#150 Toast! (11-14)

avg: 803.62  •  sd: 50.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
40 UNION** Loss 2-13 891.86 Ignored Jul 8th Heavyweights 2023
116 Jabba Loss 7-11 525.3 Jul 8th Heavyweights 2023
209 Mastodon Win 9-7 742.7 Jul 8th Heavyweights 2023
210 ELevate Win 13-12 577.38 Jul 9th Heavyweights 2023
200 Pixel Win 10-8 787.45 Jul 9th Heavyweights 2023
145 Madison United Mixed Ultimate Loss 9-10 699.22 Jul 9th Heavyweights 2023
57 Steamboat Loss 7-9 1060.07 Aug 19th Motown Throwdown 2023
184 Crucible Win 9-5 1129.47 Aug 19th Motown Throwdown 2023
194 Thunderpants the Magic Dragon Win 13-2 1156.64 Aug 19th Motown Throwdown 2023
115 Queen City Gambit Loss 5-9 468.75 Aug 20th Motown Throwdown 2023
136 Skyhawks Win 11-8 1221.62 Aug 20th Motown Throwdown 2023
186 2Fly2Furious Win 10-3 1181.62 Aug 20th Motown Throwdown 2023
107 Columbus Chaos Loss 7-10 637.87 Aug 20th Motown Throwdown 2023
231 POW! Win 15-6 859.66 Sep 9th 2023 Mixed East Plains Sectional Championship
110 Trex Mix Loss 13-15 798.97 Sep 9th 2023 Mixed East Plains Sectional Championship
246 Lightning** Win 15-4 645.5 Ignored Sep 9th 2023 Mixed East Plains Sectional Championship
107 Columbus Chaos Loss 12-14 806.57 Sep 10th 2023 Mixed East Plains Sectional Championship
107 Columbus Chaos Loss 8-14 491.5 Sep 10th 2023 Mixed East Plains Sectional Championship
110 Trex Mix Win 15-12 1313.64 Sep 10th 2023 Mixed East Plains Sectional Championship
165 Prion Win 12-10 999.85 Sep 23rd 2023 Great Lakes Mixed Regional Championship
57 Steamboat Loss 6-15 739.4 Sep 23rd 2023 Great Lakes Mixed Regional Championship
92 Three Rivers Ultimate Club Loss 8-13 589.38 Sep 23rd 2023 Great Lakes Mixed Regional Championship
110 Trex Mix Loss 6-15 413.15 Sep 23rd 2023 Great Lakes Mixed Regional Championship
131 Stackcats Loss 12-15 590.28 Sep 24th 2023 Great Lakes Mixed Regional Championship
57 Steamboat Loss 6-15 739.4 Sep 24th 2023 Great Lakes Mixed Regional 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)