org/2008comp/Events/galileo/ScheduleQual.html and best OPRs are here: http://www.chiefdelphi.com/forums/showpost.php?p=733568&postcount=152
For kicks and giggles, here is the simulation for the Carson division rerun with the new schedule:
Division: cars
Teams found: 75
Iterations: 10000
Number AvgRank Max Min
254 1.0 1.0 1.0
1519 2.9238 12.0 2.0
225 3.2303 12.0 2.0
1325 4.6833 18.0 2.0
2085 6.234 23.0 2.0
4488 6.2526 21.0 2.0
67 7.6632 24.0 2.0
85 7.7193 26.0 2.0
1730 9.4748 28.0 2.0
5406 10.9487 32.0 2.0
5254 12.0255 38.0 3.0
4587 12.127 32.0 2.0
3478 13.2271 37.0 3.0
1296 16.084 40.0 3.0
399 17.4508 42.0 5.0
5122 17.8394 41.0 5.0
973 18.2159 41.0 4.0
16 18.674 44.0 5.0
4980 19.0676 42.0 4.0
236 22.354 45.0 6.0
1501 22.3839 45.0 7.0
3604 23.095 49.0 7.0
5338 24.7953 52.0 6.0
60 25.2075 54.0 8.0
3339 25.5461 52.0 8.0
999 27.2118 53.0 9.0
1711 27.5821 56.0 10.0
558 28.4328 62.0 9.0
203 29.5784 60.0 10.0
3547 29.9607 57.0 11.0
1629 30.3261 55.0 10.0
467 31.0024 60.0 10.0
1058 31.6473 59.0 9.0
2471 32.785 59.0 12.0
1511 33.265 66.0 12.0
1510 34.8757 64.0 11.0
2377 37.0282 61.0 13.0
246 38.782 71.0 15.0
1885 38.8871 68.0 14.0
5053 39.0219 67.0 16.0
3506 39.5788 67.0 17.0
4215 43.708 70.0 21.0
3481 44.9965 72.0 20.0
5659 45.4461 74.0 21.0
3946 46.4022 73.0 20.0
20 47.1695 75.0 23.0
2534 48.0525 73.0 22.0
2075 49.9531 74.0 18.0
173 50.0761 75.0 22.0
375 50.4154 73.0 23.0
5510 51.8697 74.0 27.0
2521 51.8814 75.0 26.0
4028 54.6548 75.0 29.0
5416 54.7641 75.0 27.0
1241 54.8783 75.0 27.0
4499 55.5049 75.0 28.0
93 57.2367 75.0 32.0
418 57.4458 75.0 31.0
2905 59.3855 75.0 35.0
5625 60.0594 75.0 30.0
1458 60.5968 75.0 35.0
4818 61.0299 75.0 34.0
5655 61.7911 75.0 35.0
5549 62.3484 75.0 37.0
4574 62.4298 75.0 34.0
2601 62.8756 75.0 36.0
5059 63.1868 75.0 36.0
5719 63.4183 75.0 38.0
3256 67.3898 75.0 41.0
1710 67.797 75.0 42.0
1306 69.3932 75.0 46.0
3880 70.5046 75.0 43.0
2283 71.0979 75.0 48.0
3728 71.4733 75.0 48.0
4953 72.5791 75.0 48.0
This was run with the same obviously naive assumptions made by my initial randomization model. I don’t have too much faith in it being terribly accurate, but it is always fun to see what it spits out.
Good luck to everyone tomorrow!
Jeremy,
Are you using excel to do this analysis or spss or something else? If something else can you lets us know? Pretty fun data thanks for sharing?