#56 Outbreak (11-13)

avg: 849.83  •  sd: 53.88  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
46 Queen Cake Loss 9-11 821.76 Jun 15th ATL Classic 2019
64 Red Hot Ultimate Win 11-9 937.16 Jun 15th ATL Classic 2019
23 Tabby Rosa** Loss 5-13 895.62 Ignored Jun 15th ATL Classic 2019
57 Taco Truck Loss 7-11 376.34 Jun 15th ATL Classic 2019
29 Virginia Rebellion Loss 4-13 786.08 Jun 16th ATL Classic 2019
80 cATLanta Win 13-1 935.11 Jun 16th ATL Classic 2019
9 Traffic** Loss 2-15 1363.67 Ignored Jul 13th TCT Select Flight Invite West 2019
84 Cold Cuts Win 15-6 900.76 Jul 13th TCT Select Flight Invite West 2019
55 Crackle Win 11-7 1334.93 Jul 13th TCT Select Flight Invite West 2019
32 FAB Loss 5-13 691.9 Jul 14th TCT Select Flight Invite West 2019
49 Fiasco Loss 5-8 571.5 Jul 14th TCT Select Flight Invite West 2019
36 Seattle Soul Loss 7-13 671.35 Jul 14th TCT Select Flight Invite West 2019
88 Honey Pot** Win 13-3 835.43 Ignored Aug 10th HoDown ShowDown 23 GOAT
88 Honey Pot** Win 13-2 835.43 Ignored Aug 10th HoDown ShowDown 23 GOAT
57 Taco Truck Loss 10-13 515.09 Aug 10th HoDown ShowDown 23 GOAT
57 Taco Truck Win 12-11 968.23 Aug 10th HoDown ShowDown 23 GOAT
88 Honey Pot** Win 15-3 835.43 Ignored Sep 7th East Coast Womens Club Sectional Championship 2019
60 Huntsville Laika Win 11-7 1277.69 Sep 21st Southeast Club Womens Regional Championship 2019
7 Phoenix** Loss 1-11 1451.86 Ignored Sep 21st Southeast Club Womens Regional Championship 2019
57 Taco Truck Win 11-4 1443.23 Sep 21st Southeast Club Womens Regional Championship 2019
23 Tabby Rosa** Loss 4-11 895.62 Ignored Sep 21st Southeast Club Womens Regional Championship 2019
60 Huntsville Laika Loss 9-13 392.23 Sep 22nd Southeast Club Womens Regional Championship 2019
46 Queen Cake Loss 8-13 574.8 Sep 22nd Southeast Club Womens Regional Championship 2019
57 Taco Truck Win 12-11 968.23 Sep 22nd Southeast Club Womens Regional Championship 2019
**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)