Easy to use Offensive Power Rankings (OPR) program for mid-regional scouting - Page 6

JesseK · #76 10-03-2009, 19:27

Quote:

At first I thought that it would be unsolvable (each equation has 6 unknowns, and you'd end up with 2N unknowns and only N equations), until today I had the fairly-obvious brainwave that each match would give you two equations (one OPRred - DPRblue = scorered for red, one OPRblue - DPRred = scoreblue for blue). This approach seems like it would be more predictive of robot performance than simply doing OPR and DPR separately*. I'm going to try to implement it tonight, but if someone wants to try it themselves and report back, that'd be great

You would want to have (blueScore = blueOPR + redDPR) and vice versa. Remember, you having a low DPR is better for you, hence you want your opponents to have a high DPR. If you subtract the opponent's DPR then you're lowering your own score just because the opponents are easier to score on ... which doesn't make sense when the opponent's DPR is positive (which is pretty bad).

Some simulation data to come tonight; I have an idea for a quick and dirty Match Prediction method.

<edit> Quick match prediction using the formula above for the DC Quarterfinals:

Code:

blueScore = blueOPR - redDPR:
Teams: (RED) 234, 45, & 620 vs. (BLUE) 1111, 122, 768
Predicted Scores: Red = 43, Blue = 6
Actual Scores: Red = 126, Blue = 100


blueScore = blueOPR + redDPR:
Teams: (RED) 234, 45, & 620 vs. (BLUE) 1111, 122, 768
Predicted Scores: RED=101 BLUE=80
Actual Scores: Red = 126, Blue = 100

Note that the prediction for their second match was identical even when the new match data was added, so predicting the outcome of the second QF match when the scores are that close is nearly impossible. Predictions also don't take into account adjustments for strategy, which is probably why it's best to leave the actual statistics for Qualification Matches only.

Ed Law · #77 10-03-2009, 21:52

Quote:

Originally Posted by JesseK

You would want to have (blueScore = blueOPR + redDPR) and vice versa. Remember, you having a low DPR is better for you, hence you want your opponents to have a high DPR. If you subtract the opponent's DPR then you're lowering your own score just because the opponents are easier to score on ... which doesn't make sense when the opponent's DPR is positive (which is pretty bad).

Some simulation data to come tonight; I have an idea for a quick and dirty Match Prediction method.

<edit> Quick match prediction using the formula above for the DC Quarterfinals:

Code:

blueScore = blueOPR - redDPR:
Teams: (RED) 234, 45, & 620 vs. (BLUE) 1111, 122, 768
Predicted Scores: Red = 43, Blue = 6
Actual Scores: Red = 126, Blue = 100


blueScore = blueOPR + redDPR:
Teams: (RED) 234, 45, & 620 vs. (BLUE) 1111, 122, 768
Predicted Scores: RED=101 BLUE=80
Actual Scores: Red = 126, Blue = 100

Note that the prediction for their second match was identical even when the new match data was added, so predicting the outcome of the second QF match when the scores are that close is nearly impossible. Predictions also don't take into account adjustments for strategy, which is probably why it's best to leave the actual statistics for Qualification Matches only.

Hi Jesse,

I think the general consensus in the CD community agrees that OPR should be calculated with qualifying matches only. The extra matches by some teams who made it to elimination round will bias the results if they are used.

Ed

DanDon · #78 10-03-2009, 22:44

Hey y'all...

Can anyone run the program for the NYC regional and post the results?
I reformatted and haven't gotten a chance to install VMWare Fusion on my mac again.

Thanks a bunch!
Dandon

Vikesrock · #79 10-03-2009, 22:50

Here's the NYC results sorted by +/-

Code:

New York City
New York City data retrieved from local HTML file
01.    Team 1155	W:6	L:1	OPR:40.13	DPR:7.71	PMR:32.43
02.    Team 0694	W:5	L:1	OPR:48.45	DPR:16.43	PMR:32.02
03.    Team 2753	W:5	L:2	OPR:40.41	DPR:10.78	PMR:29.63
04.    Team 2344	W:6	L:1	OPR:37.71	DPR:11.32	PMR:26.39
05.    Team 0354	W:5	L:2	OPR:30.65	DPR:5.93	PMR:24.72
06.    Team 0371	W:5	L:2	OPR:24.19	DPR:0.05	PMR:24.14
07.    Team 0271	W:6	L:1	OPR:33.62	DPR:10.62	PMR:23.00
08.    Team 0056	W:6	L:1	OPR:34.82	DPR:13.50	PMR:21.32
09.    Team 1396	W:5	L:2	OPR:16.90	DPR:-1.69	PMR:18.59
10.    Team 3059	W:4	L:3	OPR:16.46	DPR:-0.88	PMR:17.34
11.    Team 0395	W:4	L:2	OPR:34.57	DPR:17.66	PMR:16.90
12.    Team 1302	W:5	L:2	OPR:19.31	DPR:3.41	PMR:15.90
13.    Team 0335	W:5	L:2	OPR:19.48	DPR:3.61	PMR:15.87
14.    Team 0555	W:4	L:3	OPR:31.73	DPR:16.13	PMR:15.60
15.    Team 0743	W:3	L:4	OPR:28.15	DPR:13.62	PMR:14.53
16.    Team 2265	W:3	L:4	OPR:13.59	DPR:0.03	PMR:13.56
17.    Team 2933	W:4	L:3	OPR:27.06	DPR:14.36	PMR:12.70
18.    Team 1807	W:6	L:1	OPR:23.10	DPR:10.70	PMR:12.40
19.    Team 0358	W:2	L:5	OPR:9.30	DPR:-3.01	PMR:12.31
20.    Team 0237	W:5	L:2	OPR:27.48	DPR:16.54	PMR:10.93
21.    Team 0270	W:4	L:3	OPR:16.16	DPR:6.91	PMR:9.25
22.    Team 0806	W:5	L:2	OPR:16.18	DPR:7.07	PMR:9.12
23.    Team 0041	W:3	L:4	OPR:29.08	DPR:20.49	PMR:8.58
24.    Team 1660	W:6	L:1	OPR:25.19	DPR:16.74	PMR:8.45
25.    Team 3017	W:4	L:3	OPR:9.20	DPR:1.35	PMR:7.85
26.    Team 1796	W:4	L:3	OPR:24.28	DPR:17.44	PMR:6.84
27.    Team 1257	W:3	L:4	OPR:18.69	DPR:12.12	PMR:6.57
28.    Team 1211	W:5	L:2	OPR:14.03	DPR:7.46	PMR:6.57
29.    Team 2681	W:5	L:2	OPR:14.22	DPR:8.45	PMR:5.77
30.    Team 0375	W:7	L:0	OPR:28.39	DPR:22.82	PMR:5.57
31.    Team 1881	W:3	L:4	OPR:14.22	DPR:10.59	PMR:3.62
32.    Team 0527	W:4	L:3	OPR:16.86	DPR:13.26	PMR:3.60
33.    Team 1237	W:2	L:4	OPR:12.71	DPR:9.29	PMR:3.42
34.    Team 1156	W:3	L:4	OPR:12.07	DPR:9.84	PMR:2.23
35.    Team 2285	W:3	L:3	OPR:23.47	DPR:21.81	PMR:1.66
36.    Team 2601	W:3	L:4	OPR:18.98	DPR:17.41	PMR:1.57
37.    Team 2070	W:3	L:4	OPR:7.30	DPR:6.57	PMR:0.73
38.    Team 0640	W:2	L:5	OPR:8.66	DPR:11.47	PMR:-2.81
39.    Team 1230	W:5	L:2	OPR:11.28	DPR:15.17	PMR:-3.89
40.    Team 0369	W:5	L:2	OPR:9.11	DPR:13.57	PMR:-4.45
41.    Team 2579	W:3	L:4	OPR:5.34	DPR:12.70	PMR:-7.36
42.    Team 1862	W:2	L:4	OPR:-0.63	DPR:6.93	PMR:-7.56
43.    Team 0333	W:2	L:5	OPR:7.73	DPR:16.31	PMR:-8.58
44.    Team 1563	W:1	L:6	OPR:22.48	DPR:31.97	PMR:-9.50
45.    Team 2577	W:3	L:4	OPR:20.45	DPR:30.12	PMR:-9.67
46.    Team 3111	W:2	L:4	OPR:12.75	DPR:22.44	PMR:-9.69
47.    Team 1989	W:3	L:4	OPR:2.76	DPR:13.64	PMR:-10.88
48.    Team 1600	W:3	L:4	OPR:11.61	DPR:23.95	PMR:-12.34
49.    Team 1520	W:1	L:6	OPR:2.07	DPR:14.43	PMR:-12.37
50.    Team 2205	W:2	L:5	OPR:11.63	DPR:24.03	PMR:-12.40
51.    Team 0329	W:4	L:3	OPR:-3.86	DPR:9.24	PMR:-13.10
52.    Team 2573	W:2	L:5	OPR:6.82	DPR:19.95	PMR:-13.13
53.    Team 1880	W:0	L:7	OPR:3.65	DPR:19.53	PMR:-15.88
54.    Team 0263	W:2	L:5	OPR:20.96	DPR:37.03	PMR:-16.07
55.    Team 3053	W:2	L:5	OPR:9.44	DPR:26.20	PMR:-16.76
56.    Team 1340	W:1	L:6	OPR:10.14	DPR:28.07	PMR:-17.93
57.    Team 2554	W:3	L:4	OPR:4.92	DPR:24.93	PMR:-20.00
58.    Team 0759	W:4	L:3	OPR:2.52	DPR:22.78	PMR:-20.26
59.    Team 0380	W:2	L:5	OPR:0.99	DPR:21.58	PMR:-20.59
60.    Team 0421	W:5	L:2	OPR:1.94	DPR:25.20	PMR:-23.26
61.    Team 3004	W:1	L:6	OPR:6.56	DPR:30.20	PMR:-23.65
62.    Team 1635	W:2	L:5	OPR:8.29	DPR:35.37	PMR:-27.08
63.    Team 2895	W:3	L:4	OPR:2.33	DPR:31.59	PMR:-29.26
64.    Team 3112	W:1	L:6	OPR:0.70	DPR:33.29	PMR:-32.59
65.    Team 0334	W:1	L:6	OPR:-6.96	DPR:32.80	PMR:-39.76
66.    Team 1698	W:0	L:7	OPR:-1.64	DPR:39.21	PMR:-40.86

Manoel · #80 10-03-2009, 23:24

Quote:

Originally Posted by Bongle

I just had an idea, which might resolve to CCWM/PMR, but I don't think it will.

It ends up with a 2N x 2N matrix (for a regional with N teams)

sum[team's scores] = weighted sum of OPRs of alliance members minus weighted sum of DPRs of opponents.

At first I thought that it would be unsolvable (each equation has 6 unknowns, and you'd end up with 2N unknowns and only N equations), until today I had the fairly-obvious brainwave that each match would give you two equations (one OPR_red - DPR_blue = score_red for red, one OPR_blue - DPR_red = score_blue for blue). This approach seems like it would be more predictive of robot performance than simply doing OPR and DPR separately*. I'm going to try to implement it tonight, but if someone wants to try it themselves and report back, that'd be great.

*The problem with the current approach to OPR and DPR, especially in a game like lunacy, is that they assume that a alliance's score comes ENTIRELY from its teams' offensive powers, or ENTIRELY from its opponent's lack of mobility. This proposed new equation seems like it would balance the two, and hopefully give more accurate results.

I gave it a shot. I'm not sure I implemented it correctly, but matrix A (Ax = b) always comes up with a large conditioning number - nearly singular, which means that small changes in b, or the matches results, would result in huge shifts in OPR/DPR for teams.

Anyway, here are my results for the NY Regional:

OPR:

Code:

            1         1155       32.277
            2          694       31.639
            3           56       29.308
            4          395       24.951
            5         3059       23.105
            6         2344       22.331
            7         1600       22.075
            8          743       21.927
            9         2753       21.897
           10         1257       18.944
           11         1302       16.873
           12         2554       16.547
           13          335       13.228
           14          237       11.405
           15         1796       10.521
           16         1660       10.011
           17         1156       9.9718
           18         3004        9.263
           19         1881       9.2177
           20         2681       8.3132
           21         1862       7.8608
           22         2265        6.898
           23          375       6.6255
           24          270       6.5267
           25         1807       6.2078
           26         1230        5.148
           27         1396       4.5395
           28          527       2.2106
           29          371       1.8905
           30          555       1.7473
           31           41      0.72257
           32          354       0.4532
           33          271     -0.89414
           34         2601      -1.8199
           35          421       -2.437
           36          369      -2.7544
           37         2933      -4.2083
           38         1340      -4.8236
           39         1880      -5.2459
           40          358      -5.4201
           41          263      -6.2038
           42         1563       -6.389
           43         2285      -7.0976
           44         3111      -8.2768
           45         2577      -8.5896
           46          806      -10.413
           47          333      -10.934
           48         3053      -11.506
           49          640      -11.515
           50         1520       -12.58
           51          759      -12.655
           52         2895      -13.538
           53         1237      -13.924
           54         1989      -14.043
           55         2070      -14.541
           56         3017      -14.922
           57         2573       -15.48
           58         1698      -16.094
           59         2205      -16.962
           60         2579      -17.403
           61         1635      -17.776
           62         1211      -18.224
           63          380      -19.378
           64          329      -20.404
           65         3112      -22.538
           66          334      -32.883

DPR:
(and note that now this is how many points on average your presence on an alliance takes points away from the opponent - a negative number indicates you're likely to increase your opponent's score)

Code:

            1         1862       19.379
            2          640        12.14
            3         1155       10.869
            4         1257       8.4759
            5         1396       8.1218
            6         1807       4.3868
            7           56       1.2983
            8         1600      0.60363
            9         2070      0.29453
           10          395     -0.96611
           11         2601       -1.051
           12         1237      -1.2811
           13          270      -1.4666
           14          371      -1.8914
           15         1520      -1.9435
           16         3059      -4.2095
           17         1156      -4.3151
           18         1302      -4.5479
           19         2265      -5.5535
           20         2554      -6.1296
           21          358      -7.8337
           22          380       -9.532
           23         3004      -9.8214
           24          237       -10.27
           25         2344      -10.662
           26          329      -13.279
           27         2753      -13.602
           28         1340      -14.952
           29         2933      -15.027
           30         1230      -15.604
           31         1880      -15.936
           32         3017      -16.672
           33          333      -17.101
           34          527      -17.291
           35         2681      -17.463
           36         1660      -17.746
           37         1989       -18.37
           38          421      -20.028
           39          335      -20.761
           40         1796      -21.286
           41         1881      -21.618
           42          743      -21.675
           43          806      -22.481
           44         2577      -23.904
           45          759      -25.582
           46          271      -25.939
           47         3111      -26.223
           48         1211        -26.5
           49          694       -26.53
           50          369      -26.648
           51          263      -26.796
           52         2895      -27.583
           53         3053      -28.046
           54          354      -28.105
           55           41      -29.584
           56         1563      -30.406
           57         3112      -31.288
           58         2573      -32.228
           59         1698       -32.62
           60          375       -32.85
           61          334      -33.248
           62         1635      -33.382
           63         2205      -33.423
           64          555      -36.653
           65         2579      -37.541
           66         2285      -45.508

+/-:
(just occurred me to calculate that, haven't given it much thought but I don't think it indicates anything)

Code:

            1          694       58.169
            2          743       43.602
            3          375       39.475
            4         2285       38.411
            5          555       38.401
            6         2753       35.499
            7          335       33.989
            8         2344       32.993
            9         1796       31.807
           10         1881       30.836
           11           41       30.307
           12          354       28.558
           13           56       28.009
           14         1660       27.757
           15         3059       27.315
           16          395       25.917
           17         2681       25.776
           18          271       25.045
           19         1563       24.017
           20          369       23.894
           21         2554       22.677
           22          237       21.675
           23         1600       21.471
           24         1302       21.421
           25         1155       21.408
           26         1230       20.752
           27          263       20.592
           28         2579       20.139
           29          527       19.501
           30         3004       19.084
           31         3111       17.946
           32          421       17.591
           33         2573       16.748
           34         3053        16.54
           35         1698       16.526
           36         2205        16.46
           37         1635       15.606
           38         2577       15.314
           39         1156       14.287
           40         2895       14.045
           41          759       12.927
           42         2265       12.451
           43          806       12.069
           44         2933       10.819
           45         1880        10.69
           46         1257       10.468
           47         1340       10.128
           48         3112       8.7504
           49         1211       8.2755
           50          270       7.9933
           51          333       6.1671
           52         1989       4.3263
           53          371       3.7819
           54          358       2.4136
           55         1807        1.821
           56         3017       1.7499
           57          334      0.36525
           58         2601     -0.76893
           59         1396      -3.5823
           60          329      -7.1252
           61          380       -9.846
           62         1520      -10.636
           63         1862      -11.518
           64         1237      -12.643
           65         2070      -14.836
           66          640      -23.656

I don't have prediction software implemented yet, so I can't give any estimates, so... Any ideas? Considering the matrix is ill-conditioned, I'm not sure those numbers can be trusted. It could be that my MATLAB implementation is wrong, so here's the code if anyone wants to check it out.

SteveGPage · #81 11-03-2009, 15:15

Quote:

Originally Posted by Manoel

I gave it a shot. I'm not sure I implemented it correctly, but matrix A (Ax = b) always comes up with a large conditioning number - nearly singular, which means that small changes in b, or the matches results, would result in huge shifts in OPR/DPR for teams.

(snip)

I don't have prediction software implemented yet, so I can't give any estimates, so... Any ideas? Considering the matrix is ill-conditioned, I'm not sure those numbers can be trusted. It could be that my MATLAB implementation is wrong, so here's the code if anyone wants to check it out.

I think that you are on to something, and I do think that the +/- calculation can and would be important. I've been struggling with the following equation, trying to figure out how to implement it and do the calculation, so let me know what you all think. It is similar to Boggle's concept above, and may be essentially the same result, but can't figure out how to parse the data to actually make it happen, especially since it creates a 2N x 2N matrix...

Two equations:
1. (Xioblue + Xjoblue + Xkoblue) - (Xldred + Xmdred + Xndred) = Scoreblue
2. (Xlored + Xmored + Xnored) - (Xidblue + Xjdblue + Xkdblue) = Scorered

Where ...
............X
..............io (Team i - OPR)
................blue (Alliance)

and likewise, id would represent Team i - DPR.
You would have to solve for both the OPR and DPR for each team.

For the DC regional, which had 65 teams, it would be a 130x130 matrix * {x} = Bio or Bid, which would allow you to then solve for the separate OPR and DPR values. I don't have a good way to parse this, much less see if the results actually mean anything, but the concept came from a friend of mine - Kenneth Massey, who developed one of the BCS algorithms (www.masseyratings.com) so I think it has merit.

For the DC regional, I did the following simple calculation to try and get a similar estimated value for how tough the match was:

AVG[((TotalMatchPoints/TotalAlliancePoints)*TeamOffensivePoints) + (TeamOffensivePoints – TeamDefensivePoints)]

MATCH_ID ALLIANCE_ID TEAM_ID OFF_POINTS DEF_POINTS
.....1................1................1.......... ...20...............10
.....1................1................2.......... ...10...............10
.....1................1................3.......... ...15................5
.....1................2................4.......... ....0................10
.....1................2................5.......... ...10...............20
.....1................2................6.......... ...20...............20

Example:
Total Match Points = SUM(OFF_POINTS), GROUP BY MATCH_ID
Total Alliance Points = SUM(OFF_POINTS), GROUP BY MATCH_ID, ALLIANCE_ID

Team 1
((75/45)*20 + (20 – 10)) = 43.33

Team 2
16.66

Team 3
35

Team 4
((75/30)*0 + (0 – 10)) = -10

Team 5
15

Team 6
50

Alliance 1 43.33 + 16.66 + 35 = 95
Alliance 2 (-10) + 15 + 50 = 55

Alliance 1/Alliance 2 = 1.72 – Alliance 1 72% stronger

Manoel · #82 11-03-2009, 23:32

Quote:

Originally Posted by SteveGPage

I think that you are on to something, and I do think that the +/- calculation can and would be important. I've been struggling with the following equation, trying to figure out how to implement it and do the calculation, so let me know what you all think. It is similar to Boggle's concept above, and may be essentially the same result, but can't figure out how to parse the data to actually make it happen, especially since it creates a 2N x 2N matrix...

Two equations:
1. (Xioblue + Xjoblue + Xkoblue) - (Xldred + Xmdred + Xndred) = Scoreblue
2. (Xlored + Xmored + Xnored) - (Xidblue + Xjdblue + Xkdblue) = Scorered

Where ...
............X
..............io (Team i - OPR)
................blue (Alliance)

and likewise, id would represent Team i - DPR.
You would have to solve for both the OPR and DPR for each team.

OK, so either your idea is identical to Bongle's, or I misunderstood him, because what you explained above is exactly what I implemented. Bongle's been away from the topic for a while, hopefully he'll see this and let us know if that's what he meant.

Bongle · #83 12-03-2009, 08:14

Quote:

Originally Posted by Manoel

OK, so either your idea is identical to Bongle's, or I misunderstood him, because what you explained above is exactly what I implemented. Bongle's been away from the topic for a while, hopefully he'll see this and let us know if that's what he meant.

Steve's idea appears to be the same as mine, or at least his two equations are. I tried to implement it on tuesday, but it had bugs and I haven't had to time to get back to it. It is unfortunate that the matrix comes out as somewhat ill-conditioned, but I guess that isn't that terribly surprising since the amount of data we're processing is fairly small.

I'll try again to implement this new matrix tonight, and I'll plug it into my prediction code to see if it is any better than the more basic OPR/DPR stats.

We need to come up with a name for this more-complex stat: how about OPR+ and DPR+?

SteveGPage · #84 12-03-2009, 10:18

Quote:

Originally Posted by Bongle

Steve's idea appears to be the same as mine, or at least his two equations are. I tried to implement it on tuesday, but it had bugs and I haven't had to time to get back to it. It is unfortunate that the matrix comes out as somewhat ill-conditioned, but I guess that isn't that terribly surprising since the amount of data we're processing is fairly small.

I'll try again to implement this new matrix tonight, and I'll plug it into my prediction code to see if it is any better than the more basic OPR/DPR stats.

We need to come up with a name for this more-complex stat: how about OPR+ and DPR+?

I figured as much, and am glad you have had more success in implementing it than I have! I tried to get my son, a senior in college - Comp Sci/Math Major to help me ... but "NO" he didn't have time to help me with my "homework'!

I like the idea of OPR+ and DPR+ to indicate the additional complexity of the stat. I'd love to see what you are able to do, as well as how predictive the resulting values are. Thanks for all your hard work and your efforts! If there is anything I can do to assist or push this along, just let me know!

Steve

Jared Russell · #85 12-03-2009, 10:31

This is starting to get seriously cool guys...the Sabremetrician in me is very happy.

How about a VORR stat (Value over replacement robot)?

DISS (Defense independent scoring statistics)?

Pythagorean W-L expectations?

Win shares?

The possibilities are endless...

SteveGPage · #86 12-03-2009, 10:34

Quote:

Originally Posted by Bongle

Steve's idea appears to be the same as mine, or at least his two equations are. I tried to implement it on tuesday, but it had bugs and I haven't had to time to get back to it. It is unfortunate that the matrix comes out as somewhat ill-conditioned, but I guess that isn't that terribly surprising since the amount of data we're processing is fairly small.

I'll try again to implement this new matrix tonight, and I'll plug it into my prediction code to see if it is any better than the more basic OPR/DPR stats.

We need to come up with a name for this more-complex stat: how about OPR+ and DPR+?

One other thing, then I'll go back to my day job ... When I was talking to Kenneth Massey (the BCS guy) he also suggested something, that would modify these equations just a bit - but would require team by team scouting. The red or blue score would NOT be the final score as published, but would first, obviously, not include penalties - so we are really measuring "capabilities" not just performance; and secondly, if you were to have added MR to your own alliance - so as to avoid <G22>, the points that you scored on a alliance partner would be removed from the opposing alliance's score. How much of an effect would this cause in the long run, I don't think much - but if we are basing the OPR+ and DPR+ on a limited number of matches, then it could very likely be significant.

Steve

Bongle · #87 12-03-2009, 11:22

Quote:

Originally Posted by SteveGPage

One other thing, then I'll go back to my day job ... When I was talking to Kenneth Massey (the BCS guy) he also suggested something,

A good idea (it would certainly increase accuracy), but my goal (for my program, at least), is to try and provide the most accurate scouting-independent ranking possible using only match data.

As for implementation of OPR+/DPR+, how I arranged my matrix was:

Odd Rows (for row i): "Team i/2's total score is equal to the weighted sum of the team i/2's alliance OPRs minus the weighted sum of team i/2's opponents' DPRs"

Constant: team i/2's total score
Coefficients: Alternating
- Odd columns (col j): # of times team i/2 played with team j/2 (positive)
- Even columns (col j): # of times team i/2 played against team j/2 (negative)

Even Rows (for row i): "Team i's opponents' score is equal to the weighted sum of the opponents' alliance OPRs minus the weighted sum of team i's alliance's DPRs"

Constant: team i/2's opponents' total score
Coefficients: Alternating
- Odd columns (col j): # of times team i/2 played against team j/2 (positive)
- Even columns (col j): # of times team i/2 played with team j/2 (negative)

For verification, I'm pretty sure that the sum of the absolute values of each row should equal the number of matches that team played times 6.

SteveGPage · #88 12-03-2009, 16:45

Quote:

Originally Posted by Bongle

A good idea (it would certainly increase accuracy), but my goal (for my program, at least), is to try and provide the most accurate scouting-independent ranking possible using only match data.

As for implementation of OPR+/DPR+, how I arranged my matrix was:

...

I think that is a great goal to have that level of information! Certainly, we will continue to do scouting, and would never expect a particular algorithm to tell us who or who not to select. This is a tool to use in that over all process. My scouts will continue to do pit scouting, as well as individual match scouting, and I think we will be looking at the OPR+/DPR+ type of values to help us find those teams down in the noise of the competition, that may have a very low ranking, but have the qualities we are looking for in an alliance.

I would love to see how you finally implement this, since it is questionable that I will be able to put in my database between now and next weekend, especially since I am using a datawarehouse and a Business Intelligence tool to create the reports. The data interface is still lagging!

The methodology you described is exactly what I was looking doing as well, and I believe it has merit! Good luck with getting it debugged! Good luck at your competitions!

If anyone else is working on these concepts, and will be at the Chesapeake Regional next week, I would love to chat with you there!

Steve

SteveGPage · #89 13-03-2009, 08:59

Quote:

Originally Posted by Bongle

...
I'll try again to implement this new matrix tonight, and I'll plug it into my prediction code to see if it is any better than the more basic OPR/DPR stats.

...

Just heard my Comp Sci/Math Major son is coming home for the weekend. He was excited to hear what we were working on and wants to help me get something up and running this weekend. If you would like, we could work on any issues you may be having and perhaps get something up to the CD community prior to next week's competitions.

Steve

Killraine · #90 13-03-2009, 12:09

Quote:

Originally Posted by SteveGPage

Just heard my Comp Sci/Math Major son is coming home for the weekend. He was excited to hear what we were working on and wants to help me get something up and running this weekend. If you would like, we could work on any issues you may be having and perhaps get something up to the CD community prior to next week's competitions.

Steve

So in time for Chesapeake?

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