#49 Boomtown (17-0)

avg: 1463.97  •  sd: 70.87  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
75 Bexar Win 12-9 1618.88 Jun 15th Texas Two Finger 2019
256 Balloon** Win 15-3 977.86 Ignored Jun 15th Texas Two Finger 2019
100 Risky Business Win 15-4 1769 Jun 15th Texas Two Finger 2019
107 blOKC party Win 13-8 1642.81 Jul 13th OK Corral 2019
243 rubber duck ultimate.** Win 13-2 1062.64 Ignored Jul 13th OK Corral 2019
149 Tex Mix Win 10-7 1316.34 Jul 13th OK Corral 2019
107 blOKC party Win 13-11 1375.49 Jul 14th OK Corral 2019
243 rubber duck ultimate.** Win 13-2 1062.64 Ignored Jul 14th OK Corral 2019
149 Tex Mix Win 12-10 1164.8 Jul 14th OK Corral 2019
107 blOKC party Win 11-6 1693.34 Aug 17th Hootie on the Hill 2019
123 Impact Win 13-12 1202.49 Aug 17th Hootie on the Hill 2019
157 Hellbenders Win 13-7 1468.09 Aug 17th Hootie on the Hill 2019
70 Memphis STAX Win 13-10 1629.67 Aug 17th Hootie on the Hill 2019
186 Be Reasonable Win 15-7 1345.44 Aug 18th Hootie on the Hill 2019
123 Impact Win 15-7 1677.49 Aug 18th Hootie on the Hill 2019
107 blOKC party Win 14-13 1271.65 Sep 14th Ozarks Mixed Club Sectional Championship 2019
284 Mixed on the Rock** Win 15-2 736.35 Ignored Sep 14th Ozarks Mixed 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)