#5 6ixers (18-6)

avg: 2254.95  •  sd: 76.32  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
27 Wicked** Win 13-5 2001.32 Ignored Jul 13th TCT Pro Elite Challenge 2019
13 Rival Win 13-8 2331.23 Jul 13th TCT Pro Elite Challenge 2019
15 Ozone Win 13-11 1955.5 Jul 13th TCT Pro Elite Challenge 2019
17 Showdown Win 13-7 2237.74 Jul 14th TCT Pro Elite Challenge 2019
6 Scandal Loss 9-12 1831.24 Jul 14th TCT Pro Elite Challenge 2019
8 Schwa Win 13-9 2415.01 Jul 14th TCT Pro Elite Challenge 2019
7 Phoenix Loss 8-11 1686.25 Aug 31st TCT Pro Championships 2019
2 Brute Squad Win 12-9 2776.06 Aug 31st TCT Pro Championships 2019
10 Nightlock Win 15-10 2411.24 Aug 31st TCT Pro Championships 2019
1 Fury Loss 4-15 1841.46 Sep 1st TCT Pro Championships 2019
6 Scandal Win 14-12 2397.56 Sep 1st TCT Pro Championships 2019
4 Molly Brown Loss 11-12 2155.55 Sep 1st TCT Pro Championships 2019
45 Vice** Win 13-0 1673.1 Ignored Sep 21st Northeast Club Womens Regional Championship 2019
100 DINO** Win 13-0 414.72 Ignored Sep 21st Northeast Club Womens Regional Championship 2019
38 Rebel Rebel** Win 13-5 1797.47 Ignored Sep 21st Northeast Club Womens Regional Championship 2019
12 Siege Win 15-11 2231.27 Sep 21st Northeast Club Womens Regional Championship 2019
2 Brute Squad Win 14-12 2651.65 Sep 22nd Northeast Club Womens Regional Championship 2019
8 Schwa Win 15-11 2377.6 Oct 24th USA Ultimate National Championships 2019
16 Pop Win 11-5 2280.7 Oct 24th USA Ultimate National Championships 2019
2 Brute Squad Loss 10-15 1977.09 Oct 24th USA Ultimate National Championships 2019
14 Nemesis Win 15-9 2290 Oct 25th USA Ultimate National Championships 2019
3 Seattle Riot Win 14-12 2566.67 Oct 25th USA Ultimate National Championships 2019
8 Schwa Win 15-8 2561.25 Oct 26th USA Ultimate National Championships 2019
2 Brute Squad Loss 7-15 1830.69 Oct 27th USA Ultimate National Championships 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)