#29 Brickhouse (15-9)

avg: 1508.25  •  sd: 82.47  •  top 16/20: 0.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
30 Mad Men Win 13-9 1914.65 Jul 27th TCT Select Flight Invite East 2019
31 Black Market I Win 13-7 2052.67 Jul 27th TCT Select Flight Invite East 2019
11 Pittsburgh Temper Loss 8-13 1365.59 Jul 27th TCT Select Flight Invite East 2019
54 Red Circus Loss 9-13 907.17 Jul 27th TCT Select Flight Invite East 2019
13 Furious George Loss 10-13 1508.42 Jul 28th TCT Select Flight Invite East 2019
40 Nitro Loss 11-13 1193.13 Jul 28th TCT Select Flight Invite East 2019
42 Garden State Ultimate Loss 12-13 1289.02 Aug 10th Chesapeake Open 2019
44 Shade Loss 9-13 990.63 Aug 10th Chesapeake Open 2019
23 Vault Loss 11-12 1498.65 Aug 10th Chesapeake Open 2019
132 Oakgrove Boys Win 13-7 1445.31 Aug 10th Chesapeake Open 2019
38 Lost Boys Win 13-8 1939.15 Aug 11th Chesapeake Open 2019
62 Big Wrench Win 13-4 1876.64 Aug 11th Chesapeake Open 2019
47 CITYWIDE Special Win 13-8 1888.66 Aug 11th Chesapeake Open 2019
99 Bash Bros Win 15-5 1642.71 Sep 7th North Carolina Mens Club Sectional Championship 2019
120 baNC Win 11-10 1061.22 Sep 7th North Carolina Mens Club Sectional Championship 2019
63 Turbine Win 10-7 1642.2 Sep 7th North Carolina Mens Club Sectional Championship 2019
99 Bash Bros Win 15-10 1496.32 Sep 8th North Carolina Mens Club Sectional Championship 2019
63 Turbine Win 15-7 1852.54 Sep 8th North Carolina Mens Club Sectional Championship 2019
99 Bash Bros Win 13-9 1461.28 Sep 21st Southeast Club Mens Regional Championship 2019
102 Charleston Heat Stroke Win 13-5 1634.45 Sep 21st Southeast Club Mens Regional Championship 2019
50 UpRoar Loss 11-13 1131.63 Sep 21st Southeast Club Mens Regional Championship 2019
67 Ironmen Win 13-10 1566.3 Sep 21st Southeast Club Mens Regional Championship 2019
52 El Niño Win 13-10 1684.99 Sep 22nd Southeast Club Mens Regional Championship 2019
37 Tanasi Loss 11-13 1246.41 Sep 22nd Southeast Club 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)