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  #151   Spotlight this post!  
Unread 08-04-2008, 16:47
Jacob Plicque Jacob Plicque is offline
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Re: Offensive Power Rankings for 2008

Quote:
Originally Posted by AndyB View Post
Well, the head ref records penatlies for each robot during the match on a sheet, but you never see it unless you go to the "Contest the score" box.

My data comes from Lines Crossed in Hybrid, Balls Removed in Hybrid, Laps, Herds, Hurdles, and Balls Placed at the End.
Is the "contest the score box" online data?
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  #152   Spotlight this post!  
Unread 09-04-2008, 15:41
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Re: Offensive Power Rankings for 2008

By anticipated popular demand, OPRs by division.
Column order is
Team#, # of regionals, last OPR, last regional, best OPR, best regional

Stats that I found interesting:
Code:
Total OPR:
Galileo: 	1707
Archimedes: 	1622
Curie: 		1521
Newton: 	1514

OPR of top 24:
Galileo: 	892 (figures, with 1114 in there)
Newton: 	851
Curie: 		806
Archimedes: 	798

% of total OPR that the top 24 take up (a measure of depth, I suppose)
Archimedes: 	49.2%
Galileo: 	52.2%
Curie: 		52.9%
Newton: 	56.2%
Archimedes
Code:
987	2	49.3643	lv	53.1822	sd
1024	3	49.0029	buck	49.0029	buck
1124	1	47.9453	ct	47.9453	ct
525	2	46.4897	mn	46.4897	mn
27	2	41.5673	glr	41.5673	glr
93	2	40.3215	mn	40.3215	mn
365	2	37.1666	phil	37.1666	phil
1218	1	35.6348	ches	35.6348	ches
357	2	34.7581	phil	34.7581	phil
71	2	34.647	wm	34.647	wm
337	2	33.5239	hi	33.5239	hi
41	2	31.2988	ny	31.2988	ny
1065	1	30.4807	fl	30.4807	fl
201	2	29.9324	glr	29.9324	glr
2024	2	27.7007	hi	27.7007	hi
1	2	27.6828	det	27.6828	det
1598	1	27.0323	vcu	27.0323	vcu
116	2	26.4095	ny	26.4095	ny
555	4	26.0896	ny	28.6788	bay
1261	2	25.5961	palm	25.5961	palm
1690	1	24.8488	is	24.8488	is
2166	2	24.0923	gtr	24.0923	gtr
2550	1	23.621	or	23.621	or
842	3	23.3982	lv	23.3982	lv
292	2	23.0619	boil	23.0619	boil
11	2	22.9507	ches	22.9507	ches
222	1	22.724	pitts	22.724	pitts
816	2	22.6902	phil	29.4389	nj
386	2	22.5882	palm	22.5882	palm
107	2	21.8664	glr	21.8664	glr
1987	1	21.4701	mn	21.4701	mn
1504	2	21.4204	wm	21.4204	wm
85	2	21.2389	wm	21.2389	wm
2081	2	20.6428	boil	20.6428	boil
1094	1	20.5252	stl	20.5252	stl
171	1	20.006	wi	20.006	wi
66	2	20.0018	glr	20.0018	glr
614	1	18.7356	ches	18.7356	ches
2048	2	18.6977	buck	18.6977	buck
1746	2	18.6837	palm	18.6837	palm
401	1	18.6136	vcu	18.6136	vcu
1528	1	18.5772	det	18.5772	det
2377	1	18.2489	ches	18.2489	ches
190	2	18.0702	svr	18.0702	svr
499	2	17.6879	bay	17.6879	bay
1727	2	17.5064	phil	17.5064	phil
2472	1	16.8751	mn	16.8751	mn
2335	1	16.7533	gkr	16.7533	gkr
1323	2	16.5556	davis	16.5556	davis
1305	2	15.8986	gtr	15.8986	gtr
177	1	15.4353	ct	15.4353	ct
1834	2	14.9705	hi	14.9705	hi
2668	1	14.4941	phil	14.4941	phil
2624	1	13.4544	gtr	13.4544	gtr
949	1	13.3528	seat	13.3528	seat
1547	2	12.9788	gtr	12.9788	gtr
236	1	12.5966	ct	12.5966	ct
2449	1	12.261	ar	12.261	ar
1143	1	11.9864	phil	11.9864	phil
476	2	11.8943	ok	12.9425	gkr
2518	1	11.743	mn	11.743	mn
1646	2	11.4519	glr	11.4519	glr
1474	1	10.7638	bos	10.7638	bos
2342	1	10.522	bae	10.522	bae
599	2	10.2908	la	10.2908	la
1902	2	10.2798	bay	21.5419	fl
135	2	10.1138	boil	14.1674	stl
2604	1	9.16108	wm	9.16108	wm
1379	2	9.08873	bay	11.5536	peach
2575	1	9.08777	br	9.08777	br
701	2	8.54408	davis	15.0851	sd
1398	2	8.26199	palm	8.26199	palm
122	1	7.81042	vcu	7.81042	vcu
49	2	7.79258	bay	9.5169	det
303	1	7.528	nj	7.528	nj
2424	1	6.33004	ok	6.33004	ok
228	1	6.02626	ct	6.02626	ct
151	1	4.74671	bae	4.74671	bae
269	1	4.51404	wi	4.51404	wi
752	1	3.66353	nj	3.66353	nj
322	1	3.07761	glr	3.07761	glr
433	1	3.01739	phil	3.01739	phil
1577	1	2.92397	is	2.92397	is
900	2	2.86289	palm	4.02724	vcu
1795	1	0.12213	peach	0.12213	peach
461	2	-0.978711	glr	14.5376	boil
Curie
Code:
1126	2	42.8778	buck	42.8778	buck
2171	2	42.5333	wm	42.5333	wm
67	2	40.5394	glr	43.2951	flr
368	2	39.5571	hi	39.5571	hi
33	3	38.5823	glr	38.5823	glr
395	2	38.4891	ny	38.4891	ny
16	2	38.313	bay	38.313	bay
100	2	37.5837	svr	37.5837	svr
2337	2	37.2119	wm	37.2119	wm
126	2	37.134	ct	37.134	ct
191	2	35.8837	buck	39.0776	flr
1511	2	35.0501	phil	35.0501	phil
1477	2	34.4194	bay	34.4194	bay
358	2	33.2138	spbli	33.2138	spbli
703	2	33.0321	gtr	33.0321	gtr
237	2	31.8253	phil	31.8253	phil
326	2	31.1764	glr	31.1764	glr
501	2	31.0184	lone	31.0184	lone
768	2	30.8543	ches	30.8543	ches
1592	2	30.3363	co	30.3363	co
2590	1	27.1592	nj	27.1592	nj
45	2	27.1463	boil	27.1463	boil
1418	1	26.2306	ches	26.2306	ches
435	1	24.7734	vcu	24.7734	vcu
1649	1	24.1985	fl	24.1985	fl
2344	2	21.8188	ny	21.8188	ny
1350	1	21.6921	bos	21.6921	bos
294	2	21.0882	la	21.0882	la
57	2	20.7083	lone	20.7083	lone
573	2	20.1932	glr	21.5527	pitts
231	1	19.3323	lone	19.3323	lone
223	1	18.6501	nj	18.6501	nj
108	2	18.3036	fl	18.3036	fl
2609	1	18.099	wat	18.099	wat
903	2	18.0734	wm	18.0734	wm
1732	2	17.8759	glr	17.8759	glr
1747	2	17.6147	glr	17.6147	glr
155	1	16.9729	bos	16.9729	bos
486	2	16.6277	phil	16.6277	phil
1386	1	16.4528	buck	16.4528	buck
173	2	15.9452	bos	15.9452	bos
118	2	15.3893	bay	31.3232	lone
2520	1	15.2241	lv	15.2241	lv
138	1	15.0914	bae	15.0914	bae
1989	1	15.0827	ny	15.0827	ny
75	2	14.9254	ches	14.9254	ches
178	1	14.5708	ct	14.5708	ct
1802	1	13.9393	gkr	13.9393	gkr
418	1	13.6205	lone	13.6205	lone
1025	1	13.46	det	13.46	det
271	2	13.4145	ny	13.4145	ny
2474	1	13.1963	boil	13.1963	boil
1013	2	13.1038	lv	13.1038	lv
1699	1	12.7678	bos	12.7678	bos
2053	1	12.629	flr	12.629	flr
1860	1	12.5214	br	12.5214	br
340	1	12.2471	flr	12.2471	flr
1939	1	12.1341	gkr	12.1341	gkr
1566	1	12.0738	sd	12.0738	sd
967	1	11.7833	stl	11.7833	stl
1245	1	11.1402	co	11.1402	co
527	1	10.6705	spbli	10.6705	spbli
2410	2	10.5735	gkr	10.5735	gkr
2556	1	10.5214	bay	10.5214	bay
462	1	10.4146	bay	10.4146	bay
304	1	10.1755	phil	10.1755	phil
1311	1	10.0732	peach	10.0732	peach
1533	1	9.89596	peach	9.89596	peach
2038	1	9.09497	peach	9.09497	peach
86	1	8.56691	fl	8.56691	fl
1102	2	8.01401	palm	8.01401	palm
830	2	7.30086	glr	14.877	stl
2115	1	6.9709	mw	6.9709	mw
1156	1	6.54232	br	6.54232	br
858	1	6.19068	wm	6.19068	wm
1071	1	5.97234	ct	5.97234	ct
1266	2	5.88688	lv	5.88688	lv
2629	1	5.77631	svr	5.77631	svr
1599	1	3.42647	vcu	3.42647	vcu
2454	1	3.33634	hi	3.33634	hi
4	2	2.8721	lv	2.8721	lv
604	1	1.99006	svr	1.99006	svr
2429	1	0.38431	la	0.38431	la
203	1	-1.3286	ches	-1.3286	ches
677	1	-5.87753	bos	-5.87753	bos
Galileo
Code:
1114	3	85.1523	gtr	85.1523	gtr
103	3	55.0129	phil	55.0129	phil
217	3	50.7032	glr	50.7032	glr
40	2	49.7452	bos	49.7452	bos
330	2	46.3083	la	51.5438	sd
254	2	44.5277	hi	44.5277	hi
494	3	42.5188	glr	42.5188	glr
25	2	42.1758	hi	42.1758	hi
1717	2	38.8717	la	38.8717	la
121	1	36.7586	bae	36.7586	bae
1629	2	35.6228	buck	35.6228	buck
234	2	34.9821	wm	34.9821	wm
469	2	34.469	glr	36.1295	det
343	2	29.7443	palm	29.7443	palm
65	2	29.6645	glr	29.6645	glr
176	2	28.9639	gtr	28.9639	gtr
195	1	28.8298	ct	28.8298	ct
1540	2	28.4674	lv	28.9083	or
88	2	28.0692	bos	28.0692	bos
291	2	24.9495	glr	24.9495	glr
1450	1	24.3531	flr	24.3531	flr
2468	1	24.1073	lone	24.1073	lone
70	3	24.0254	glr	27.176	stl
2062	2	24.019	wi	24.019	wi
716	1	23.9309	ct	23.9309	ct
1503	2	23.5493	gtr	23.5493	gtr
1816	2	22.8742	mn	22.8742	mn
932	2	22.6811	ok	22.6811	ok
48	3	22.5494	buck	22.5494	buck
384	1	22.352	vcu	22.352	vcu
694	2	22.2311	ny	22.2311	ny
148	2	22.1501	bay	32.2765	stl
1089	2	22.0812	ct	22.0812	ct
316	2	20.0927	phil	20.0927	phil
548	2	20.0728	glr	20.0728	glr
2165	1	20.0665	ok	20.0665	ok
1676	2	19.7986	gtr	19.7986	gtr
364	1	19.3686	bay	19.3686	bay
84	2	19.2057	phil	19.2057	phil
1366	1	19.0103	nj	19.0103	nj
134	2	18.5866	ches	18.5866	ches
1023	1	18.233	glr	18.233	glr
2487	1	17.9854	spbli	17.9854	spbli
180	1	17.4385	fl	17.4385	fl
812	1	17.435	sd	17.435	sd
2237	2	16.7312	palm	16.7312	palm
2638	1	16.7157	spbli	16.7157	spbli
425	1	16.4547	fl	16.4547	fl
2340	1	16.3891	flr	16.3891	flr
894	1	16.1329	glr	16.1329	glr
1319	2	16.0827	palm	16.0827	palm
2046	2	15.6193	seat	29.4435	or
2549	1	15.4527	wi	15.4527	wi
2564	1	14.6666	fl	14.6666	fl
980	2	14.3034	la	14.3034	la
612	2	13.5524	buck	15.1939	vcu
1038	1	13.3602	mw	13.3602	mw
1983	2	13.2864	seat	20.1201	or
8	3	13.101	lv	13.101	lv
302	2	12.9585	wm	12.9585	wm
457	1	12.2723	fl	12.2723	fl
1739	2	11.8769	wi	11.8769	wi
1758	2	11.297	palm	11.297	palm
597	2	11.2872	hi	14.4516	sd
2599	1	11.2497	sd	11.2497	sd
2023	1	11.1125	fl	11.1125	fl
2437	2	9.80826	hi	13.5781	wi
399	2	9.67775	la	11.861	sd
1296	1	9.23699	co	9.23699	co
1212	1	9.22365	ar	9.22365	ar
1138	2	8.48742	hi	12.1488	sd
2423	1	8.05239	bos	8.05239	bos
1390	1	7.99435	fl	7.99435	fl
226	2	7.03603	glr	7.03603	glr
1885	2	6.41505	phil	10.7223	vcu
168	1	5.60546	fl	5.60546	fl
2354	1	4.46069	ok	4.46069	ok
1254	1	4.39064	wm	4.39064	wm
115	2	4.20432	davis	10.905	svr
839	2	2.64941	bos	7.2402	ct
1523	1	1.93133	fl	1.93133	fl
2621	1	0.116815	ct	0.116815	ct
1576	1	-0.647048	is	-0.647048	is
509	1	-3.26759	bae	-3.26759	bae
1595	1	-5.93147	or	-5.93147	or
Newton
Code:
2056	2	59.334	gtr	59.334	gtr
233	2	55.609	hi	55.609	hi
39	2	52.6717	lv	52.6717	lv
175	2	47.1574	ct	47.1574	ct
1625	3	44.1421	co	49.8168	wi
141	2	42.6529	wm	42.6529	wm
359	3	39.5435	hi	39.5435	hi
1086	1	37.2707	vcu	37.2707	vcu
47	2	36.6663	glr	36.6663	glr
1806	2	35.2555	ok	35.2555	ok
968	2	34.9029	hi	35.1886	sd
20	2	34.6227	bos	40.235	flr
1574	1	34.6153	is	34.6153	is
1251	2	34.1576	palm	36.9569	fl
192	2	31.8033	svr	31.8033	svr
288	2	30.668	wm	30.668	wm
111	2	29.3402	boil	29.3402	boil
692	2	25.7874	davis	25.7874	davis
1736	1	25.5406	wi	25.5406	wi
1279	2	24.6228	bay	24.6228	bay
179	1	24.5431	fl	24.5431	fl
1594	2	23.9915	ny	23.9915	ny
397	2	23.5141	wm	25.7875	det
79	2	23.3832	bay	47.2358	fl
68	3	23.0027	glr	23.0027	glr
1662	1	22.8678	davis	22.8678	davis
1388	2	22.7622	davis	22.7622	davis
488	2	21.8286	seat	21.8286	seat
2122	2	21.7512	davis	21.7512	davis
1318	2	21.288	seat	21.288	seat
801	1	20.9428	fl	20.9428	fl
341	2	20.7585	phil	22.0585	ches
312	1	19.624	fl	19.624	fl
1429	2	18.971	lone	18.971	lone
375	3	18.6259	ny	24.7563	pitts
2214	1	18.4471	is	18.4471	is
1756	1	17.8439	mn	17.8439	mn
102	2	17.3692	spbli	17.3692	spbli
2483	2	16.524	palm	16.524	palm
503	2	16.2864	glr	17.8147	mw
2016	2	16.0195	ches	27.2495	nj
1165	1	14.3534	ar	14.3534	ar
2630	1	13.9012	is	13.9012	is
329	2	13.4912	spbli	13.4912	spbli
2430	1	12.8461	palm	12.8461	palm
2352	1	12.4272	ok	12.4272	ok
1868	1	12.3273	svr	12.3273	svr
587	1	12.3214	vcu	12.3214	vcu
1108	2	11.8804	lone	15.665	gkr
2338	1	11.471	mw	11.471	mw
440	2	11.3609	bay	11.3609	bay
832	2	11.3028	bay	16.531	peach
1714	2	11.249	buck	11.249	buck
2591	1	10.9547	glr	10.9547	glr
207	2	10.853	la	13.9768	ar
224	2	10.8517	ches	10.8517	ches
87	1	10.7999	nj	10.7999	nj
2455	1	10.7198	hi	10.7198	hi
128	1	10.4523	pitts	10.4523	pitts
1357	1	10.2145	co	10.2145	co
60	2	10.1602	lv	22.0938	ar
1014	1	10.022	buck	10.022	buck
2415	1	9.7428	peach	9.7428	peach
159	1	9.40661	co	9.40661	co
610	2	9.13372	gtr	11.0441	sd
2614	1	8.05032	pitts	8.05032	pitts
2543	2	7.97485	co	7.97485	co
868	2	7.82689	boil	7.82689	boil
1127	1	7.30454	peach	7.30454	peach
1033	1	7.02428	vcu	7.02428	vcu
1538	2	6.88265	la	9.84046	sd
547	1	6.79991	peach	6.79991	peach
2041	1	5.87738	mw	5.87738	mw
1522	1	5.56067	vcu	5.56067	vcu
5	1	3.63177	det	3.63177	det
1941	2	2.84981	glr	8.57622	wm
2557	1	0.903917	seat	0.903917	seat
120	2	0.741635	buck	17.8092	pitts
714	1	0.34217	nj	0.34217	nj
1502	2	-0.382598	buck	16.9048	stl
422	1	-0.500982	vcu	-0.500982	vcu
241	1	-0.749577	bae	-0.749577	bae
922	1	-1.32254	lone	-1.32254	lone
296	1	-5.98088	gtr	-5.98088	gtr
468	1	-7.71247	mw	-7.71247	mw
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  #153   Spotlight this post!  
Unread 09-04-2008, 15:46
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Re: Offensive Power Rankings for 2008

Can we get an OPR for divisions based on overall performance instead of individual regionals?
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  #154   Spotlight this post!  
Unread 09-04-2008, 15:51
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Re: Offensive Power Rankings for 2008

does the makes us the 14th highest scoring robot in Galileo (based on our last regional)?
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Unread 09-04-2008, 16:02
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Re: Offensive Power Rankings for 2008

Quote:
does the makes us the 14th highest scoring robot in Galileo (based on our last regional)?
It means that you have the 14th highest OPR . That is all it means. OPR correlates strongly with scoring ability, but it also correlates with other things such as teamwork (teams that get in the way of their alliance partners would have lower OPRs) and rule-abiding (see those negative numbers? those teams tended to take penalties). Someone who has scouting data on every single team in FIRST might be able to tell you if you're literally the 14th-highest scoring robot in Galileo. However, since I don't think anyone actually has that much data, this is a nice stand-in. It is certainly something to be proud of.

Quote:
Originally Posted by MasterChief 573 View Post
Can we get an OPR for divisions based on overall performance instead of individual regionals?
Overall OPR: Accuracy is directly proportional to a team's consistency over the course of their regionals.

Code:
DIVISION Archimedes	
987	51.019
525	41.6417
1124	40.8123
1024	39.5312
1218	34.8327
365	31.3377
1065	29.7045
93	29.6847
357	27.7146
41	27.6033
71	27.5385
337	27.4501
27	26.3468
2024	25.8596
1598	25.4333
1690	24.8488
201	24.5064
816	23.9692
2575	23.7964
1261	23.1945
222	23.1573
2550	23.0673
171	21.9414
555	21.2763
66	20.4658
1	20.2903
1746	19.9551
107	19.5672
1987	19.2164
292	19.0851
2377	18.916
386	18.0085
1094	17.371
2624	17.2509
401	16.9756
842	16.9502
116	16.9407
2472	16.7278
2081	16.613
2668	15.8868
1528	15.7027
2335	15.5526
177	15.5282
1504	15.51
85	15.0574
614	15.0475
1902	14.6906
949	13.9314
11	13.7692
2048	13.7644
236	13.5819
1143	13.3837
1305	13.0689
1727	12.8284
701	12.6087
2166	12.1423
499	12.0759
476	12.0487
1474	11.9805
2604	11.8988
1646	11.8752
1323	11.4047
2518	10.8855
2449	10.8716
135	10.7748
461	10.2657
1834	10.076
2342	10.0724
190	9.92706
1379	9.74611
122	9.31586
322	9.06697
49	8.83094
269	7.55734
228	7.43488
1547	6.6488
433	6.43184
2424	6.02979
900	5.22459
599	5.16972
1398	4.73357
303	3.99989
151	3.53961
1577	2.92397
752	-0.5685
1795	-1.06975
Code:
DIVISION Curie	
67	43.4969
191	36.8639
368	35.6446
33	34.8674
1126	34.0836
126	31.7883
1156	30.7684
16	30.4445
2171	29.2048
1477	29.0948
100	28.9546
326	27.8608
1511	27.3043
768	27.016
1649	25.2292
435	25.2239
703	24.9523
2337	24.7343
118	24.5567
1592	24.3233
501	23.7284
45	23.6296
1350	23.041
358	22.9799
573	22.6403
237	22.4558
1418	22.3514
2590	21.7718
395	20.5786
231	20.5403
155	19.6723
1989	18.0971
2520	17.0888
223	16.0801
2609	15.9879
486	15.7825
418	15.6633
173	15.1487
57	14.9705
271	14.7283
2474	14.5278
1386	14.2628
1699	13.8221
1802	13.0795
830	13.028
108	12.9329
1245	12.8222
138	12.4111
1939	12.3322
1311	11.6342
2344	11.4982
1747	11.3411
1566	11.3223
903	11.2957
527	11.2424
1025	11.227
462	11.2191
2053	10.5242
2556	10.4971
304	10.4408
294	10.3778
340	9.8499
1732	9.80434
1533	9.59762
1013	8.82684
2410	8.58155
2115	7.70206
2038	7.54205
967	6.97196
178	6.81536
2454	6.72034
86	6.52571
1102	5.72384
858	5.61987
2629	4.37071
75	3.97127
1860	3.44524
1599	3.19584
1071	2.87368
4	2.78029
1266	2.25798
2429	1.69903
604	0.72305
677	-0.8247
203	-3.1758
Code:
DIVISION Galileo	
1114	68.2768
330	48.6731
103	42.7726
217	39.6146
25	39.2088
40	36.7399
494	35.8872
121	35.6617
469	33.1259
1717	32.6803
254	32.2266
195	29.5645
343	28.5426
1629	28.4807
1540	28.3651
148	26.7634
70	26.6412
88	25.5715
2468	23.6247
234	23.3023
176	22.9377
384	22.7696
1450	22.0209
291	21.7519
65	21.6693
932	21.2843
364	20.9701
316	20.4409
2046	20.357
716	20.1283
2165	20.1283
2062	19.9514
894	19.905
180	19.7554
1503	19.556
1816	19.5513
2487	18.8446
1023	18.4076
1089	17.4138
1983	17.4012
84	17.3274
694	17.1061
48	16.8149
2638	15.5348
2549	15.4257
1319	15.177
2437	15.1399
812	14.7013
612	14.3585
425	13.7583
2340	13.2112
1366	12.7707
597	12.2728
134	12.1221
302	12.0411
548	11.4201
980	11.4169
2237	10.9598
2564	10.8055
1296	10.6061
8	10.5598
1038	10.2052
1676	10.0692
115	10.0361
1138	9.90502
2599	9.57715
399	9.24283
1885	9.05314
2423	8.50531
1254	8.20877
457	7.99797
2023	7.5684
1212	7.44153
1390	7.4258
1758	6.75003
839	6.32888
226	5.2471
2354	4.33936
1739	3.21296
168	0.70237
1523	0.25661
1576	-0.647
1595	-1.6056
2621	-2.1082
509	-5.2435
Code:
DIVISION Newton	
2056	53.8927
233	50.9844
39	44.3005
175	39.5251
141	37.9473
1086	37.4814
20	36.9751
1251	36.8961
968	35.1509
1625	35.0787
79	34.8553
1574	34.6153
47	30.8913
111	28.7166
359	28.2419
1806	28.0158
341	25.2499
692	25.1171
179	24.1943
288	23.0485
1662	22.7656
1736	22.4684
397	22.3247
2016	21.736
192	21.6076
375	21.1323
1388	20.4556
488	19.2197
2214	18.4471
1279	18.3019
68	17.1798
2122	16.8979
60	16.8806
1756	16.8379
503	15.9745
1429	15.5409
801	15.3759
312	15.2619
2455	14.5993
2630	13.9012
832	13.7808
1594	13.4057
2483	13.3354
1108	13.2547
2430	12.9953
1318	12.8126
329	12.127
2352	11.7462
1014	11.6403
207	11.3265
1165	10.8877
587	10.834
102	10.4933
120	10.4564
2591	9.50392
159	9.46525
1538	9.44737
128	9.26673
610	9.26187
1357	9.24339
1868	9.1569
1714	8.83824
2614	8.69098
2415	8.63608
2338	8.5209
1127	8.42351
1502	8.25353
1033	7.87484
2041	7.37036
547	7.05956
440	6.74309
1941	6.64743
87	5.88571
2543	5.07453
224	4.18051
1522	3.30679
2557	3.26342
714	3.16513
5	1.21677
296	1.06856
922	0.27762
241	-1.0851
868	-1.3509
422	-3.2074
468	-10.173

Last edited by Bongle : 09-04-2008 at 16:07.
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Re: Offensive Power Rankings for 2008

How about some statistics on the individual divisions like mean, median, standard deviation, and whatever else might be interesting. I'd like to throw some fuel on the best division debate by seeing how they stack up against each other in OPR.
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Unread 09-04-2008, 17:29
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Re: Offensive Power Rankings for 2008

Code:
A	C	G	N
1622.8	1564.3	1707.0	1514.0	Sum
798.6	824.9	892.0	851.7	Sum Top 24
49.2	52.7	52.2	56.2	Top 24 % of total
19.1	18.4	20.0	17.8	Mean
18.2	15.2	17.9	13.9	Median
11.3	11.6	14.3	13.8	Stdev
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Unread 09-04-2008, 17:49
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Re: Offensive Power Rankings for 2008

This might be immpossible but our team wants to know the rankings for the best hyrid teams in the country..
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Re: Offensive Power Rankings for 2008

Quote:
Originally Posted by nikeairmancurry View Post
This might be immpossible but our team wants to know the rankings for the best hyrid teams in the country..
Not possible from these stats, you'd need to consult teams that do lots of scouting.
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Unread 09-04-2008, 19:18
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Re: Offensive Power Rankings for 2008

I noticed some people were requesting DPR scores. While the meaning of a DPR number isn't as straightforward as OPR, I think we may be able to improve the OPR calculation by taking it into account. If a team tends to play heavy defense, the teams they play against shouldn't have their OPR reduced when they play below average. Plus I love linear algebra so this gave me an excuse to use it.

<complex math warning>

So here's the equation:

Code:
( M -N ) ( p ) = ( s_t )
( N -M ) ( d ) = ( s_o )
Where (n = total # of teams):
M = n x n matrix with M(ij) = # of times i played with j. M(ii) = # of times i played. (same as M from before)
N = n x n matrix with N(ij) = # of times i played against j. N(ii) = 0.
p = n x 1 column vector of OPRs. p(i) = OPR for team i. (same as p from before)
d = n x 1 column vector of DPRs. d(i) = DPR for team i.
s_t = n x 1 column vector of total scores. s_t(i) = Sum of all of team i's match scores. (same as s from before)
s_o = n x 1 column vector of total opponent scores. s_o(i) = Sum of all of team i's opponents' match scores.

In other words, the first n equations add all the offense played by team i's allies, subtracts all the defense played by team i's opponents, and equates that with team i's total score. The second n equations sums all the offense played by team i's opponents, subtracts all the defense played by team i's allies, and equates it with team i's opponents' total score.

We can rewrite the equation as Ax = y where A = (M -N; N -M), x = (p; d), and y = (s_t; s_o).

In the data set I used, there are 2 isolated sets of teams that played no matches with teams outside their set: the Israeli and non-Israeli teams. We can separate these sets and write an equation for each one, and I think it's easier if we do:

Code:
A_1 * x_1 = y_1
A_2 * x_2 = y_2
We can solve each equation completely independently, so I'm just going to focus on one equation and call it Ax = y. A has a null space of dimension 1 so it's not invertible. We can increase all the OPRs and DPRs by the same amount without having any effect on the scores, so the null space is the span of x = (1 1 1 ... 1). We can get a unique solution by adding one more equation. I (somewhat arbitrarily) chose the equation by saying: if there was no defense, scores would be 25% higher. In equation form that is:

Code:
M(11)*p(1) + M(22)*p(2) + ... + M(nn)*p(n) = 1.25 * (sum(s_t) / 3)
or
Code:
( E 0 ) ( p ) = 1.25 * (sum(s_t) / 3)
        ( d )

E = ( M(11) M(22) ... M(nn) )
You can tack the last equation onto the end of A like so:
Code:
A = ( M -N )
    ( N -M )
    ( E  0 )
And just ask Matlab to solve Ax = y for you. Or replace a random row in A with ( E 0 ) so A becomes invertible and solve x = A_inv * y.

</complex math warning>

I ran this against the first csv Greg posted and here are the results (top 50, ordered by OPR):

Code:
Team   OPR      DPR      OPR + DPR
1114   71.6377  0.9474   72.5852
1124   53.2773  15.2694  68.5467
2056   51.7991  6.8767   58.6759
217    51.6703  13.4995  65.1698
233    51.5346  11.4470  62.9816
39     51.0832  4.8484   55.9316
330    50.1059  0.1292   50.2351
525    50.0129  1.2725   51.2855
175    47.6712  11.6710  59.3422
40     46.3240  10.1761  56.5001
1731   46.1172  -0.0044  46.1128
987    45.9656  7.0006   52.9662
103    45.0985  10.9291  56.0276
191    44.6783  12.1649  56.8432
79     44.1938  6.1087   50.3025
1024   43.9389  5.5522   49.4911
16     43.2490  6.0931   49.3421
67     43.2308  11.1761  54.4070
20     42.3096  6.3370   48.6466
469    41.9469  -5.4304  36.5165
494    41.2950  -1.9009  39.3941
1806   40.8038  5.2194   46.0232
365    40.6742  2.5704   43.2446
47     40.3067  -1.6335  38.6732
148    39.3002  9.0662   48.3663
1493   39.0307  -0.9984  38.0323
383    38.9932  5.5749   44.5681
1625   38.8912  5.1900   44.0813
1519   38.8147  6.3316   45.1463
1126   38.6616  0.8032   39.4648
141    38.6570  7.6568   46.3137
1718   38.4372  3.7425   42.1797
663    38.2419  14.4349  52.6767
126    37.8160  6.3778   44.1938
121    37.7131  12.7949  50.5080
195    37.7043  -1.5850  36.1192
1477   37.4595  10.8694  48.3289
368    37.1072  -2.7458  34.3614
25     37.0417  -3.1295  33.9121
1717   36.5859  6.3136   42.8995
71     36.1253  8.4642   44.5895
836    35.9330  6.4528   42.3859
93     35.5093  1.6895   37.1989
69     35.3987  2.3330   37.7317
61     35.3566  5.7965   41.1532
968    34.7835  4.2148   38.9984
2345   34.6020  1.6196   36.2216
1086   34.5104  10.6218  45.1321
58     34.4129  6.7595   41.1725
935    34.3941  7.7033   42.0974
Compare this with Guy's results from the same dataset. The results are fairly similar, but there's definitely some movement in the rankings. Make of it what you will.

Personally, I don't think it tells you a whole lot to know a team's DPR. The two OPRs tell you slightly different things about a team. The old OPR tries to tell you how much a team actually scored each match. The new OPR tries to tell you how much a team could have scored each match if there was no defense. They are both potentially useful numbers.

Finally, knowing both OPR and DPR does allow you to better predict the score of a match. If you define error as:
Code:
 error = actual_red_score - ( p(red1) + p(red2) + p(red3) - d(blue1) - d(blue2) - d(blue3) )
then both methods are the least squares solution for their respective vector spaces, but method #2 has a lower MSE (mean square error) and ME (mean (abs) error) because it has a bigger vector space (less information loss).

Method #1 MSE = 245.0446, ME = 12.306
Method #2 MSE = 180.8867, ME = 10.514

So it's better at predicting past scores. Is it better at predicting future scores? I guess we'll see.
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Re: Offensive Power Rankings for 2008

Jay, this is some very inpressive Linear Algebra. This is pretty awesome!

Two thoughts: Would it be possible for you, at some point, to post all DPR rankings from the overall data set? Also, maybe something more interesting for the results, could you try and solve for which correction factor (in the arbitrarily chosen equation) makes for the lowest ME? Maybe that can make it even more vaulble of a tool. I'll also shoot you a PM with another idea I have to make OPR (and probably DPR) even more meaningful.
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Re: Offensive Power Rankings for 2008

Quote:
Originally Posted by Jay Lundy View Post
I noticed some people were requesting DPR scores. While the meaning of a DPR number isn't as straightforward as OPR, I think we may be able to improve the OPR calculation by taking it into account. If a team tends to play heavy defense, the teams they play against shouldn't have their OPR reduced when they play below average. Plus I love linear algebra so this gave me an excuse to use it.

<complex math warning>

So here's the equation:

Code:
( M -N ) ( p ) = ( s_t )
( N -M ) ( d ) = ( s_o )
Where (n = total # of teams):
M = n x n matrix with M(ij) = # of times i played with j. M(ii) = # of times i played. (same as M from before)
N = n x n matrix with N(ij) = # of times i played against j. N(ii) = 0.
p = n x 1 column vector of OPRs. p(i) = OPR for team i. (same as p from before)
d = n x 1 column vector of DPRs. d(i) = DPR for team i.
s_t = n x 1 column vector of total scores. s_t(i) = Sum of all of team i's match scores. (same as s from before)
s_o = n x 1 column vector of total opponent scores. s_o(i) = Sum of all of team i's opponents' match scores.

In other words, the first n equations add all the offense played by team i's allies, subtracts all the defense played by team i's opponents, and equates that with team i's total score. The second n equations sums all the offense played by team i's opponents, subtracts all the defense played by team i's allies, and equates it with team i's opponents' total score.

We can rewrite the equation as Ax = y where A = (M -N; N -M), x = (p; d), and y = (s_t; s_o).

In the data set I used, there are 2 isolated sets of teams that played no matches with teams outside their set: the Israeli and non-Israeli teams. We can separate these sets and write an equation for each one, and I think it's easier if we do:

Code:
A_1 * x_1 = y_1
A_2 * x_2 = y_2
We can solve each equation completely independently, so I'm just going to focus on one equation and call it Ax = y. A has a null space of dimension 1 so it's not invertible. We can increase all the OPRs and DPRs by the same amount without having any effect on the scores, so the null space is the span of x = (1 1 1 ... 1). We can get a unique solution by adding one more equation. I (somewhat arbitrarily) chose the equation by saying: if there was no defense, scores would be 25% higher. In equation form that is:

Code:
M(11)*p(1) + M(22)*p(2) + ... + M(nn)*p(n) = 1.25 * (sum(s_t) / 3)
or
Code:
( E 0 ) ( p ) = 1.25 * (sum(s_t) / 3)
        ( d )

E = ( M(11) M(22) ... M(nn) )
You can tack the last equation onto the end of A like so:
Code:
A = ( M -N )
    ( N -M )
    ( E  0 )
And just ask Matlab to solve Ax = y for you. Or replace a random row in A with ( E 0 ) so A becomes invertible and solve x = A_inv * y.

</complex math warning>

I ran this against the first csv Greg posted and here are the results (top 50, ordered by OPR):

Code:
Team   OPR      DPR      OPR + DPR
1114   71.6377  0.9474   72.5852
1124   53.2773  15.2694  68.5467
2056   51.7991  6.8767   58.6759
217    51.6703  13.4995  65.1698
233    51.5346  11.4470  62.9816
39     51.0832  4.8484   55.9316
330    50.1059  0.1292   50.2351
525    50.0129  1.2725   51.2855
175    47.6712  11.6710  59.3422
40     46.3240  10.1761  56.5001
1731   46.1172  -0.0044  46.1128
987    45.9656  7.0006   52.9662
103    45.0985  10.9291  56.0276
191    44.6783  12.1649  56.8432
79     44.1938  6.1087   50.3025
1024   43.9389  5.5522   49.4911
16     43.2490  6.0931   49.3421
67     43.2308  11.1761  54.4070
20     42.3096  6.3370   48.6466
469    41.9469  -5.4304  36.5165
494    41.2950  -1.9009  39.3941
1806   40.8038  5.2194   46.0232
365    40.6742  2.5704   43.2446
47     40.3067  -1.6335  38.6732
148    39.3002  9.0662   48.3663
1493   39.0307  -0.9984  38.0323
383    38.9932  5.5749   44.5681
1625   38.8912  5.1900   44.0813
1519   38.8147  6.3316   45.1463
1126   38.6616  0.8032   39.4648
141    38.6570  7.6568   46.3137
1718   38.4372  3.7425   42.1797
663    38.2419  14.4349  52.6767
126    37.8160  6.3778   44.1938
121    37.7131  12.7949  50.5080
195    37.7043  -1.5850  36.1192
1477   37.4595  10.8694  48.3289
368    37.1072  -2.7458  34.3614
25     37.0417  -3.1295  33.9121
1717   36.5859  6.3136   42.8995
71     36.1253  8.4642   44.5895
836    35.9330  6.4528   42.3859
93     35.5093  1.6895   37.1989
69     35.3987  2.3330   37.7317
61     35.3566  5.7965   41.1532
968    34.7835  4.2148   38.9984
2345   34.6020  1.6196   36.2216
1086   34.5104  10.6218  45.1321
58     34.4129  6.7595   41.1725
935    34.3941  7.7033   42.0974
Compare this with Guy's results from the same dataset. The results are fairly similar, but there's definitely some movement in the rankings. Make of it what you will.

Personally, I don't think it tells you a whole lot to know a team's DPR. The two OPRs tell you slightly different things about a team. The old OPR tries to tell you how much a team actually scored each match. The new OPR tries to tell you how much a team could have scored each match if there was no defense. They are both potentially useful numbers.

Finally, knowing both OPR and DPR does allow you to better predict the score of a match. If you define error as:
Code:
 error = actual_red_score - ( p(red1) + p(red2) + p(red3) - d(blue1) - d(blue2) - d(blue3) )
then both methods are the least squares solution for their respective vector spaces, but method #2 has a lower MSE (mean square error) and ME (mean (abs) error) because it has a bigger vector space (less information loss).

Method #1 MSE = 245.0446, ME = 12.306
Method #2 MSE = 180.8867, ME = 10.514

So it's better at predicting past scores. Is it better at predicting future scores? I guess we'll see.
I knew it was possible, finally somebody looked at it.
Thank You!
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Re: Offensive Power Rankings for 2008

Now that its all over, would anyone like to post OPR based on just matches at nationals (separately for each division). I am interested in observing how teams did at an individual regional compared to nationals.
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Re: Offensive Power Rankings for 2008

I would like to say -- I love numbers and data. I predicted the scores of all our qualification matches and ( other than the first match that had two of our robots quit ) the scores were within 10% most of the time -- very close !!! We also had the 15th highest OPR in Archimedes -- we ended up the 17th seed -- again very close. Based on the numbers, I said we would go 6-1 -- we were 5-2.

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Re: Offensive Power Rankings for 2008

Quote:
Originally Posted by DRH2o View Post
I would like to say -- I love numbers and data. I predicted the scores of all our qualification matches and ( other than the first match that had two of our robots quit ) the scores were within 10% most of the time -- very close !!! We also had the 15th highest OPR in Archimedes -- we ended up the 17th seed -- again very close. Based on the numbers, I said we would go 6-1 -- we were 5-2.

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I Dont think this has a good idea on what actually counts as reliable data.
We were 15th in Galileo in OPR, however, we dont have a hurdler. All of our points are in auto/laps/assists.
We thought we would go 5-2, but our matches didnt go as planned so we went 3-4.
I like the details of this and i really appreciate it, i have been looking for something this detailed.
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