#3 Molly Brown (17-3)

avg: 2349.43  •  sd: 112.72  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
62 Trainwreck** Win 13-1 1369.67 Ignored Jun 22nd Fort Collins Summer Solstice 2019
28 Wicked** Win 13-5 2010.01 Ignored Jun 22nd Fort Collins Summer Solstice 2019
67 Jackwagon** Win 14-4 1310.59 Ignored Jun 22nd Fort Collins Summer Solstice 2019
28 Wicked Win 10-6 1906.17 Jun 23rd Fort Collins Summer Solstice 2019
55 Dish** Win 13-3 1475.68 Ignored Jun 23rd Fort Collins Summer Solstice 2019
67 Jackwagon** Win 13-3 1310.59 Ignored Jun 23rd Fort Collins Summer Solstice 2019
105 Colorado Cutthroat: Youth Club U-20 Girls** Win 13-0 600 Ignored Jun 23rd Fort Collins Summer Solstice 2019
33 Heist** Win 13-2 1971.72 Ignored Jul 13th TCT Pro Elite Challenge 2019
17 Showdown Win 13-9 2145.8 Jul 13th TCT Pro Elite Challenge 2019
15 Nemesis Win 13-6 2363.98 Jul 13th TCT Pro Elite Challenge 2019
7 Phoenix Win 13-7 2704.73 Jul 14th TCT Pro Elite Challenge 2019
8 Schwa Win 11-10 2110.49 Jul 14th TCT Pro Elite Challenge 2019
5 Scandal Loss 11-12 2129.63 Jul 14th TCT Pro Elite Challenge 2019
1 Fury Loss 10-15 2048.63 Aug 2nd 2019 US Open Club Championship
8 Schwa Win 15-10 2439.1 Aug 31st TCT Pro Championships 2019
2 Seattle Riot Win 13-10 2741.53 Aug 31st TCT Pro Championships 2019
12 Rival Win 15-8 2477.85 Aug 31st TCT Pro Championships 2019
6 6ixers Win 12-11 2364.36 Sep 1st TCT Pro Championships 2019
5 Scandal Win 13-12 2379.63 Sep 1st TCT Pro Championships 2019
1 Fury Loss 11-12 2377.24 Sep 2nd TCT Pro 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)