#200 Pixel (7-13)

avg: 524.78  •  sd: 92.55  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
236 Mad City Vibes Loss 9-11 -16.18 Jul 8th Heavyweights 2023
103 Bird Loss 9-13 618.97 Jul 8th Heavyweights 2023
57 Steamboat** Loss 4-13 739.4 Ignored Jul 8th Heavyweights 2023
176 The Force Win 13-11 880.71 Jul 8th Heavyweights 2023
170 Boomtown Pandas Win 13-7 1284.57 Jul 9th Heavyweights 2023
150 Toast! Loss 8-10 540.96 Jul 9th Heavyweights 2023
186 2Fly2Furious Win 13-7 1139.15 Jul 9th Heavyweights 2023
210 ELevate Loss 8-9 327.38 Aug 19th Motown Throwdown 2023
231 POW! Win 11-7 726.55 Aug 19th Motown Throwdown 2023
107 Columbus Chaos Loss 3-13 427.53 Aug 19th Motown Throwdown 2023
210 ELevate Win 10-5 1026.28 Aug 20th Motown Throwdown 2023
110 Trex Mix Loss 4-10 413.15 Aug 20th Motown Throwdown 2023
186 2Fly2Furious Loss 6-12 2.31 Aug 20th Motown Throwdown 2023
194 Thunderpants the Magic Dragon Loss 4-8 -8.17 Aug 20th Motown Throwdown 2023
115 Queen City Gambit Loss 4-15 397.8 Sep 9th 2023 Mixed East Plains Sectional Championship
5 Cleveland Crocs** Loss 5-15 1336.51 Ignored Sep 9th 2023 Mixed East Plains Sectional Championship
186 2Fly2Furious Win 15-11 962.78 Sep 9th 2023 Mixed East Plains Sectional Championship
194 Thunderpants the Magic Dragon Loss 10-13 228.5 Sep 10th 2023 Mixed East Plains Sectional Championship
231 POW! Win 11-8 625.27 Sep 10th 2023 Mixed East Plains Sectional Championship
194 Thunderpants the Magic Dragon Loss 11-15 175.48 Sep 10th 2023 Mixed East 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)