#7 Ozone (22-10)

avg: 1936.53  •  sd: 147.84  •  top 16/20: 95%

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# Opponent Result Game Rating Status Date Event
37 Fiasco** Win 13-1 1696.58 Ignored Jun 16th ATL Classic 2018
- cATLanta** Win 13-0 397.15 Ignored Jun 16th ATL Classic 2018
27 Tabby Rosa Win 13-6 1981.55 Jun 16th ATL Classic 2018
37 Fiasco** Win 13-2 1696.58 Ignored Jun 17th ATL Classic 2018
- Orbit** Win 13-0 -202.85 Ignored Jun 17th ATL Classic 2018
27 Tabby Rosa Win 13-5 1981.55 Jun 17th ATL Classic 2018
10 Nemesis Win 14-8 2360.52 Aug 3rd 2018 US Open Club Championships
11 Rival Win 15-5 2404.18 Aug 3rd 2018 US Open Club Championships
1 Brute Squad Loss 10-15 2009.07 Aug 3rd 2018 US Open Club Championships
19 Pop Win 14-13 1804.32 Aug 4th 2018 US Open Club Championships
5 Scandal Loss 8-12 1661.94 Aug 4th 2018 US Open Club Championships
8 Nightlock Loss 13-14 1795.39 Aug 4th 2018 US Open Club Championships
10 Nemesis Loss 10-12 1586.36 Aug 5th 2018 US Open Club Championships
22 BENT Win 15-9 2095.19 Sep 1st TCT Pro Championships 2018
5 Scandal Loss 10-15 1649.49 Sep 1st TCT Pro Championships 2018
35 Hot Metal** Win 15-3 1737.96 Ignored Sep 1st TCT Pro Championships 2018
16 Heist Win 15-10 2175.68 Sep 2nd TCT Pro Championships 2018
21 Siege Win 15-5 2199.64 Sep 2nd TCT Pro Championships 2018
1 Brute Squad Loss 7-15 1862.67 Sep 2nd TCT Pro Championships 2018
73 Honey Pot** Win 13-1 849.43 Ignored Sep 22nd Southeast Womens Regional Championship 2018
45 Outbreak** Win 13-4 1503.78 Ignored Sep 22nd Southeast Womens Regional Championship 2018
27 Tabby Rosa Win 13-4 1981.55 Sep 22nd Southeast Womens Regional Championship 2018
59 Queen Cake** Win 13-3 1214.29 Ignored Sep 22nd Southeast Womens Regional Championship 2018
37 Fiasco Win 15-8 1661.39 Sep 23rd Southeast Womens Regional Championship 2018
18 Phoenix Win 15-7 2297.67 Sep 23rd Southeast Womens Regional Championship 2018
19 Pop Win 14-11 1992.66 Oct 18th USA Ultimate National Championships 2018
11 Rival Win 15-8 2368.99 Oct 18th USA Ultimate National Championships 2018
4 Seattle Riot Loss 10-15 1810.17 Oct 18th USA Ultimate National Championships 2018
12 Traffic Win 15-14 1894.07 Oct 19th USA Ultimate National Championships 2018
3 Molly Brown Loss 9-15 1758.09 Oct 19th USA Ultimate National Championships 2018
6 6ixers Loss 11-15 1635.19 Oct 19th USA Ultimate National Championships 2018
9 Schwa Loss 14-15 1712.97 Oct 20th USA Ultimate National Championships 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)