Galileo 2012!

[David Smellie]

Originally Posted by Rashek

I just wanted to post what my research has showed me about the teams on Galileo. I came up with this data by going down the entire listof the field. Next I looked up these teams on FRC Spyder. Anytime that scored 200+ in any of the categories I wrote down. After I figured out those teams I copied the information onto an excel spreadsheet.

The numbers I used were a mix from each of the competitions. For example: Say a team went to three competitions. At Competition A they had their best teleop points. At Competition B they had their best hybrid. At Competition C they had their best balancing. So I would used the teleop from A, the hybrid from B, and the balancing from C.

Next I added all the numbers together in order to come up with a grand total of points. I then arranged the grand totals in descending order. I came up with the following:

Team # HP BP TP Grand Total
2054 227 200 289 716
33 195 130 380 705
1918 235 140 269 644
25 175 120 329 624
2337 234 150 239 623
1718 166 150 260 576
2016 205 140 209 554
3322 166 100 285 551
180 175 120 209 504
2169 155 30 317 502
103 136 110 223 469
48 154 70 240 464
1323 132 50 240 422
801 116 90 215 421
399 93 110 206 409
868 148 50 211 409
772 137 50 204 391
148 97 50 208 355

I am well aware of the fact that FRC Spyder doesn't count the elimination matches, and therefore this spredsheet isn't 100% accurate. This is my first time doing anything like this so any feedback woul be greatly appreciated.

The primary problem with this method is that FRC Spyder calculates alliance scores not individual robot scores. As such this will produce a bias towards teams in deeper regional events (ie MSC and MAR). The top ten appears to confirm this bias as the top eight teams are from these two regions. When looking at this data, be sure to consider this fact.

[Rashek]

I am aware of both of these problems. This was just a last minute thing I put together.

[Cory]

Originally Posted by David Smellie

The primary problem with this method is that FRC Spyder calculates alliance scores not individual robot scores. As such this will produce a bias towards teams in deeper regional events (ie MSC and MAR). The top ten appears to confirm this bias as the top eight teams are from these two regions. When looking at this data, be sure to consider this fact.

The bigger problem is that you need to normalize to either a per match basis or a 10 match schedule, as many events got more or less than 10 qual matches.

[maverickfan138]

It would be interesting to see how that list would look with the rest of the division considered. For instance, 573 at MSC scored 234 HP, 170 BP, and 239 TP for a total of 613 points. That places them in between 2337 and 1718, and I'm sure there are other teams who would be thrown into the mix.

[Rashek]

I'm sorry 573 Somehow I must have forgot to put you guys into that list. I don't know how that happened.

[maverickfan138]

Originally Posted by Rashek

I'm sorry 573 Somehow I must have forgot to put you guys into that list. I don't know how that happened.

No worries. You did say it was a quick job so a slip up is understandable.

[John G]

Team 399 has successfully tested our "stinger" / balancing assist mechanism. Can't wait to use it. Only 5 more days!!!

[IKE]

Originally Posted by Rashek

I just wanted to post what my research ....

Next I added all the numbers together in order to come up with a grand total of points. I then arranged the grand totals in descending order. I came up with the following:

Team # HP BP TP Grand Total
2054 227 200 289 716
...
I am well aware of the fact that FRC Spyder doesn't count the elimination matches, and therefore this spredsheet isn't 100% accurate. This is my first time doing anything like this so any feedback woul be greatly appreciated.

I really like that you are looking at the data but... this is kind of bad math gone wrong scenario. As others have stated, you have two factors contributing to a lot of possible error.

#1 12 matches at FiM & MAR versus 8-10 matches most other regionals. If you normalize per match, you will get a much better metric.

#2 Alliance partners. The metric above can be highly dependent on alliance partners. When a robot is scoring around 2X the average, in theory they are only responsible for 50% 0f the points (2X+X+X)=4X & 2X/4X=50%...). Since MSC and MAR are "qualified" events, the average strength of the participant is significantly different from most regionals.


The general trends are often reasonable (highest scorer will be highest), but trying to sort with any sort of fidelity requires additional work.

Look at some of the normalizing functions (dividing by number of matches, normalizing to event strength...). Normalizing function have their own problems as well.
In 2010, from an OPR perspective, 3 robots all at an OPR of 2 (about 2X national average) could actually combine to be worth 8-10 pts. versus the predicted 6 pts. in a match doing zone strategy.
In 2011, at really deep fields, OPR was actually driven down as 60+60+60 would often equal 120 due to the digresive scoring last year. Thus teams taht had really good partners at a strong event might actually see their OPR go down as they more frequently got into the stronger digresive scoring regions .

[akoscielski3]

Team 772 has a Robot Information/Overview page for all teams. Please take a look at it in the attachments below.

Something about our robot that I would like to point out for all teams is that it is Possible for us to triple balance! Our robot can hang 17" off the bridge, taking up only 22" of the bridge (with bumpers).

Good Luck to all teams at Championships!

[EricS-Team180]

Originally Posted by Gary Dillard

I have a feeling that they may have kept a few tricks in their bag just for the Championship.

A new wrinkle? I can neither confirm nor deny.... :-) It's good to see to 801 and 744 hanging out in the top 25 of Joe's list. Can't wait to mix it up in this division!

[S.P.A.M.er]

Originally Posted by Gary Dillard

Hmmmmmmmmmm SPAM was #1 seed at both of their regionals, finalist in one and winner in the other, 2nd highest OPR & World ranking in the division, Joe's simulation seems to like them, yet noone posting seems to think they are a contender. They must be flying under the radar. I have a feeling that they may have kept a few tricks in their bag just for the Championship. It's been 10 years since their last Einstein appearance; could this be their year?

Yes It is time for a SPAM appearance in Einstein.

[rick.oliver]

1038 at Smoky

[E-Stop]

To add to that list 342 is also wide but everyone should have that by now. Matches seem to be getting harder and harder. Anyone notice anything awesome in our division or anything that happened in the matches that was really cool? I know 342 and 537 co-oped in literally5 seconds with the robots hanging off the edge.

[gyroscopeRaptor]

Does anyone know what's happening to 2169? They have sunk like a stone in the rankings.

[efoote868]

Just ran the OPR for results so far, figured I'd post in case anyone is interested.

180	40.1219
33	37.0848
148	36.0994
868	35.0515
744	34.6639
25	32.9627
3103	30.8391
2054	30.2903
2016	29.8592
1718	27.9295
573	27.9071
3173	26.9689
3453	26.0827
1323	25.6239
48	24.566
1918	24.3989
2169	24.1908
801	23.4026
3322	23.2505
269	23.1771
16	22.8063
1732	21.7211
1574	21.6791
4394	21.6502
2928	21.5575
4372	20.6242
1714	20.2901
3142	19.7537
2486	19.5091
1583	19.4965
231	19.2465
1305	19.2262
2439	18.8826
492	18.4234
1038	18.2836
237	17.9422
1350	17.7897
358	17.6638
1538	17.1042
2337	17.0359
1671	17.0301
533	16.9463
1208	16.3586
2010	15.841
3574	15.5875
4057	15.1774
1425	15.1184
3530	15.0245
3925	14.4958
103	14.0242
870	13.5901
2220	12.1548
384	11.9665
815	11.8108
4159	11.7794
1262	11.6797
2339	11.6545
771	11.647
125	11.552
766	11.4937
568	11.4033
527	10.892
8	10.4584
1011	10.1324
2158	9.94262
1515	8.75977
772	8.74136
3196	8.49061
2377	8.41885
281	8.15301
399	8.13106
296	8.07278
2090	8.0171
585	7.3832
1747	7.37449
69	7.27027
1382	6.77873
4146	6.55601
342	6.45622
4031	6.24064
3189	6.22137
3931	5.64647
3784	5.46039
2642	5.3852
4099	5.33062
1831	4.97288
3259	4.67095
2341	4.53083
168	4.27748
263	4.17336
4090	3.96362
4013	2.61065
151	1.94046
4083	1.72622
4028	1.13921
1108	0.975128
2704	0.153195
1212	-1.52536
4353	-3.85425
3981	-5.01059