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| View Poll Results: Was this useful? | |||
| Yes, it was! It helped point out diamonds in the rough |
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109 | 70.32% |
| No, its numbers generally did not correspond to robot's actual on-field performance |
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46 | 29.68% |
| Voters: 155. You may not vote on this poll | |||
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#151
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
I have a question about Oregon for 2010. I was trying to find out opr and pm for teams at OR this year. and it failed. any ideas?
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#152
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
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Pos Reg Team OPR SAA PM RPI 1 or 997 4.44 2.997 1.443 0.2869 2 or 753 3.342 2.472 0.8707 0.2321 3 or 3165 3.214 2.257 0.9566 0.2528 4 or 1540 2.621 1.439 1.182 0.2585 5 or 3192 2.537 2.571-0.03447 0.1906 6 or 2910 2.168 0.6331 1.535 0.2883 7 or 2865 2.045 2.172 -0.1265 0.1994 8 or 948 1.979 2.207 -0.2276 0.2096 9 or 2471 1.975 1.27 0.7048 0.2358 10 or 1432 1.922 1.494 0.4277 0.213 11 or 368 1.915 2.293 -0.3781 0.2302 12 or 8 1.825 1.703 0.1226 0.271 13 or 3145 1.814 1.986 -0.1718 0.2204 14 or 957 1.791 2.599 -0.8078 0.1842 15 or 847 1.709 0.6439 1.066 0.2785 16 or 2811 1.701 0.1783 1.522 0.3023 17 or 3223 1.692 1.6860.005676 0.2277 18 or 2046 1.685 1.653 0.03189 0.1667 19 or 1983 1.631 0.7979 0.8326 0.2701 20 or 2557 1.466 0.9125 0.5539 0.3004 21 or 488 1.444 1.262 0.1814 0.2307 22 or 2733 1.373 1.67 -0.2969 0.2069 23 or 2130 1.37 1.268 0.1024 0.2588 24 or 3024 1.366 2.677 -1.312 0.1901 25 or 1595 1.33 2.216 -0.8864 0.2402 26 or 949 1.251 2.401 -1.15 0.2016 27 or 1823 1.191 1.527 -0.336 0.2294 28 or 2542 1.172 1.343 -0.1711 0.2331 29 or 1700 1.089 1.579 -0.4898 0.2202 30 or 3131 1.004 1.263 -0.259 0.1614 31 or 3210 0.938 0.4483 0.4896 0.2581 32 or 2374 0.914 0.3753 0.5387 0.255 33 or 2951 0.8851 2.232 -1.347 0.1808 34 or 1515 0.8275 0.6605 0.167 0.177 35 or 2147 0.7307 1.34 -0.6097 0.2248 36 or 2002 0.6813 0.3311 0.3502 0.2314 37 or 1510 0.6691 0.7407-0.07164 0.2381 38 or 2605 0.6604 1.997 -1.337 0.2139 39 or 1425 0.5979 1.283 -0.685 0.2014 40 or 2517 0.5943 0.2731 0.3212 0.2188 41 or 3070 0.5522 0.4322 0.12 0.2002 42 or 2898 0.5402 0.4317 0.1084 0.2305 43 or 2522 0.5236 0.7541 -0.2305 0.199 44 or 2192 0.5206 0.8281 -0.3075 0.2332 45 or 955 0.5094 0.50780.001674 0.2162 46 or 3311 0.4487 -0.1401 0.5888 0.1892 47 or 2922 0.4001 1.29 -0.8898 0.221 48 or 2411 0.3854 0.5043 -0.119 0.2797 49 or 3188 0.3425 0.7973 -0.4548 0.2466 50 or 2915 0.3389 -0.1069 0.4458 0.2668 51 or 2550 0.2831 0.6419 -0.3587 0.2233 52 or 3213 0.2749 0.5467 -0.2718 0.2069 53 or 1571 0.2742 -0.2833 0.5575 0.218 54 or 956 0.2567 0.5675 -0.3108 0.2512 55 or 2521 0.07498-0.08733 0.1623 0.2696 56 or 3600.008782 -0.4343 0.4431 0.2718 57 or 2635 -0.1431 0.3721 -0.5152 0.1756 58 or 2990 -0.1594 -0.3548 0.1954 0.2182 59 or 1778 -0.2436 0.6652 -0.9088 0.1684 60 or 3013 -0.3261 -0.2129 -0.1132 0.2016 61 or 1318 -0.7963-0.01821 -0.7781 0.2565 |
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#153
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
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#154
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
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Also, I dont want/need a Linux version, the Windows version is great. Thanks Bongle! I have a dual boot machine and the Windows boot is what I use at competitions anyway to run Windriver, Excel and save my battery. Windows is the right choice since all of the KOP software is Windows. |
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#155
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
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#156
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Did the OPR miss the mark for the Florida Regional? The alliance of 1251 + 1612 + 86 won the whole shebang with a combined OPR of 5.4 versus alliances with group OPRs of 9.9, 9.2, 7.1, 6.4, 5.6, 5.2, and 5.10. If OPR were a good predictor wouldn't the team of 1592 + 179 + 3164 be a virtual lock with a group OPR of 9.9? In the past OPR usually was a good indicator of the strongest alliance in the elimination rounds. In the Quarterfinals, the top three teams by group OPR were knocked off.
Last edited by Jacob Plicque : 16-03-2010 at 16:45. |
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#157
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
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You need to understand, OPR is just one tool to help you decide. I'll bet in qualifications, 1251, 1612 and 86 were not able to compete along side teams that would compliment their game strategies or abilities. Look at it as a perfect storm. When you bring three teams together that fully complimented each others abilities and were able to play as a single unit, their performance as an alliance would be way better than they were able to show individually during qualifications. This is why raw numbers is not always your best predictor. Observation and paying attention to all input is a scout's best approach. Leaning on one detail, like OPR, can be mis-leading. |
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#158
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
Another reason OPR might be a poor elimination predictor is because the rules essentially change in eliminations.
In qualifying, it isn't really in anyone's interest for teams to play heavy-handed defense. In eliminations, defense is a key factor. So teams with a pneumatic-tire 8-motor, rocket-powered 8WD suddenly are much more useful, while teams with highly mobile feather-light (and light on grip, like mechanum/omni) robots suddenly find it much harder to score. A perfect example is 469: they have a low OPR (well... compared to their reputation) because they are only at maximum effectiveness when they're playing with highly effective robots that can get their ball loop going. In qualifying, that might not happen often. An ball-supplier bot is limited in offensive power by its home-zone teammate that is trying to get balls in the net. 469 is a defense-proof near-perfect ball supplier. When defense ramps up and solid ball-deliverers become available, suddenly 469 is an unstoppable force. |
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#159
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
At the Oregon, New Jersey, and Pittsburgh regionals, the alliances with the best OPR were the winners. Over the years, it has been consistant that the OPR team score was a 90%ile indicator of sucess. Bongle makes a great point about the defense as the 1251+1612+86 alliance was the number 2 rated defense in the eliminations. I am curious about how many of the regional winning teams were predicted by OPR for weeks 1 & 2 in 2010. At a glance, Florida seems the exception.
![]() Last edited by Jacob Plicque : 17-03-2010 at 23:35. |
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#160
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
Also, OPR cannot take into account teams that are playing the "seeding points game" by scoring goals for their opponents. There are several very high performing teams I know of that have abysmal OPRs for this reason - because 2 or 3 times a match they were scoring for the other alliance. That's a huge hit to your OPR, SAA and ultimately your PM.
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#161
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
I think you need to take a look at all four stats to determine who the best robot is on the field.
High OPR + Unusually High SAA + mediocre PM + Strong RPI = a really good robot High OPR + Low SAA + strong PM + Strong RPI = Good Robot thats how i look at the stats. |
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#162
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I have not used RPI in the past since it has such a narrow range of data like 0.16 to 0.40. Obviously higher is better as an indicator of strength of schedule and wins. However its relationship to OPR, DPR, & PM is not easy to compare since these valus are often 20 times larger
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#163
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
I noticed this weekend that this is the year of the prediction feature. Running it for KC, the self-check indicates it would have been 70% correct after only 39 matches, and consistently 80% correct after 48 matches. For Lunacy it does much worse. This seems to indicate that this game is much more predictable, and that good robots in one match will often do well in subsequent matches. Note that this is only for predicting the winner. So although it is better at predicting the winner of a match than last year, that's a less useful thing to do than it was last year.
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After 10 matches, OPR would not have been computable After 11 matches, OPR would not have been computable After 12 matches, OPR would not have been computable After 13 matches, OPR would not have been computable After 14 matches, OPR would not have been computable After 15 matches, OPR would not have been computable After 16 matches, OPR would not have been computable After 17 matches, OPR would not have been computable After 18 matches, OPR would not have been computable After 19 matches, OPR would not have been computable With 20 matches of data, match prediction would have been 50% of the time With 21 matches of data, match prediction would have been 56% of the time With 22 matches of data, match prediction would have been 48% of the time With 23 matches of data, match prediction would have been 63% of the time With 24 matches of data, match prediction would have been 62% of the time With 25 matches of data, match prediction would have been 55% of the time With 26 matches of data, match prediction would have been 58% of the time With 27 matches of data, match prediction would have been 50% of the time With 28 matches of data, match prediction would have been 67% of the time With 29 matches of data, match prediction would have been 50% of the time With 30 matches of data, match prediction would have been 50% of the time With 31 matches of data, match prediction would have been 52% of the time With 32 matches of data, match prediction would have been 61% of the time With 33 matches of data, match prediction would have been 69% of the time With 34 matches of data, match prediction would have been 64% of the time With 35 matches of data, match prediction would have been 60% of the time With 36 matches of data, match prediction would have been 66% of the time With 37 matches of data, match prediction would have been 66% of the time With 38 matches of data, match prediction would have been 65% of the time With 39 matches of data, match prediction would have been 71% of the time With 40 matches of data, match prediction would have been 71% of the time With 41 matches of data, match prediction would have been 77% of the time With 42 matches of data, match prediction would have been 80% of the time With 43 matches of data, match prediction would have been 75% of the time With 44 matches of data, match prediction would have been 76% of the time With 45 matches of data, match prediction would have been 74% of the time With 46 matches of data, match prediction would have been 73% of the time With 47 matches of data, match prediction would have been 75% of the time With 48 matches of data, match prediction would have been 82% of the time With 49 matches of data, match prediction would have been 80% of the time With 50 matches of data, match prediction would have been 83% of the time With 51 matches of data, match prediction would have been 87% of the time With 52 matches of data, match prediction would have been 87% of the time With 53 matches of data, match prediction would have been 78% of the time With 54 matches of data, match prediction would have been 80% of the time With 55 matches of data, match prediction would have been 77% of the time With 56 matches of data, match prediction would have been 74% of the time With 57 matches of data, match prediction would have been 73% of the time With 58 matches of data, match prediction would have been 75% of the time With 59 matches of data, match prediction would have been 75% of the time With 60 matches of data, match prediction would have been 84% of the time With 61 matches of data, match prediction would have been 81% of the time With 62 matches of data, match prediction would have been 83% of the time With 63 matches of data, match prediction would have been 80% of the time With 64 matches of data, match prediction would have been 88% of the time With 65 matches of data, match prediction would have been 88% of the time With 66 matches of data, match prediction would have been 84% of the time With 67 matches of data, match prediction would have been 81% of the time With 68 matches of data, match prediction would have been 87% of the time With 69 matches of data, match prediction would have been 86% of the time With 70 matches of data, match prediction would have been 86% of the time With 71 matches of data, match prediction would have been 85% of the time With 72 matches of data, match prediction would have been 85% of the time With 73 matches of data, match prediction would have been 84% of the time With 74 matches of data, match prediction would have been 84% of the time With 75 matches of data, match prediction would have been 83% of the time With 76 matches of data, match prediction would have been 82% of the time With 77 matches of data, match prediction would have been 81% of the time With 78 matches of data, match prediction would have been 80% of the time With 79 matches of data, match prediction would have been 80% of the time With 80 matches of data, match prediction would have been 78% of the time With 81 matches of data, match prediction would have been 77% of the time With 82 matches of data, match prediction would have been 82% of the time With 83 matches of data, match prediction would have been 81% of the time With 84 matches of data, match prediction would have been 80% of the time With 85 matches of data, match prediction would have been 78% of the time With 86 matches of data, match prediction would have been 76% of the time With 87 matches of data, match prediction would have been 75% of the time With 88 matches of data, match prediction would have been 81% of the time With 89 matches of data, match prediction would have been 80% of the time With 90 matches of data, match prediction would have been 77% of the time With 91 matches of data, match prediction would have been 87% of the time With 92 matches of data, match prediction would have been 85% of the time With 93 matches of data, match prediction would have been 83% of the time With 94 matches of data, match prediction would have been 80% of the time With 95 matches of data, match prediction would have been 100% of the time With 96 matches of data, match prediction would have been 100% of the time With 97 matches of data, match prediction would have been 100% of the time With 98 matches of data, match prediction would have been 100% of the time Last edited by Bongle : 22-03-2010 at 13:03. |
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#164
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
Man this code is awful.
Anyway, v12 (based on v7) is now ready. The prediction feature now has awareness of the new seeding system, though since it can't know penalties, the predicted seeding scores are too high. Even if I gave it an entire regional of match scores with no prediction, the rankings it would give out would still be incorrect because teams would not be getting as many points as they should. |
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#165
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Re: Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting
Looks like you are missing the Ann Arbor District Event (WC is the abbreviation that FIRST uses) Last year it was the Lansing District.
-Clinton- |
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