#141 Mad Udderburn (13-8)

avg: 968.38  •  sd: 60.03  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
226 Boomtown Pandas Win 13-6 1171.61 Aug 3rd Heavyweights 2019
135 Los Heros Win 13-9 1429.65 Aug 3rd Heavyweights 2019
228 Midwestern Mediocrity Win 13-8 1022.1 Aug 3rd Heavyweights 2019
169 Wildstyle Win 13-6 1411.29 Aug 3rd Heavyweights 2019
71 Northern Comfort Loss 9-13 864.19 Aug 4th Heavyweights 2019
125 Nothing's Great Again Loss 11-13 823.51 Aug 4th Heavyweights 2019
129 Bird Loss 8-13 539.4 Aug 17th Cooler Classic 31
254 Robotic Snakes Win 13-6 1004.08 Aug 17th Cooler Classic 31
192 Jabba Win 13-11 937.2 Aug 17th Cooler Classic 31
147 Point of No Return Loss 10-13 604.58 Aug 17th Cooler Classic 31
209 Pushovers-B Win 9-5 1188.48 Aug 18th Cooler Classic 31
192 Jabba Win 9-8 833.36 Aug 18th Cooler Classic 31
171 Mousetrap Loss 6-8 499.06 Aug 18th Cooler Classic 31
248 Dinosaur Fancy Win 13-8 928.77 Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
154 Melt Loss 12-13 788.8 Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
32 NOISE** Loss 2-13 1011.8 Ignored Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
218 Great Minnesota Get Together Win 11-8 977.93 Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
129 Bird Loss 10-13 707.42 Sep 8th Northwest Plains Mixed Club Sectional Championship 2019
213 Mastodon Win 14-12 866.48 Sep 8th Northwest Plains Mixed Club Sectional Championship 2019
147 Point of No Return Win 12-8 1373.88 Sep 8th Northwest Plains Mixed Club Sectional Championship 2019
171 Mousetrap Win 13-3 1399.55 Sep 8th Northwest 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)