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#31
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Re: Week3 cumulative Twitter stats & OPRs
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#33
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Re: Week3 cumulative Twitter stats & OPRs
No, I am reading it correctly. The threaded view is a hierarchy. The intent is to show which post you are responding to.
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If you look at the threaded view, you will see this post linked to your post in the hierarchy, even though your post is not the most recent one as I am typing this. The way to get the links correct is to use the "Reply with quote" or "Quick reply to this message" buttons on the post to which you are responding. Last edited by Ether : 16-03-2014 at 21:28. |
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#34
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Re: Week3 cumulative Twitter stats & OPRs
I can assure you that the algorithm I use to compute the L2 norm of the simple linear combination of team scores (what we're calling OPR) is correct.
But that leaves two questions: 1) What is the "correct" data to use, and 2) Is the L2 norm of a linear combination of team scores the "correct" algorithm to get the most meaningful and useful metric? Question 2 has been discussed in various threads here on CD in the past. I won't beat that horse here. Question 1 is especially problematic this year because of the high value and erratic enforcement of fouls (I am not blaming the refs: this is a difficult game to ref and score). To get a truer measure of performance arguably requires that the foul points be removed from the score before computing the OPR. The problem is, you can't do this with the official data. You need to use the Twitter data to remove the foul points. Ed Law maintains a spreadsheet in which he makes every effort to "repair" the Twitter data whenever possible and deciding when and when not to integrate it with the official FIRST Match Results and Team Standings data. It's apparently a labor of love for Ed and he spends many hours getting it right, for which we are in his debt. If I tried to do that, I'd probably introduce more errors than I corrected. So if you're looking for the "official" OPR, rather than the "correct" OPR, I would say that Ed Law's spreadsheet is the de facto standard. |
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#35
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Re: Week3 cumulative Twitter stats & OPRs
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I suspect the break around zero occurs due to penalties. All the matches with negative win margins were decided by penalties points and the number of matches where this occurs is a small subset of all the data. All the data with positive win margins include matches with no penalties, offsetting penalties or matches where the penalties did not affect the final outcome. Mike |
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#36
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Re: Week3 cumulative Twitter stats & OPRs
Correct.
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I think Mark's question was why the curve changes so abruptly at the Y axis. I've been trying to come up with a good intuitive way to explain it. Any takers? Last edited by Ether : 16-03-2014 at 22:47. |
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#37
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Re: Week3 cumulative Twitter stats & OPRs
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#38
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Re: Week3 cumulative Twitter stats & OPRs
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The numbers are calculated correctly. However: 1) that spreadsheet was prepared from Twitter data from Friday night before you had finished quals (on Saturday), and 2) Twitter contained only 5 of the 8 matches you played on Friday. For such a small data sample size, the OPR may be misleading. To answer your questions: Column B is the "standard" OPR, based on final score (which includes awarded foul points). Column E is unpenalized CCWM... "unpenalized" in this context meaning the awarded foul points are removed from the final score, and CCWM meaning the opposing alliance's score (with awarded foul points removed) is subtracted from your alliance's score for each match. Many folks think removing the foul points gives a better metric for this year's game because of the high value and erratic enforcement* of penalties. Note: AIUI, Ed does not use the Twitter data to remove foul points if it is not complete for that event. So you won't see unpenalized CCWM for MOKC in his spreadsheet. update: Quote:
* I am not faulting the refs. This is a very difficult game to ref and score. Last edited by Ether : 16-03-2014 at 23:42. |
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#39
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Re: Week3 cumulative Twitter stats & OPRs
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We're not seeing a break so much as a graph representing two populations of different sizes. Everything to the right of the Y-axis shows the fairly expected distribution of winning margins. This represents just under 90% of all matches. The data to the left of the Y-axis shows a similar albeit reflected pattern. It is scaled down in frequency since it comes from the ~11% of matches that would have a different winner without the penalties. There are some other effects due to penalties being larger and more quantized than the point value of scoring objectives, but the main cause is due to sub-population size differences. |
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#40
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Re: Week3 cumulative Twitter stats & OPRs
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In the meantime, can you overplot this graph with the penalized version? And (separately) the histogram of penalties? |
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#41
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Re: Week3 cumulative Twitter stats & OPRs
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In our "less normal" world, we probably have a large skew to the left - the winning margin is far more likely to be small than large. But, I would expect it to still be more or less continuous (as you point out, there are quantization effects because of the scoring objectives... just as scores of say 4 or 5 in football are unlikely). I would expect the penalty point distribution to be continuous, as well, but with even larger gaps between likely values. When the penalties are subtracted from the penalized score, I expect the resulting distribution to be continuous. The jump right at zero is not expected. I'll withhold my tin foil hat theories as to why this is until I can take a look at Ester's raw data. |
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#42
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Re: Week3 cumulative Twitter stats & OPRs
Mike,
Thank you for a better explanation. Perhaps a different way to look this graph is to plot the data as two separate sets. The first being all matches where there were no foul points. The second with the matches that had foul points. Mike |
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#43
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Re: Week3 cumulative Twitter stats & OPRs
Twitter winning margin histograms As requested, attached is an Excel XLS spreadsheet using non-tied qual matches from Twitter data 3/16 16:44:46. Included are histograms of frequency (counts) versus: wm: winning margin (with awarded foul points included)All the necessary raw data is in the spreadsheet, as well as the derived data and the formulas used to compute it. You can play around with it to see if there's a better way to present it. Other than removing ties, I made no further effort to modify the Twitter data. The usual Twitter data caveats apply. Last edited by Ether : 17-03-2014 at 16:16. |
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#44
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Re: Week3 cumulative Twitter stats & OPRs
Ether, maybe I'm just blind as a bat (likely) but have you posted raw data for twitter? I'm trying to avoid going to the effort of scraping it if I can.
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#45
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Re: Week3 cumulative Twitter stats & OPRs
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Thank you, Brandon ![]() Here's an XLS version with practice matches removed Last edited by Ether : 19-03-2014 at 17:44. |
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