#116 Jabba (18-8)

avg: 992.19  •  sd: 49.69  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
128 Mousetrap Win 10-9 1039.95 Jul 8th Heavyweights 2023
150 Toast! Win 11-7 1270.52 Jul 8th Heavyweights 2023
209 Mastodon Win 13-5 1063.36 Jul 8th Heavyweights 2023
40 UNION Loss 8-13 995.7 Jul 8th Heavyweights 2023
88 Spectre Loss 8-13 612.46 Jul 9th Heavyweights 2023
135 Point of No Return Win 13-5 1461.51 Jul 9th Heavyweights 2023
105 Bandwagon Loss 8-10 765.57 Jul 9th Heavyweights 2023
212 Chalice Win 13-7 1002.44 Jul 22nd Corny Classic II
167 Indiana Pterodactyl Attack Win 13-10 1075.47 Jul 22nd Corny Classic II
209 Mastodon Win 13-8 959.52 Jul 22nd Corny Classic II
241 PanIC** Win 15-1 741.8 Ignored Jul 23rd Corny Classic II
131 Stackcats Win 15-13 1104.96 Jul 23rd Corny Classic II
92 Three Rivers Ultimate Club Loss 11-12 960.54 Jul 23rd Corny Classic II
131 Stackcats Win 10-9 1015.78 Aug 19th Cooler Classic 34
103 Bird Loss 6-13 437.54 Aug 19th Cooler Classic 34
189 Great Minnesota Get Together Win 10-6 1063.05 Aug 19th Cooler Classic 34
211 Lake Superior Disc Win 13-6 1046.79 Aug 19th Cooler Classic 34
170 Boomtown Pandas Win 15-6 1327.04 Aug 20th Cooler Classic 34
182 Melt Win 14-6 1220.06 Aug 20th Cooler Classic 34
145 Madison United Mixed Ultimate Win 14-6 1424.22 Aug 20th Cooler Classic 34
210 ELevate Win 10-7 842.05 Sep 9th 2023 Mixed Central Plains Sectional Championship
92 Three Rivers Ultimate Club Win 13-12 1210.54 Sep 9th 2023 Mixed Central Plains Sectional Championship
167 Indiana Pterodactyl Attack Loss 12-13 622.33 Sep 9th 2023 Mixed Central Plains Sectional Championship
99 Nothing's Great Again Loss 11-13 819.07 Sep 9th 2023 Mixed Central Plains Sectional Championship
210 ELevate Win 14-4 1052.38 Sep 10th 2023 Mixed Central Plains Sectional Championship
131 Stackcats Loss 9-11 641.57 Sep 10th 2023 Mixed Central 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)