#7 Phoenix (23-10)

avg: 2051.86  •  sd: 72.21  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
11 Wildfire Win 13-8 2363.16 Jul 13th TCT Pro Elite Challenge 2019
20 Grit Win 13-7 2165.59 Jul 13th TCT Pro Elite Challenge 2019
6 Scandal Loss 9-13 1758.03 Jul 13th TCT Pro Elite Challenge 2019
15 Ozone Win 13-10 2054.8 Jul 14th TCT Pro Elite Challenge 2019
4 Molly Brown Loss 7-13 1723.02 Jul 14th TCT Pro Elite Challenge 2019
13 Rival Win 13-9 2253.64 Jul 14th TCT Pro Elite Challenge 2019
14 Nemesis Win 13-6 2374.52 Jul 14th TCT Pro Elite Challenge 2019
20 Grit Win 15-6 2208.05 Aug 17th TCT Elite Select Challenge 2019
31 Heist** Win 15-4 1907.56 Ignored Aug 17th TCT Elite Select Challenge 2019
22 LOL Win 15-3 2129.16 Aug 17th TCT Elite Select Challenge 2019
11 Wildfire Loss 7-10 1477.34 Aug 18th TCT Elite Select Challenge 2019
21 BENT Win 12-9 1931.33 Aug 18th TCT Elite Select Challenge 2019
13 Rival Win 9-4 2435.07 Aug 18th TCT Elite Select Challenge 2019
1 Fury Loss 6-15 1841.46 Aug 31st TCT Pro Championships 2019
5 6ixers Win 11-8 2620.56 Aug 31st TCT Pro Championships 2019
2 Brute Squad Loss 13-14 2305.69 Aug 31st TCT Pro Championships 2019
10 Nightlock Loss 9-13 1539.07 Aug 31st TCT Pro Championships 2019
8 Schwa Win 13-9 2415.01 Sep 1st TCT Pro Championships 2019
13 Rival Win 14-6 2435.07 Sep 1st TCT Pro Championships 2019
57 Taco Truck** Win 15-4 1443.23 Ignored Sep 8th North Carolina Womens Club Sectional Championship 2019
60 Huntsville Laika** Win 11-2 1410.8 Ignored Sep 21st Southeast Club Womens Regional Championship 2019
23 Tabby Rosa Win 11-8 1861.23 Sep 21st Southeast Club Womens Regional Championship 2019
56 Outbreak** Win 11-1 1449.83 Ignored Sep 21st Southeast Club Womens Regional Championship 2019
57 Taco Truck** Win 11-2 1443.23 Ignored Sep 21st Southeast Club Womens Regional Championship 2019
30 Steel Win 13-6 1948.29 Sep 22nd Southeast Club Womens Regional Championship 2019
15 Ozone Win 13-8 2222.82 Sep 22nd Southeast Club Womens Regional Championship 2019
11 Wildfire Loss 9-14 1393.13 Oct 24th USA Ultimate National Championships 2019
12 Siege Win 15-10 2303.71 Oct 24th USA Ultimate National Championships 2019
4 Molly Brown Loss 10-15 1826.95 Oct 24th USA Ultimate National Championships 2019
2 Brute Squad Loss 8-15 1865.88 Oct 25th USA Ultimate National Championships 2019
6 Scandal Win 15-12 2477.09 Oct 25th USA Ultimate National Championships 2019
3 Seattle Riot Loss 10-15 1892.11 Oct 25th USA Ultimate National Championships 2019
9 Traffic Win 16-15 2088.67 Oct 26th 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)