#9 Nightlock (12-12)

avg: 1948.3  •  sd: 53.33  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
10 Traffic Loss 10-11 1812.23 Jun 22nd Eugene Summer Solstice 2019
30 Colorado Small Batch Win 8-7 1522.71 Jun 22nd Eugene Summer Solstice 2019
27 Elevate Win 13-4 2033.54 Jun 22nd Eugene Summer Solstice 2019
1 Fury Loss 6-13 1902.24 Jun 22nd Eugene Summer Solstice 2019
18 Underground Win 9-5 2225.54 Jun 23rd Eugene Summer Solstice 2019
1 Fury Loss 4-13 1902.24 Jun 23rd Eugene Summer Solstice 2019
34 PDXtra Win 13-3 1961.8 Jun 23rd Eugene Summer Solstice 2019
8 Schwa Win 10-8 2248.16 Jun 23rd Eugene Summer Solstice 2019
30 Colorado Small Batch Win 13-2 1997.71 Jul 13th TCT Pro Elite Challenge 2019
20 Pop Win 13-9 2051.94 Jul 13th TCT Pro Elite Challenge 2019
8 Schwa Loss 9-10 1860.49 Jul 13th TCT Pro Elite Challenge 2019
33 Heist Win 13-6 1971.72 Jul 14th TCT Pro Elite Challenge 2019
17 Showdown Loss 8-12 1286.08 Jul 14th TCT Pro Elite Challenge 2019
13 Ozone Win 12-11 2029.57 Jul 14th TCT Pro Elite Challenge 2019
8 Schwa Loss 8-15 1420.68 Aug 2nd 2019 US Open Club Championship
5 Scandal Loss 12-14 2033.67 Aug 2nd 2019 US Open Club Championship
15 Nemesis Win 15-8 2328.79 Aug 3rd 2019 US Open Club Championship
8 Schwa Loss 15-16 1860.49 Aug 4th 2019 US Open Club Championship
6 6ixers Loss 10-15 1785.76 Aug 31st TCT Pro Championships 2019
1 Fury Loss 4-15 1902.24 Aug 31st TCT Pro Championships 2019
7 Phoenix Win 13-9 2565.76 Aug 31st TCT Pro Championships 2019
4 Brute Squad Loss 7-15 1743.67 Sep 1st TCT Pro Championships 2019
12 Rival Loss 11-12 1788.04 Sep 1st TCT Pro Championships 2019
8 Schwa Win 11-8 2351.1 Sep 1st 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)