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Offensive Power Rankings for 2008
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Inspired by a post by sw293 in one of my long-past stats threads from 2006, I re-implemented his/her Offensive Power Rankings algorithm. Essentially, it tries to determine approximately how much each team, on average, contributed to their alliance.
You can see the math behind it detailed here: http://www.chiefdelphi.com/forums/sh...0&postcount=19. In essence, it looks at a team's accumulated score over the course of a weekend as a sum of the offensive power ratings of all the other teams, multiplied by the number of times that team was paired with each team. Example: 1565's first match at GTR was with 2198 and 1870, and they played 8 matches total. The '...' would be all their other alliance partners through qualifying, multiplied each time by how many times they were allied with them. TotalScore(1565) = OPR(1565)*8 + OPR(2198)*1 + OPR(1870)*1 + ... Once you arrange the scores of all the teams from a regional in this way, then you end up with N equations (one big sum of scores per team) and N unknowns (the offensive power ratings). This can be solved to find out the OPRs. Without further ado, here they are for GTR. I'll be doing other regionals in the very near future. |
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These power rankings were a great help in 2006. (very accurate and very predictive) Of course they did not predict the exact outcome of matches, but it certainly let a team know what they were up against and who they were partnered with. It was a big part of our Championship scouting routine in 2006.
Thanks for offering to do this! |
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The penalties are such a huge factor this year, unless you find a way to account for them, this formula won't accurately reflect a teams offensive ability.
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Another interesting thing is that a team that constantly gets in the way of it's alliance parters would have a lower OPR then one that scores the same number of points but allows it's alliance partners to live to their full potential. |
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are thier going to be ones for every regional? or would that be an endeavour of the individual teams to do?
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Just finished the complete list. This includes every single 2nd-week and later regional. The first-week regionals have slightly different formatting for match results, and I couldn't include them easily. At least based on this analysis, we finally have an answer for the question: "Is anyone CLOSE to 1114?". The answer: no.
The top 50: Code:
1114 79.0239 |
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I don't see entries for any teams I remember from Microsoft-Seattle Regional (Week 4). :confused:
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If you can't factor out alliance penalties then there is way too much "luck of the draw" possible in this chart. |
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In fact, I can think of a way I should be able to get the week 1 regionals working as well. Check this thread again in 10 minutes, I should have an updated one up. |
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If you let me know a precise format you want, I can give you a huge CSV dump into the formatting you want from The Blue Alliance's database.
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I would also like to see standings of all teams ranked based on their record at the attended official events (Win-Loss-Tie). :)
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So including week 1, the top 50 now looks like: Code:
1114 68.5388 |
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The CSV is too big to attach. Find it here: http://www.thebluealliance.net/cdvid...30_matches.csv
Let me know if that works for you :) |
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Okay I somewhat understand how this is all calculated but I am guessing that somebody has already made an excel file to calculate all this? I tried to download one from '06 but the link was broken so can somebody please upload a new one? Thanks.
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Could you help me make sure I understand the linear algebra involved?
I understand that p is what we're looking to find, or the average number of points a team contributes per match. s and M we can calculate from the results coming from each regional. Since Mp = s, we can conclude that p = (M-1)s (where M-1 denotes M inverse). It seems to me from thinking about it that M is symmetric, and as such M = M-1. Is that actually the case? I'll probably try to write a program to take a csv dump and spit out rankings if I understand the math. |
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I don't think the inverse of a symmetric matrix is necessarily symmetric. The transpose of a symmetric matrix M would be equal to its non-transpose, but I don't think that carries for symmetry. To prove that M-1 is not necessarily equal to M for a symmetric matrix, just think of the symmetric matrix 2I, where I is the identity matrix. If (M)-1 = (M) for symmetric matrices like you are proposing, and we know MM-1 = I for any matrix and its inverse, then (2I)(2I) must equal I, but this is not the case. I didn't have to write the matrix solver myself, I used a library I found online. If there are any tricks it used, I'm not aware of them. |
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You're right. For some reason, I was thinking of the transpose rather than the inverse. I'll see how I handle the inversion of the matrix when I get there. In the meantime, I'm working through the easy stuff: reading the csv and making and populating s and M.
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Symmetry means that M = M transpose (flipped along the identity axis), which is different than M inverse. It's easy to confuse the two (I remember doing that plenty of times, thank Ms. Martin). Here's an example where M = M inverse, and you quickly see that M is definitely not symmetric: Code:
| 9 5 | | 9 5 | - | 1 0 | |
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Just an FYI...all of the match data is NOT included from the Peachtree regional. The scoring system failed so all of the elimination matches and many of the qualifying matches from Saturday are not included. As an example...team 343 finished 12-4 at that regional. We are still shown as 5-3 on the Blue Alliance. That means there are eight matches not accounted for on us alone. That would/could be the same for many of the other teams who attended Peachtree.
Is there anybody that has that manual data? Can it be manually entered into the Blue Alliance? Thanks, Mike Carron Team 343 |
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if there was a normally very high scoring team that happened to malfunction in a match where they were in your alliance, or was defended a lot in your particular round, wouldn't your score be highly skewed?
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Okay, I have created a basic program to make the n X n matrix as defined in the post that sort of explains the rankings but what do I do from there? I am only in trig and we have definitely not learned that stuff with matrices. Can somebody give me an example of "M(k1)p(1)+M(k2)p(2)+...+M(kn)p(n) should equal s(k)" or clarify that please? Thanks!
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We were hevily defended all through GLR hurdling only 2-3 times a match where we usualy hurdle 4-6 times and at Detroit we were broken 3 of the matches, but we are still high on the list:ahh: . That shouldn't effect it much.
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Then you need to multiply M inverse by s. What you're looking to get out is another column vector, or list of numbers, similar to the one you have in s, although with different numbers. To do that, simply multiply each element in a column of M inverse (with a fixed row) by each element in the corresponding row in s (which has only one column). For example, to find the 10th element of p, or the average number of points contributed by the team in the 10th row, add up M(10,1)*s(1) + M(10,2)*s(2) + M(10,3)*s(3) + ... Doing that for each row in M will get you p, the vector you're looking for. If this didn't make much sense, look at http://mathdemos.gcsu.edu/mathdemos/matvec/matvec.html or http://en.wikipedia.org/wiki/Matrix_multiplication |
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I added Peachtree missing matches to the database. I'll do another database dump after the NYC regional is finished. Thanks for pointing me to the data!
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Looks like the system is working well, but I think if we add a little to it it would work much better.
Right now it is set up to calculate the average points contributed to an alliance by one team. This method does not take care of the defence. you guys who did the calculations, can you add defence to it as well. so example: Red Score = RedA_O + RedB_O + RedC_O - BlueA_D - BlueB_D - BlueC_D then solve for Team_O which is the average points added to their alliance, and Team_D which is the average points one team take away from their Opponent's alliance. |
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Just a few questions left but the post above helped a bunch. Do i want M inverse or transverse? Also, do I want it to equal s and multiply by p or the other way around? Lastly, M(10,1)*s(1) + M(10,2)*s(2) + M(10,3)*s(3) Means to take whatever value is in the matrix at (10,1) and multiple by s and by 1 right? thanks!
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Hey Kyler:
Do you want me to send a slide rule with Nathan tomorrow to help you out? Nesher |
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Bongle-
Are you going to do recreate the rankings after all the regionals are done and before the finals in Atlanta? |
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I'll do an Atlanta-only one after ATL too if there is demand. |
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I am not a programmer, but here is an interesting challenge for someone with some time on their hands. Once Atlanta divisions are posted, use the OPR to pick the winning alliance assuming that the rankings and draft follow the OPR (alliances would be 1,2,24-3,4,23-5,6,22......). Obviously the OPR will pick 1114 and some other powerful hurdler as the winners, but I am curious how closely it may actually predict things.
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Use the match scheduling algorithm to generate a schedule, then simulate each division's games, assuming that the alliance with the higher OPR always wins. Now you've got more accurate simulated rankings, because you may even have a weaker team get into the top 8 due to luck with the scheduling. However, you'd have issues because in the real world there'll be draft rejections which you can't plan for. Then do the division playoffs and Einstein simulation. I may do that after my first exam when I have a long time off. |
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Hate to ask for more, but here goes:
I noticed our OPR drop dramatically when week 1 was added in (we had a lot of technical difficulties). Is anyone planning on doing an OPR ranking only using a teams last regional? This may more acurately show how the teams will perform at Atlanta.... |
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Well, just something interesting I have found. Its to be expected and nothing major but it dies go to show being very percise (adding 4 decimal points) Can change things quite a bit in ranking the teams. To test my own Power Ranking program I ran the GTR results and found my results were different that bongles by up to a point (might not seem like much but once you get to the lower power rankings it is 10 % +) and it changes orders a lot. Here's what i found.
Nevermind, I entered one score wrong. My results are the exact same but with 4 excta decimal points. I only included a few differences because the teams start to change then and I was too lazy to sort that all out. Also, a big THANKYOU! to everyone that helped me understand this stuff. |
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Kyler,
Will you post your program code for review? I think that your are on to something by adding precision to your calculation.:) |
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Wee, I managed to break java :P
My inversion algorithm for the 1465x1465 or so matrix runs out of heap space. I'm currently working on an alternative way to get p. Right now, I'm going from Mp = s to p = Minverses. I might try to create an augmented matrix and rref it to get the solution. Any other ideas? EDIT: Nevermind, I just solved the equation Mp = s. Greg, I now have the OPR's generated from the entire csv dump you generated. Is there any way you could generate other csv dumps to analyze? By regional, by week, over the last three weeks, etc., all sounds like interesting sets of data to analyze. |
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I have the rankings generated from Greg Marra's csv dump of the TBA database.
Top 50: Code:
1114 66.10642467 |
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for some reason, i dont think that ours is right... b/c we have had three hurdles or more in 75% of our matches and it shows that we score about 26 points per match, which w/ three hurdles (including lines) we should be just a little under 30... but of course i dont know what went into the compiling:cool:
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Is this data supposed to be your average score or your potential average? What exactly is your number supposed to mean?
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Also, different methods of inverting the matrix could cause somewhat substantial differences in the floating-point input. I haven't had the time to update my program to use TBA's CSV dump, so we won't be able to compare until probably tuesday when my classes end. |
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The Blue Alliance data also gives scores of -1 for the missing peachtree matches (which are now in TBA database, so they would be included for the next data dump). I've attached just the GTR qualifying matches (which I pulled from TBA's big dump) so that it is possible for sumadin to check his results against Bongle and Kyler's. |
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I also see a couple of teams on your list that I know who's scores are way to high. As I have said from the beginning the algorithm just won't work unless the penalties are somehow added in. They are just to important to this years game. |
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Team # OPR |
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You might want to look into using Maple or Matlab. They're design for these types of computation, plus I'm sure you'll find plenty of other uses for them. I've found them to be indispensable tools. |
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For instance, take 1114 at GTR in the eliminators: they were playing with a team that they founded and they had won 3 regionals with. That team of robots will probably work together much better than a random team in qualifications, and so the scores will be different. Another thing is that during eliminations, robots steadily get worn down: mechanisms break that can't get repaired in time, batteries slowly get discharged, motors overheat, and so you get things other than the team's competitiveness affecting the scoring level. Plus, at a shallower regional like at waterloo, you'll end up with things like 1114/2056 against 3 teams who can't even hurdle, and the scores will be ludicrously high. Quote:
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Also I see several teams who's expected contribution is way to high from my experience of their performance this year. If you guys want this tool to be useful you need to figure out why. It's just my theory that it can't property calculate the impact of penalties, if you think this theory is wrong then you need to figure out what is causing the figure to be so different than the actual results. |
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By no means am I suggesting that this model is perfect. From the numbers that Team 1114 has run, there are some definite anomalies. That being said, for the most part this model seems to agree with the specific match data we've collected at three events we attended. Like with most statistics, the value is all in how you interpret them. If you don't think they're accurate, ignore them. |
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Just to emphasize the quality of OPR as a predictor of who will be playing on Saturday afternoon. Keep in mind, this was a 66 team regional, and predicting 24 robots out of the field is no small feat.
Here's the top 24 OPR from the GTR, and whether they were picked in the elims: Code:
1114 #1 CaptainExpect all good scouting teams to be using these stats in the very near future. |
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We are not competing in Atlanta so I have no need for this data, all I am saying some of what I see in spreadsheet is very wrong. You can ignore it, I don't care. Edit: I'll still buy you a doughnut in Atlanta. |
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All I understand is 1114 has an impressive score...
Which seems to fit their impressive robot. Nice work... this will make a excellent tool to help newbies . |
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Sumadin
Your latest OPR must have an error (86 1.478877681). All the previous OPR data runs had Team 86 with an OPR between 8.8 and 9.2 points. Our actual average for the Florida Regional was 11.5 points. The difference of over 2 points can be explained by the fact that our 1st match included two no show partners. However, I can not figure out the 10 point difference in your post today |
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Why? What team in their right mind would choose a team with a 3-5-0 record? Teams that understand the math behind the scouting, that's who. Don |
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GLR: 20/24 Florida: 19/24 VCU: 17/24 So it is a somewhat accurate predictor, but it doesn't take into account that a team's 3rd pick is likely to be a lower-scoring lap bot because of the game only having 2 trackballs. Picking strictly by OPR is a fast way to get eliminated, because you'd probably end up with 3 delicate, relatively slow hurdlers fighting over 2 trackballs, rather than a more-ideal combination of 1 robust lapper/defender and 2 hurdlers. |
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217 50.7032 |
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do you have one for each regional? b/c if you do, i would like to see palmetto... b/c i never got to see any statistics:(
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Palmetto Code:
845 34.6689 |
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So all i gotta do is say
"Can i see the opr's for the wisconsin regional?" and they will magically appear a few posts down from this one? |
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well we'll see if this magic works...
ABRAWisconsinRegionalKADABRA!!! |
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Wisconsin Regional Code:
1625 49.8168 |
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Again, not a programmer, but I have an interesting idea for writing a DPR (Defensive Power Rating) algorithm. Take your OPR values, apply them to the matches. The delta between the expected score and the actual score would be the raw value for a DPR score. Use your OPR techniques to then factor out the DPR each team might provide.
One thought on why your average score is much higher than your OPR is that a decent hurdler may actually slow down the scoring of an exceptional hurdler. Take for instance an 1114. I know they have done 7 hurdles and may even have done 8 in a match. The match where they did 8, probably would not occur with 2 other decent hurdlers as they may hurdle also and thus accidentally slow down an 1114. Thanks for posting your results. |
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oh thank you it seems like there really is magic in this world
*looks up with his fingers interlaced in a weird disney like pose* |
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Bongle, not that you need additional validation but your numbers match up exactly with the numbers that our guys have calculated.
This information in the hands of teams that know how to use it will be very dangerous :) ... I know that I plan to have up to date information in Atlanta for our division. Although there is no replacement for watching matches and see teams improve through out the weekend. I will have to keep my 'black magic' going and pull more 503s out of my hat, or they just might replace me with a OPR scoring database. |
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My explanation for why the data is seams like it is skewed towards the extremes is team coordination, track speed, and defense. When very good teams get to together, this is a different game (you can see it in the elims). They are able to coordinate their strategy better (particularly hybrid mode). Ex. If a partner's hybrid routine bumps knocks off their higher-scoring partner's hybrid, that can easily cost the alliance 20+ points (which would reflect very badly on the the OPR of lower scoring bot). When good scoring machines are together (particularly when they are on both alliances) the track moves a lot quicker and there are very few traffic jams, so everyone scores more. If there is defense being played, it will likely only slow down the higher scoring machine, so the impact of the scoring capabilities of the partners is increased. These reasons should also explain some of the differences between regionals. Of coarse there is always the random variable of luck, but we will assume that is zero mean:D Well thats my $0.02 |
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personally i like these numbers. No its not a substitute for scouting but it atleast lets you know who you may want to pay closer attention to. I also like the new list as 1629 moved up to 40th!!
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I'm not sure I saw this yet, but thought it would be interesting to cross reference the OPR with the CMP teams that are currently on the list... Here's the top 50 CMP OPRs...
Code:
Team Reg Wins OPR |
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Can you do the full CMP list as of now?
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Doug, 191 was a member of the winning alliance at FLR.
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Your assumption is correct that Team 86 was a lapbot/defender. Our alliance won the elimination 6-0 at the Florida Regional with scores the ranged from 60 to 116 for 77 point average. I earlier tried to include the use the eliminations for the Florida Regional like Sumadin and the results were very skewed. So the matrices have a problem when the elimination match data is used to solve the matrix. I am still puzzled as to why the eliminations skew the OPR results so drastically (8,8 OPR no elims versus 1.47 OPR with elim) |
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Also 233 performed much better at Hawaii then they did in Florida. Since there is only one OPR for the year, it looks like Pink underperformed at Hawaii and overperformed at Florida. I think that 86's OPR would change less when adding in the elims if you were looking at the Florida data only. *by underperformed, I mean as far as OPR is concerned. It's likely that more defense was played in the finals and was the cause. |
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The Hawaii data for 233 is 53.16 pts versus 35.63 pts at the Florida regional. This 18 point change in OPR probably reflects on the fact that the current matrix mathematics would need a strength of schedule adjustment when comparing 40+ regionals. Has 233 improved its offence by 150% in Hawaii or is the competition 66% as strong? In contrast 1251's OPR varied by only 1 point between Florida and South Carolina.:D |
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can you post the boston regional OPR?
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I'd think that the change in our scoring average could be due to a few things:
At Florida, there were 3 qualifying matches where our tower was 100% inoperable and 2 elimination matches where our top roller did not work, leading to below-average scores there. Also, our autonomous scored about twice as many points in Hawaii than it did in Florida. |
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I love this power rankings, judging by the teams at the top and what I have seen at competitions. It is a good indication of what teams are scoring. This would be a good tool to do some scouting on what teams to watch for on Thursday at the Championship. (Once divisions come out.) |
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can we please get a top 50 with the elimination rounds. i think it would be intresting to see these stats
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http://www.chiefdelphi.com/forums/sh...7&postcount=42 |
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So... I'm really curious to see the OPRs for the Colorado Regional...
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SVR and Davis please?
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Sorry, I've been out with friends and doing homework for the last couple days.
Colorado Code:
1625 44.1421Code:
100 37.5837Code:
766 35.6844 |
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Can we expect a CMP OPR by sometime tonight or tomorrow?
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probably more like wednesday or thursday... horray for fill-ins!:p
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I'll post a championship OPR by division once the division lists come out, so probably sometime this or next week.
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GRR!!! Must have scouting information!
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