#23 Oregon Scorch (16-8)

avg: 1676.76  •  sd: 79.32  •  top 16/20: 6.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
11 Seattle Mixtape Win 15-8 2438.83 Jul 8th TCT Pro Elite Challenge West 2023
28 Flight Club Loss 12-13 1510.27 Jul 8th TCT Pro Elite Challenge West 2023
18 Polar Bears Loss 8-14 1225.18 Jul 8th TCT Pro Elite Challenge West 2023
17 Lawless Loss 9-15 1247.7 Jul 9th TCT Pro Elite Challenge West 2023
25 MOONDOG Win 13-8 2144.44 Jul 9th TCT Pro Elite Challenge West 2023
28 Flight Club Win 12-8 2076.43 Jul 9th TCT Pro Elite Challenge West 2023
49 Donuts Win 11-10 1521.52 Aug 26th Northwest Fruit Bowl 2023
41 California Burrito Win 13-8 1984.38 Aug 26th Northwest Fruit Bowl 2023
32 Mile High Trash Loss 9-13 1144.29 Aug 26th Northwest Fruit Bowl 2023
10 Red Flag Win 13-12 2001.21 Aug 27th Northwest Fruit Bowl 2023
25 MOONDOG Loss 11-13 1419.44 Aug 27th Northwest Fruit Bowl 2023
24 Loco Win 12-10 1908.97 Aug 27th Northwest Fruit Bowl 2023
172 Choco Ghost House** Win 13-4 1290.8 Ignored Sep 9th 2023 Mixed Oregon Sectional Championship
229 Night Cap** Win 13-2 894.9 Ignored Sep 9th 2023 Mixed Oregon Sectional Championship
173 Nebula** Win 13-3 1281.68 Ignored Sep 9th 2023 Mixed Oregon Sectional Championship
118 Stump Win 13-9 1404.64 Sep 9th 2023 Mixed Oregon Sectional Championship
90 Hive Win 15-7 1688.37 Sep 10th 2023 Mixed Oregon Sectional Championship
53 Quick Draw Win 14-11 1663.89 Sep 23rd 2023 Northwest Mixed Regional Championship
72 Grit City Loss 11-12 1070.37 Sep 23rd 2023 Northwest Mixed Regional Championship
118 Stump Win 15-8 1550.89 Sep 23rd 2023 Northwest Mixed Regional Championship
4 BFG Loss 13-15 1745.42 Sep 23rd 2023 Northwest Mixed Regional Championship
10 Red Flag Loss 13-15 1662.03 Sep 24th 2023 Northwest Mixed Regional Championship
34 Spoke Win 15-8 2109.14 Sep 24th 2023 Northwest Mixed Regional Championship
58 Lights Out Win 15-7 1911.04 Sep 24th 2023 Northwest 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)