#37 Fiasco (10-10)

avg: 1096.58  •  sd: 95.13  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
7 Ozone** Loss 1-13 1336.53 Ignored Jun 16th ATL Classic 2018
27 Tabby Rosa Loss 9-13 962.98 Jun 16th ATL Classic 2018
- cATLanta** Win 13-2 397.15 Ignored Jun 16th ATL Classic 2018
45 Outbreak Win 13-6 1503.78 Jun 17th ATL Classic 2018
7 Ozone** Loss 2-13 1336.53 Ignored Jun 17th ATL Classic 2018
40 Steel Loss 9-11 791.79 Jun 17th ATL Classic 2018
75 Autonomous Win 13-6 735.98 Aug 4th Heavyweights 2018
- Frenzy** Win 13-1 600 Ignored Aug 4th Heavyweights 2018
50 Cold Cuts Win 12-6 1397.58 Aug 4th Heavyweights 2018
34 Dish Loss 7-12 640.29 Aug 4th Heavyweights 2018
70 Lady Forward Win 10-5 951.85 Aug 5th Heavyweights 2018
16 Heist** Loss 5-12 1122.08 Ignored Aug 5th Heavyweights 2018
58 Stellar Win 10-9 788.29 Aug 5th Heavyweights 2018
27 Tabby Rosa Loss 11-13 1152.71 Sep 8th Florida Womens Sectional Championship 2018
18 Phoenix Loss 9-15 1182.19 Sep 22nd Southeast Womens Regional Championship 2018
40 Steel Win 13-11 1269.84 Sep 22nd Southeast Womens Regional Championship 2018
63 Taco Truck Win 15-4 1166.83 Sep 22nd Southeast Womens Regional Championship 2018
7 Ozone Loss 8-15 1371.73 Sep 23rd Southeast Womens Regional Championship 2018
45 Outbreak Loss 8-10 641.11 Sep 23rd Southeast Womens Regional Championship 2018
40 Steel Win 12-7 1561.51 Sep 23rd Southeast Womens Regional Championship 2018
**Blowout Eligible

FAQ

The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)