#210 ELevate (4-15)

avg: 452.38  •  sd: 52.02  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
135 Point of No Return Loss 10-13 533.37 Jul 8th Heavyweights 2023
88 Spectre** Loss 4-13 508.62 Ignored Jul 8th Heavyweights 2023
145 Madison United Mixed Ultimate Loss 6-13 224.22 Jul 8th Heavyweights 2023
241 PanIC Win 13-7 699.33 Jul 8th Heavyweights 2023
150 Toast! Loss 12-13 678.62 Jul 9th Heavyweights 2023
170 Boomtown Pandas Loss 7-9 447.7 Jul 9th Heavyweights 2023
236 Mad City Vibes Win 13-6 833.02 Jul 9th Heavyweights 2023
107 Columbus Chaos Loss 8-10 764.87 Aug 19th Motown Throwdown 2023
200 Pixel Win 9-8 649.78 Aug 19th Motown Throwdown 2023
231 POW! Win 8-4 824.47 Aug 19th Motown Throwdown 2023
136 Skyhawks Loss 6-12 276.7 Aug 20th Motown Throwdown 2023
184 Crucible Loss 6-10 104.25 Aug 20th Motown Throwdown 2023
231 POW! Loss 7-9 -19.68 Aug 20th Motown Throwdown 2023
200 Pixel Loss 5-10 -49.12 Aug 20th Motown Throwdown 2023
88 Spectre Loss 5-11 508.62 Sep 9th 2023 Mixed Central Plains Sectional Championship
156 Practice Player Penguins [JV] Loss 6-8 489.16 Sep 9th 2023 Mixed Central Plains Sectional Championship
116 Jabba Loss 7-10 602.53 Sep 9th 2023 Mixed Central Plains Sectional Championship
131 Stackcats Loss 6-11 344.08 Sep 10th 2023 Mixed Central Plains Sectional Championship
116 Jabba Loss 4-14 392.19 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)