New FRC Stat: aWAR

First, I’d like to recognize Dan Niemitalo for being the one to create most of this spreadsheet for a statistic that he invented, called Performance Index. Here is where he introduces that: http://www.chiefdelphi.com/forums/showthread.php?t=121979&highlight=performance+index. Thanks also go to Ed Law and Mark McLeod as all the raw data from this spreadsheet comes from their spreadsheets.

My goal for aWAR is to be a season-long statistic that accurately ranks all FRC teams based on all-around performance… It is primarily based off of the new standardized district point model, but also implements the tweaks mentioned in the bullets below (devised and implemented by Dan Niemitalo and I). aWAR stands for aggregate Wins Above Replacement and is based on the concept of WAR from baseball… Wins Above Replacement.

What is really unique about aWAR is that it is scaled to a useful range… Similar to the concept of a “Replacement-Level” found in WAR in baseball, 0 indicates a “replacement-level team” which we defined to be one that goes something like 5-7, is on the cusp of being picked in elims (hence replacement-level), and may or may not win an award. Based on this 0-point, it is then scaled so that the top teams usually get an aWAR of around 7 (12-0 record… 7 Wins Above Replacement). A team that has an aWAR of about 4 probably went about 9-3 at the regional/district level.

The changes made from the 2014 district point model include:

  • Omission of the 1st and 2nd year team benefit. This is in no way because I dislike this element of the point system (I actually like it a lot!), but because it exists primarily to help younger teams get to the next level of competition. With aWAR I’m just trying to rank teams based on skill, this element would be counter-productive for that.
  • Includes a weight for OPR (primarily to differentiate among top-tier teams… is: 16 * IndividualTeam’sEventOPR/MaxOPRofSeason, thus being comparable in significance to alliance selection)
  • Pro-rates for 12 qual matches so that variation due to # of qual matches is reduced (thus a team that goes 6-2 in 8 qual matches would be treated as if it went 9-3 in 12 qual matches).
  • Includes essentially 3 events: a team’s best two regional/district events and the average of their performance at DCMP and CMP events.
  • Multiplies points earned at DCMP and CMP by 1.7x so that mid-tier teams that do well at the regional/district level but struggle at higher levels aren’t punished for having competed against stronger teams.
  • Uses a weighted average of the (up to) 3 events, helping teams that have proved consistency by competing at a particular level over multiple events. This weight is: 1x for one event, 1.11x for two events, and 1.18x for three events. I, personally, really like how this balance works out in the rankings.
  • Includes another 10 points for Einstein Finalist and 20 points for Einstein Winner (these are then multiplied by the DCMP/CMP 1.7x multiplier)
  • Based on the concept of replacement-level, subtracts out 12 points (putting the replacement-level team at 0; teams above that level would have a positive aWAR while teams below that level would have a negative aWAR).
  • Then scales the raw points to fit a max of about 7 by dividing the “subtotal” by 15.
  • For multi-season aWAR, the aWAR from the past 4 seasons are averaged, with higher weight being put on more recent seasons (32%, 29%, 23%, 16%).

I hope to update this spreadsheet weekly to include 2014 data… I’ll need to add the infrastructure to do this, and then bring in data from Ed Law’s spreadsheet every week. I also hope to include rWAR (robot Wins Above Replacement), which would include only robot-performance-related data (i.e. few or no awards)… I doubt that will happen soon, though!

The spreadsheet should be attached… it’s a massive 30+ mb though, so don’t expect a speedy download! :slight_smile: There are three tabs that you’re intended to use (or tinker with)… The “Team Lookup” can be used just to look up one team at a time. The “aWAR” tab has the aWAR calculations from the past 6 years along with the Weighted Average of aWAR as of 2013. Feel free to sort by anything and/or filter by states/regions/countries! Lastly, you can use the “Point System” tab to tweak the system in various ways and see what happens! I would recommend if you’re trying to really see the dynamics of the system, pick a region and season you’re familiar with, and sort… watch what happens at each level of the rankings (but especially top and middle levels).

Please feel free to provide feedback… While I do generally like how aWAR is calculated currently, I would definitely be interested in improving it! If you find bugs, improvements, or recommendations, please mention it!

I tried putting it up in CD-Media under white papers, but that was over an hour ago with no result yet… hopefully that’ll come through eventually. :slight_smile: So for now at least I started this thread and uploaded a copy of the spreadsheet here: https://app.box.com/s/l6f9tgciw929415om2z0.

Top-50 by 4-year Weighted Average of aWAR:
1 1114 7.08
2 2056 7.03
3 469 6.78
4 987 6.67
5 254 6.50
6 67 6.28
7 33 5.95
8 148 5.90
9 118 5.83
10 1986 5.75
11 359 5.66
12 1717 5.62
13 341 5.50
14 1676 5.40
15 111 5.33
16 16 5.30
17 1983 5.18
18 233 5.07
19 2054 5.03
20 610 4.94
21 1538 4.94
22 1918 4.90
23 1477 4.85
24 217 4.81
25 2169 4.81
26 1718 4.66
27 2826 4.61
28 27 4.60
29 1519 4.53
30 2415 4.52
31 525 4.51
32 2016 4.48
33 330 4.40
34 365 4.38
35 1218 4.37
36 234 4.31
37 2590 4.23
38 245 4.21
39 2337 4.20
40 11 4.19
41 1678 4.18
42 25 4.16
43 180 4.10
44 103 4.03
45 1241 3.99
46 973 3.99
47 624 3.96
48 2471 3.92
49 2474 3.80
50 3138 3.80

Top 50 from 2013, by aWAR:
1 469 7.41
2 1986 7.11
3 1538 7.04
4 1114 6.91
5 33 6.78
6 987 6.72
7 2056 6.63
8 2169 6.50
9 118 6.41
10 1983 6.35
11 148 6.32
12 610 6.26
13 862 6.17
14 254 6.12
15 1477 6.09
16 2590 6.08
17 2054 5.90
18 359 5.83
19 1241 5.75
20 67 5.69
21 11 5.64
22 3476 5.59
23 1718 5.53
24 868 5.49
25 1717 5.47
26 1678 5.46
27 1676 5.44
28 20 5.41
29 128 5.41
30 4814 5.29
31 2415 5.29
32 341 5.12
33 2474 5.07
34 1918 5.07
35 3539 5.06
36 245 5.02
37 2052 4.98
38 2468 4.89
39 2959 4.89
40 1519 4.84
41 948 4.83
42 2729 4.83
43 126 4.81
44 234 4.79
45 1806 4.76
46 3997 4.76
47 1334 4.75
48 27 4.75
49 3824 4.72
50 3990 4.67

What methods did you use to come up with the various scaling factors applied?

e; Got it working

Download worked fine for me.

The link worked for me, but I had to download the spreadsheet.

Very cool stat and database. Just by looking at a few teams’ year to year aWAR, it seems to be a very good indicator of performance.

And, I’m glad you only did the past four seasons so you didn’t include our 08 and 09 robot. :slight_smile:

I didn’t have any special methods for coming up with the scaling factors… just closely examining specific samples (NH and New England in particular years, occasionally the global set) to try to see how they impact the ranking from top to bottom. Some of the scaling factors I studied more (# of events; 1.7x for CMPs) than others (/15 to scale the top teams to have aWAR of about 7; *16 for OPR).

To all, please do investigate what you think of the various factors… I’d like to get them “right!”

I (and others) have downloaded it… let me know if it continues to not work.

Hah, I am too… our team got significantly better from 2009 to 2010! :slight_smile:

Regardless of personal preference, I think 4 years makes sense in a lot of ways… it’s a cycle of HS students and provides significant history without going so far back that it stops being relevant. That said, I think 3 years may make more sense as a predictor, as the 4th year (that you’re predicting) would still have many of the same students from the last two or three. Doing only 2 or 3 years would also help the rookie or up-and-coming teams who are hurt by the length of 4 or more year averages…

Our decrease from 2011 to 2012 just goes to show what happens when you graduate off 75% of your team in one year. :wink:

going to go through it this weekend and see what shakes out. I find it humorous that the file that is 32 megs is called “slimdown” :slight_smile:

Afternoon,

We are very interested in looking at your spreadsheet but when I follow the link to Box there is a message that says:

We’re sorry, this file could not be opened. It might be password protected. :confused:

I created a Box account and still cannot download the file. Suggestions please?

Click the button that says download. Box is trying to preview the file and it’s too large to preview, which is what the message should be says.

Nice, I like and appreciate your efforts to come up with a ‘better way’. And, I am astounded that 1676 is ranked #14 over 4 years.

Considering on your explanation (thanks for that) I don’t understand how you might treat a 3rd event, such as a 3rd district or regional, before DCMP.

I already tried…*both *buttons the one underneath the file *and *the one in the top right corner…

I noticed an interesting thing in the team lookup form, there are several teams (just from the few that I checked) where their Championship stats (OPR, rank, win/loss record, etc.) is a direct duplicate of their State/Regional Championship stats. For example, team 469, their Archimedes division stats are a direct duplicate of their MSC stats. The same is true for team 33, team 217, and team 245, but not for ALL MSC teams… I’m not sure if I see a pattern, but I’m really confused. Is this some element of the system that I’m not understanding?

Hands down, this is the coolest thing I’ve seen all season.

As someone who loves ranking everything, this rankings list is like candy to me.

Unless I’m mistaken, couldn’t the aWAR of 4 be a 4-8 team that won Chairmans award, though? I’d be curious to see the rankings based only on on-field preformance.

I find the yearly ranking element interesting:
(32%, 29%, 23%, 16%).

On simialr efforts, using a fraction to the exponent of years produces a neat result:
For instance 1/2^year would be (1/2, 1/4, 1/8, 1/16 or 0.50, 0.25, 0.13, 0.06)… This weighting tends to favor last year’s performance heavily with a quick roll-off on history. The neat thing about it is you can use all of history and still not hit 1.0. The bad thing about it is that past 4 years has very little impact.

Using (2/3)^year gets 0.667, 0.444, 0.296, 0.198 which normalized to a sum of 1 would be 0.417, 0.275, 0.185, 0.116. Using this algorithm tends to favor teams with longevity ans consistent high performance, but can also keep a team in the spot-light possibley a year or two after their prime if they have a very strong legacy.

A correlation study would be interesting to dig into.

Finally got it. Turned out it was my school firewall blocking the download with no popup warning.

Changed to a different computer not behind the firewall and it worked. Although one more thing I discovered for anyone else who may be having trouble, the page did not want to open in Chrome but opened immediately in Explorer…how’s that for weird? :]

Any chance of saving a copy as .xls (2003 era)? It’s too big for Google Docs, and I won’t have access to newer Excel until Monday :stuck_out_tongue:

I did a quick linear regression using the aWAR data from 2008-2012 to predict aWAR in 2013. The R^2 was 0.50.

	Coefficients	Standard Error	t Stat		P-value
Intrcpt	0.3038132	0.052869809	5.746440231	1.23044E-08
2008	-0.000281711	0.032586229	-0.008645104	0.993104123
2009	0.070683871	0.031397538	2.251255225	0.02459942
2010	0.058408153	0.034406836	1.697574055	0.089918829
2011	0.250272535	0.035138483	7.12246277	2.10545E-12
2012	0.427316251	0.033320186	12.82454585	8.15505E-35

This shows that data from 5 years ago is not statistically significant, and 3-4 years old is minimally significant.

Thanks! I’d appreciate having more eyes on it! Yeah, the “slimdown” doesn’t drop that much file size… it does drop the extra functionality to easily rank different events that Dan Niemitalo had made… I hadn’t yet adapted all the formulas to work with aWAR so I dropped it for now. I would like to add it back in though, as it is really cool!

Thanks for pointing out the bug… there’s probably an issue with the formulas somewhere. Feel free to look into it yourself… I will later too tough.

Thanks! :slight_smile:

Yes, that could be… I don’t think winning Chairmans would have that much of an impact… but I do agree that it would be interesting to have a robot-only version. That was my original goal but it got tabled… the intention was that it would be called rWAR, though. I’ll add it in before long… it’s an easy-enough addition.

Agreed that the correlation study is worthwhile… thanks Joe Ross!

It opened for me in Chrome… maybe not as slow though? Glad you ended up getting it downloaded!

I can do that… maybe tomorrow.

This would probably argue for only doing the past three years… so maybe we should drop it to 3. I’d be curious if 2009 or 2010 are outliers though… given how much of a drop there is from 2011 to 2010. I have some more thoughts related to this correlation study… I’ll comment more when I have time later.

Practice 'bot to finish… :slight_smile:

If I’m interpreting your P-value results correctly, 2008 and 2010 aWARs were really bad predictors of 2013 success, while 2009 aWARs were reasonably good? From the games, that’s pretty much the opposite of what I’d expect. Or am I misinterpreting your stat program’s outputs?