Offensive Power Rankings for 2008

[MasterChief 573]

Originally Posted by Bongle

these are ranked off of a team's last regional performance, like people requested.

Oh thats it, so the first one was an overall OPR and the last one was based only on a teams last performances? Gotcha.

[AndyB]

My data was determined using actual scouting. Scouting was done by 2194 and 171. I have the data for every team if you want me to compile it all. This is only for Friday at Milwaukee for comparison.

Kind of interesting to see the differences.

Power Rankings

1625	1	45.6544
141	1	37.3371
1730	1	31.3317
2169	1	27.3694
2062	1	25.2449
1736	1	20.5779
1816	1	19.864
1675	1	19.2832
2547	1	18.3416
2549	1	17.2544
93	1	17.011
171	1	16.9353
930	1	16.7177
2606	1	16.5577
2077	1	16.0525
2153	1	15.7331
1850	1	15.6312
1091	1	15.5336
2202	1	14.8521
2481	1	14.6515
2545	1	14.5534
2526	1	13.1104
2437	1	12.7125
1716	1	11.9898
537	1	11.9816
166	1	10.9945
2574	1	10.0294
904	1	9.62609
2039	1	9.34348
1259	1	9.1616
2506	1	8.72875
167	1	8.64531
2667	1	8.18725
2535	1	7.59668
2530	1	7.49738
269	1	7.30741
1714	1	7.01325
857	1	6.86925
2116	1	6.45649
1103	1	5.9275
1732	1	5.82385
1652	1	5.42679
2129	1	4.6678
74	1	3.92383
2143	1	3.64548
2538	1	2.9253
2136	1	2.26655
2220	1	1.04619
1306	1	0.442787
1739	1	0.0543406
1988	1	-0.00043864
1864	1	-0.086347
1984	1	-0.339205
2194	1	-0.386904
754	1	-1.22019
81	1	-1.43283
706	1	-1.65319
2586	1	-2.85733
1268	1	-6.65054
2561	1	-11.5318

Actual

Team      Avg Pts/Match
2062      35.67
171       35.67
93        32.67
1625      32.40
1730      29.00
141       28.67
2169      27.00
930       23.20
2545      23.00
1732      21.60
1816      21.33
2039      18.00
166       17.00
1736      15.67
1259      15.60
2481      15.00
1652      14.67
2606      14.40
2077      14.00
2530      14.00
2202      13.20
2129      12.80
2194      12.80

[Joe Ross]

Can you post San Diego?

[JB987]

Nice job on the stats all of you who have been working the numbers...a thought has occurred regarding use of last regional numbers to rank teams-a stated presumption of best performance then if multiple regionals were entered doesn't take into account factors like some regionals with a higher proportion of rookie teams (translating often into higher scores by veteran teams) which was seen in Hawaii last week and Okla and Minn. earlier. Also, increasing levels of defense have shown up in some of the latest regionals as the game evolves (watch footage from LVR). Seems like an average of all performances is more representative...and the likely data source my team will use for some assistance with early scouting.

[Greg Marra]

Too bad FIRST doesn't report penalties. I wonder how those are affecting these results.

[AndyB]

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.

[XaulZan11]

Like 171/2194, 1732 recorded the amount of points a team scored per match at Wisconsin. We kept track of lines and balls knocked down in hybrid, laps, hurdlers, herds and balls placed at the end.

I ran a correlation test to see how related the two sets of data are and see how good the OPR is at predicting how many points a team scores per match. There are some assumptions/problems. First, our scouting data isn't perfect so there is some error from that. Secondly, our scouting data doesn't include penalties, but the ORP does account for them. So, when doing a linear regression, I got an R value of .7841 and an R-squared value of .6149. (A perfect relationship would have an R value of -1 or 1 and no relationship would be 0). So, while it is not pefect (not surprising) the ORP is a fairly good predictor of a team's preformance.

[AndyB]

Originally Posted by XaulZan11

Like 171/2194, 1732 recorded the amount of points a team scored per match at Wisconsin. We kept track of lines and balls knocked down in hybrid, laps, hurdlers, herds and balls placed at the end.

I ran a correlation test to see how related the two sets of data are and see how good the OPR is at predicting how many points a team scores per match. There are some assumptions/problems. First, our scouting data isn't perfect so there is some error from that. Secondly, our scouting data doesn't include penalties, but the ORP does account for them. So, when doing a linear regression, I got an R value of .7841 and an R-squared value of .6149. (A perfect relationship would have an R value of -1 or 1 and no relationship would be 0). So, while it is not pefect (not surprising) the ORP is a fairly good predictor of a team's preformance.

I agree. The OPR does do a pretty good job. Of course, hard data is nice too, but I find it really awesome that you can figure this much out with just match scores.

[John Wanninger]

Originally Posted by AndyB

My data was determined using actual scouting. Scouting was done by 2194 and 171. I have the data for every team if you want me to compile it all. This is only for Friday at Milwaukee for comparison.

Kind of interesting to see the differences.

One reason for Offensive Power Rankings not matching reality is due to the fact that OPR’s are based on the averages of partners’ OPRs rather than actual contributions. Any deviation of a team from its ‘average’ contribution in a given match will always erroneously drive their partners’ OPR up or down.

These deviations can be large; it would not be unusual for a team to contribute 20 points in one match and then perhaps due to penalties, “contribute” -20 in the next. Given enough matches one might expect these errors to cancel each other out. However, when there is a limited number of matches (think: 8 coin tosses don’t always produce 4 heads and 4 tails) and the high degree of volatility, the error can be significant.

So while the OPR may be a good predictor, there's still no substitute for accurate, detailed scouting!

[AndyB]

Originally Posted by John Wanninger

One reason for Offensive Power Rankings not matching reality is due to the fact that OPR’s are based on the averages of partners’ OPRs rather than actual contributions. Any deviation of a team from its ‘average’ contribution in a given match will always erroneously drive their partners’ OPR up or down.

These deviations can be large; it would not be unusual for a team to contribute 20 points in one match and then perhaps due to penalties, “contribute” -20 in the next. Given enough matches one might expect these errors to cancel each other out. However, when there is a limited number of matches (think: 8 coin tosses don’t always produce 4 heads and 4 tails) and the high degree of volatility, the error can be significant.

So while the OPR may be a good predictor, there's still no substitute for accurate, detailed scouting!

Spot on John. Congrats on the Woodie btw. You deserve it. Hope to catch up with you in Atlanta.

[Eugene Fang]

I asked this question earlier, but no one replied to it:

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?

-also-

if three very very good teams were in the same round on the same team at the same time, wouldnt it also get skewed?

thanks for any input, because the OPR isnt even close to our average point contribution to our allinace that i calculated by hand after watching our regional videos, and our team hopes that teams at ATL won't rely on the OPR greatly for scouting, as it may negatively influence their decisions for alliance parings with our team, as well as many others. once again, thanks!

[Guy Davidson]

Of course that many things could skew the nubers. However, the law of large numbers still applies: the more and more samples you take, the more the number you're sampling appraoch its true value. So even if one matches is skewed, the average if your ten matches, or maybe more, will still be close to the expect numbers.

I think relying on any one tool too much to make a decision is a bad idea. Look, for example at what NFL teams do before they draft someone: watch game video, look at game statistics, look at the player during workouts, look at iq tests, personal intrviews, and more. Similarly, the best scouring teams in Atlanta will combine several different measurements and inputs to make their lists. I imagine they will go around talking to teams and getting an impression of their strategy, gameplanning, and experience. I bet they will have people recording matches and looking at what each team does, driver skill, performance, etc. I would guess they also watch some teams' recorded matches, especially from eliminations, to see how those teams fair when it's make-it or break-it time. A combination of these different tools is what will probably lead the eventual winning alliance captain to compile its alliance.

[DRH2o]

Off subject a little--Is there a csv with all the teams and their addresses that I can get my hands on. I would love for my Geospatial Tech class to use the opr data and do some geocoding to look at trends.

[Tom Bottiglieri]

Can someone post CT?

[DRH2o]

I would like to see VCU