#95 Scarecrow (11-8)

avg: 1068.5  •  sd: 55.37  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
147 FLI Win 12-7 1338.8 Jul 8th AntlerLock
120 WHUF [B] Win 13-8 1468.04 Jul 8th AntlerLock
96 Bench Win 13-5 1667.59 Jul 8th AntlerLock
218 Sugar Shack** Win 15-3 997.47 Ignored Jul 8th AntlerLock
84 Buffalo Lake Effect Loss 9-10 1005.22 Jul 8th AntlerLock
111 Lampshade Loss 5-6 880.42 Jul 9th AntlerLock
195 Swampbenders Win 13-6 1148.56 Aug 5th Philly Open 2023
184 Crucible Win 13-5 1200.41 Aug 5th Philly Open 2023
71 Grand Army Win 9-8 1329.31 Aug 5th Philly Open 2023
164 Espionage Loss 7-9 492.22 Aug 6th Philly Open 2023
119 Mashed Loss 6-13 383.99 Aug 6th Philly Open 2023
84 Buffalo Lake Effect Win 7-6 1255.22 Aug 6th Philly Open 2023
47 Darkwing Loss 3-13 819.5 Sep 9th 2023 Mixed East New England Sectional Championship
163 Sunken Circus Win 12-10 1009.86 Sep 9th 2023 Mixed East New England Sectional Championship
6 Sprocket** Loss 6-15 1324.96 Ignored Sep 9th 2023 Mixed East New England Sectional Championship
64 Obscure Loss 13-15 1036.2 Sep 9th 2023 Mixed East New England Sectional Championship
190 Rainbow Win 15-7 1165.25 Sep 10th 2023 Mixed East New England Sectional Championship
65 League of Shadows Loss 12-15 948.2 Sep 10th 2023 Mixed East New England Sectional Championship
111 Lampshade Win 13-11 1234.26 Sep 10th 2023 Mixed East New England 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)