#77 Portland Rain Check (3-20)

avg: 410.41  •  sd: 124.87  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 Underground** Loss 1-11 825.92 Ignored Jun 24th Summer Solstice 2023
50 Drift Loss 6-11 368.25 Jun 24th Summer Solstice 2023
33 Seattle END** Loss 1-11 674.73 Ignored Jun 24th Summer Solstice 2023
51 Seven Devils Loss 4-11 307.19 Jun 24th Summer Solstice 2023
38 FAB Loss 7-12 598.75 Jun 25th Summer Solstice 2023
84 Seattle Soul Win 8-5 722.79 Jun 25th Summer Solstice 2023
25 Colorado Small Batch** Loss 5-15 859.47 Ignored Jul 8th TCT Pro Elite Challenge West 2023
3 Fury** Loss 1-15 1854.65 Ignored Jul 8th TCT Pro Elite Challenge West 2023
9 Schwa** Loss 1-15 1501.71 Ignored Jul 8th TCT Pro Elite Challenge West 2023
27 Underground** Loss 4-15 825.92 Ignored Jul 9th TCT Pro Elite Challenge West 2023
6 Flipside** Loss 0-15 1667.63 Ignored Jul 9th TCT Pro Elite Challenge West 2023
13 Nightlock** Loss 1-15 1231.52 Ignored Jul 9th TCT Pro Elite Challenge West 2023
27 Underground** Loss 2-15 825.92 Ignored Sep 9th 2023 Womens Washington Sectional Championship
53 Hucklebears Loss 3-15 277.79 Sep 9th 2023 Womens Washington Sectional Championship
33 Seattle END** Loss 2-15 674.73 Ignored Sep 9th 2023 Womens Washington Sectional Championship
50 Drift Loss 5-15 314.94 Sep 10th 2023 Womens Washington Sectional Championship
84 Seattle Soul Win 11-8 634.8 Sep 10th 2023 Womens Washington Sectional Championship
19 Dark Sky** Loss 0-13 981.24 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
50 Drift Loss 6-13 314.94 Sep 23rd 2023 Northwest Womens Regional Championship
9 Schwa** Loss 1-13 1501.71 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
33 Seattle END** Loss 2-13 674.73 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
56 Eugene Further Loss 5-15 245.32 Sep 24th 2023 Northwest Womens Regional Championship
84 Seattle Soul Win 11-9 518.39 Sep 24th 2023 Northwest Womens 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)