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DCI 2015 Predictions Using Machine Learning


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I love the data analysis. However, it is very difficult to analyze an entirely subjective score as a trend :(

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There you go, throwing around the F word like it is nothing...I'm aghast!

I will try and refrain from using the F bomb on this or any thread in the future, c mor.

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I had to look up that word, that's a new one for me. All models have the chance to be wrong, this one for 2015 rankings predictions included for sure. For sure, the ultimate experience is seeing the corps give their all at finals and finding out which corps is crowned the champion.

I hear you... I'm only foolin' around. I give you credit for taking your time and energy to put this model together. I tried to make sense of your modeling, but to be honest with you, it sailed far and wide, completely way,.. WAY .. over my head. But if its methodology makes sense to you, and others, thats fine too. Besides, this model can't be any worse than the meager results I've gotten over the years peering into my now old and worn crystal ball.

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I love the data analysis. However, it is very difficult to analyze an entirely subjective score as a trend :(

100% agree. That is for sure the most problematic part underlying the modeling.

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  • 2 weeks later...

I'm curious to see how your models would have predicted with the rest of the competitions included all the way up through semi finals.

Would it have predicted the way it turned out tonight?

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I'm curious to see how your models would have predicted with the rest of the competitions included all the way up through semi finals.

Would it have predicted the way it turned out tonight?

I ran the models tonight. Here are the results:

 rank                corps NN LM  GBM H20 optim  score  optim     NN     LM    GBM    H20
    1          Blue Devils  2  2  2.0   2     2 0.9765 0.9676 0.9687 0.9687 0.9696 0.9670
    2       Carolina Crown  1  1  1.0   1     1 0.9708 0.9693 0.9716 0.9713 0.9732 0.9682
    3            Bluecoats  3  3  3.0   3     3 0.9692 0.9604 0.9606 0.9603 0.9588 0.9605
    4           The Cadets  4  4  4.0   4     4 0.9590 0.9523 0.9585 0.9582 0.9547 0.9501
    5 Santa Clara Vanguard  5  5  5.0   5     5 0.9385 0.9357 0.9358 0.9353 0.9342 0.9358
    6         Blue Knights  6  6  6.0   6     6 0.9185 0.9053 0.9091 0.9088 0.9027 0.9044
    7     Phantom Regiment  7  7  7.0   7     7 0.9032 0.8905 0.8954 0.8951 0.8923 0.8888
    8       Madison Scouts  8  8  9.0   9     9 0.8875 0.8768 0.8819 0.8818 0.8738 0.8755
    9        The Cavaliers  9  9  8.0   8     8 0.8832 0.8778 0.8801 0.8789 0.8765 0.8774
   10     Boston Crusaders 10 10 11.5  10    10 0.8680 0.8581 0.8576 0.8563 0.8550 0.8587
   11           Blue Stars 11 11 10.0  12    11 0.8515 0.8440 0.8463 0.8474 0.8551 0.8420
   12             Crossmen 12 12 11.5  11    12 0.8503 0.8426 0.8322 0.8324 0.8550 0.8444

These results rely on all scores through semis, with the 1/2 point penalty for the Blue Devils added back in to their Thursday show score.

(That is the right thing to do, since trying to reckon for penalties introduces unnecessary noise into the modeling process.) The columns

names optim refer to the R function optim rank-loss weightings of the 4 models to predict the actual final rankings, whereas the 4 models

are built to predict the finals scores for each drum corps.

Based on all the data through semifinals, relying on the models built on the 600 data points of the top 12 corps from 2005-2014, Carolina Crown

had the statistical advantage going into finals with a predicted winning margin of 0.15 points (rounding down the optim difference).

Note the huge score jump for the Bluecoats. For drum corps from position #6 to #12, note that all had scores ~+1 pts. over expected -

and remember, these models factor in any finals jumps in scores from past years' data. From semis to finals scoring, it appears to me

to be an unprecedented increase in scores for all those drum corps.

If we credit Clara for 1/2 point of GE, that would give the Blue Devils a 0.35 winning margin based on the predicte values above. Even now,

after seeing semis and finals for myself, I can't quite grasp the 0.575 margin of victory, in absolute terms having seen the shows and in relative

terms thinking about the progression of scores from 3 weeks out, 9 days out, then Thursday/Friday/Saturday.

2015.semis.cv.lm.png

2015.semis.gbm.varimp.png

Edited by dalyea
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Thanks for this.

I also thought the scoring for Finals night was a bigger jump than normal. Considering that scores have been general low overall this year, I think DCI may have felt pressure to bring scores back up. Nobody wants to see someone win with a 96 or less.

Maybe I'm getting off topic now, but...

I think DCI had the judges depress the scoring for this year to raise the bar for 99pts, because of what BD did last Finals. They did need to raise that thresh-hold. However, I do think the winning corps this season was worthy of a 97.65 but no more. Some may say "raw numbers are meaningless, it's the spreads that matter", but according to the judging sheet criteriae, a 98+ is not the same as a 97, and I do think the top 4 shows this season general deserved the raw scores they got, and can be compared to the scores they got last season.

BD this season were pretty much 2pts behind themselves last year. Same for everyone else.

I'm not sure how you would put this kind of info into your program, or if this is even relevant.

What I would REALLY like to see is the predictions for scores based on certain judges. What would Finals scores have been, say, if the same judging panel from Thursday or Friday judged Saturday also? What would the computer predict then?

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I think DCI had the judges depress the scoring for this year to raise the bar for 99pts, because of what BD did last Finals. They did need to raise that thresh-hold. However, I do think the winning corps this season was worthy of a 97.65 but no more.

You really can't have both of these. Are the scores depressed, or do they actually represent the performance of the corps? Choose one...

(FWIW I think they were a bit depressed, but I didn't mind)

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