#231 POW! (4-15)

avg: 259.66  •  sd: 60.66  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
105 Bandwagon** Loss 3-13 428.24 Ignored Jul 8th Heavyweights 2023
86 Mad Udderburn** Loss 4-13 521.14 Ignored Jul 8th Heavyweights 2023
170 Boomtown Pandas Loss 7-13 169.5 Jul 8th Heavyweights 2023
186 2Fly2Furious Loss 5-13 -18.38 Jul 8th Heavyweights 2023
241 PanIC Win 12-7 662.31 Jul 9th Heavyweights 2023
209 Mastodon Loss 11-12 338.36 Jul 9th Heavyweights 2023
210 ELevate Loss 4-8 -112.43 Aug 19th Motown Throwdown 2023
200 Pixel Loss 7-11 57.89 Aug 19th Motown Throwdown 2023
107 Columbus Chaos Loss 4-9 427.53 Aug 19th Motown Throwdown 2023
210 ELevate Win 9-7 731.72 Aug 20th Motown Throwdown 2023
167 Indiana Pterodactyl Attack Loss 6-11 200.64 Aug 20th Motown Throwdown 2023
194 Thunderpants the Magic Dragon Loss 1-12 -43.36 Aug 20th Motown Throwdown 2023
184 Crucible Loss 7-11 133.52 Aug 20th Motown Throwdown 2023
57 Steamboat** Loss 3-15 739.4 Ignored Sep 9th 2023 Mixed East Plains Sectional Championship
194 Thunderpants the Magic Dragon Win 10-9 681.64 Sep 9th 2023 Mixed East Plains Sectional Championship
150 Toast! Loss 6-15 203.62 Sep 9th 2023 Mixed East Plains Sectional Championship
200 Pixel Loss 8-11 159.17 Sep 10th 2023 Mixed East Plains Sectional Championship
186 2Fly2Furious Loss 10-12 343.5 Sep 10th 2023 Mixed East Plains Sectional Championship
246 Lightning Win 11-10 170.5 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)