#21 Brickyard (16-8)

avg: 1643.19  •  sd: 80.83  •  top 16/20: 7.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
46 Haymaker Win 13-6 1996.63 Aug 3rd Heavyweights 2019
80 ISO Atmo Win 13-5 1746.91 Aug 3rd Heavyweights 2019
18 Yogosbo Loss 12-13 1591.13 Aug 3rd Heavyweights 2019
46 Haymaker Win 13-5 1996.63 Aug 4th Heavyweights 2019
84 Black Lung Win 13-5 1725.55 Aug 4th Heavyweights 2019
18 Yogosbo Loss 8-13 1219.97 Aug 4th Heavyweights 2019
41 Inception Win 15-11 1801.61 Aug 17th TCT Elite Select Challenge 2019
19 Voodoo Loss 11-15 1310.02 Aug 17th TCT Elite Select Challenge 2019
11 Pittsburgh Temper Loss 11-15 1480.58 Aug 17th TCT Elite Select Challenge 2019
8 DiG Loss 11-12 1826.74 Aug 18th TCT Elite Select Challenge 2019
18 Yogosbo Loss 9-10 1591.13 Aug 18th TCT Elite Select Challenge 2019
32 Prairie Fire Win 10-7 1873.39 Aug 18th TCT Elite Select Challenge 2019
231 Black Market III** Win 13-0 706.25 Ignored Sep 7th Central Plains Mens Club Sectional Championship 2019
156 Ditto A Win 13-6 1345.75 Sep 7th Central Plains Mens Club Sectional Championship 2019
172 MomINtuM** Win 13-3 1240.31 Ignored Sep 7th Central Plains Mens Club Sectional Championship 2019
122 Satellite** Win 13-4 1526.03 Ignored Sep 7th Central Plains Mens Club Sectional Championship 2019
31 Black Market I Win 15-10 1948.74 Sep 8th Central Plains Mens Club Sectional Championship 2019
31 Black Market I Win 11-8 1860.75 Sep 21st Great Lakes Mens Regional Championship 2019
20 CLE Smokestack Loss 8-10 1400.13 Sep 21st Great Lakes Mens Regional Championship 2019
81 Omen Win 11-5 1738.86 Sep 21st Great Lakes Mens Regional Championship 2019
31 Black Market I Win 10-9 1620.14 Sep 22nd Great Lakes Mens Regional Championship 2019
133 Kentucky Flying Circus Win 11-5 1484.93 Sep 22nd Great Lakes Mens Regional Championship 2019
33 Nain Rouge Win 11-10 1603.8 Sep 22nd Great Lakes Mens Regional Championship 2019
5 Chicago Machine Loss 6-13 1487.11 Sep 22nd Great Lakes Mens Regional 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)