#125 Nothing's Great Again (6-11)

avg: 1052.35  •  sd: 82.38  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
71 Northern Comfort Loss 7-13 725.22 Aug 3rd Heavyweights 2019
58 Toast Loss 11-13 1156.24 Aug 3rd Heavyweights 2019
109 Shakedown Win 13-12 1266.92 Aug 3rd Heavyweights 2019
93 PanIC Loss 6-13 584.69 Aug 4th Heavyweights 2019
141 Mad Udderburn Win 13-11 1197.22 Aug 4th Heavyweights 2019
154 Melt Win 13-8 1409.96 Aug 4th Heavyweights 2019
18 Columbus Cocktails** Loss 6-15 1162.6 Ignored Aug 17th TCT Elite Select Challenge 2019
7 Mischief** Loss 5-15 1327.51 Ignored Aug 17th TCT Elite Select Challenge 2019
15 Loco** Loss 4-15 1202.34 Ignored Aug 17th TCT Elite Select Challenge 2019
30 No Touching! Loss 7-10 1251.07 Aug 18th TCT Elite Select Challenge 2019
37 Jughandle Loss 4-13 962.97 Aug 18th TCT Elite Select Challenge 2019
172 Prion Loss 8-13 302.98 Sep 7th Central Plains Mixed Club Sectional Championship 2019
277 Indiana Pterodactyl Attack** Win 13-4 823.31 Ignored Sep 7th Central Plains Mixed Club Sectional Championship 2019
156 ELevate Win 13-6 1512.98 Sep 7th Central Plains Mixed Club Sectional Championship 2019
118 Stripes Win 13-7 1651.82 Sep 7th Central Plains Mixed Club Sectional Championship 2019
50 U54 Ultimate Loss 3-15 861.31 Sep 8th Central Plains Mixed Club Sectional Championship 2019
109 Shakedown Loss 11-14 828.58 Sep 8th Central Plains 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)