#19 Voodoo (16-8)

avg: 1735.56  •  sd: 75.19  •  top 16/20: 18.1%

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# Opponent Result Game Rating Status Date Event
72 Sawtooth Win 13-6 1788.67 Jun 22nd Eugene Summer Solstice 2019
136 Syndicate Win 13-6 1456.08 Jun 22nd Eugene Summer Solstice 2019
65 Dohrk Stor Win 13-9 1663.82 Jun 22nd Eugene Summer Solstice 2019
110 Oregon Eruption!** Win 13-2 1589 Ignored Jun 22nd Eugene Summer Solstice 2019
13 Furious George Loss 6-15 1279.83 Jun 23rd Eugene Summer Solstice 2019
40 Blackfish Win 15-8 1977.57 Jun 23rd Eugene Summer Solstice 2019
45 Red Dawn Win 15-9 1893.81 Jun 23rd Eugene Summer Solstice 2019
16 Chain Lightning Loss 7-13 1279.44 Jul 13th TCT Pro Elite Challenge 2019
7 Chicago Machine Loss 9-11 1759.77 Jul 13th TCT Pro Elite Challenge 2019
5 Revolver Loss 11-13 1880.79 Jul 13th TCT Pro Elite Challenge 2019
39 Inception Win 13-9 1833.26 Jul 14th TCT Pro Elite Challenge 2019
11 Johnny Bravo Loss 10-11 1773.7 Jul 14th TCT Pro Elite Challenge 2019
32 Prairie Fire Win 13-7 2071.64 Jul 14th TCT Pro Elite Challenge 2019
39 Inception Loss 13-15 1200.52 Aug 17th TCT Elite Select Challenge 2019
21 Brickyard Win 15-11 2068.96 Aug 17th TCT Elite Select Challenge 2019
12 Pittsburgh Temper Loss 6-15 1289.86 Aug 17th TCT Elite Select Challenge 2019
20 Yogosbo Win 13-8 2229.19 Aug 18th TCT Elite Select Challenge 2019
30 Black Market I Loss 4-10 935.38 Aug 18th TCT Elite Select Challenge 2019
32 Prairie Fire Win 10-5 2088 Aug 18th TCT Elite Select Challenge 2019
83 Seattle Blacklist** Win 15-5 1726.02 Ignored Sep 7th Washington Mens Club Sectional Championship 2019
- Kingdome** Win 15-3 1158.07 Ignored Sep 7th Washington Mens Club Sectional Championship 2019
- Gonzaga** Win 15-0 600 Ignored Sep 7th Washington Mens Club Sectional Championship 2019
13 Furious George Win 15-12 2180.33 Sep 8th Washington Mens Club Sectional Championship 2019
53 Ghost Train Win 15-9 1826.34 Sep 8th Washington Mens Club Sectional Championship 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)