#235 Mad City Vibes (4-15)

avg: 229.8  •  sd: 69.33  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
201 Pixel Win 11-9 788.29 Jul 8th Heavyweights 2023
50 Steamboat** Loss 2-13 790.5 Ignored Jul 8th Heavyweights 2023
111 Bird** Loss 4-13 422.53 Ignored Jul 8th Heavyweights 2023
212 ELevate Loss 6-13 -157.22 Jul 9th Heavyweights 2023
191 2Fly2Furious Loss 6-13 -6.23 Jul 9th Heavyweights 2023
181 Frostbite Loss 6-13 41.15 Jul 9th Heavyweights 2023
240 PanIC Loss 9-11 -107.55 Aug 19th Cooler Classic 34
182 The Force Loss 5-13 39.22 Aug 19th Cooler Classic 34
205 Locomotion Loss 7-12 -3.33 Aug 19th Cooler Classic 34
209 Mastodon Loss 6-10 -33.21 Aug 19th Cooler Classic 34
240 PanIC Win 11-7 608.55 Aug 20th Cooler Classic 34
227 Arms Race Win 13-11 554.82 Aug 20th Cooler Classic 34
245 Underdogs Win 15-6 674.21 Aug 20th Cooler Classic 34
111 Bird** Loss 4-13 422.53 Ignored Sep 9th 2023 Mixed Northwest Plains Sectional Championship
196 Great Minnesota Get Together Loss 10-13 230.6 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
92 Mad Udderburn** Loss 2-13 500.16 Ignored Sep 9th 2023 Mixed Northwest Plains Sectional Championship
155 Madison United Mixed Ultimate Loss 9-13 398.57 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
196 Great Minnesota Get Together Loss 6-13 -41.25 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
188 Melt Loss 10-12 372.11 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)