#6 Flipside (9-5)

avg: 2267.63  •  sd: 137.33  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
25 Colorado Small Batch** Win 15-4 2059.47 Ignored Jul 8th TCT Pro Elite Challenge West 2023
3 Fury Loss 10-15 2001.05 Jul 8th TCT Pro Elite Challenge West 2023
9 Schwa Win 13-9 2520.27 Jul 8th TCT Pro Elite Challenge West 2023
77 Portland Rain Check** Win 15-0 1010.41 Ignored Jul 9th TCT Pro Elite Challenge West 2023
10 Traffic Loss 11-13 1872.22 Jul 9th TCT Pro Elite Challenge West 2023
3 Fury Win 15-9 2970.14 Jul 9th TCT Pro Elite Challenge West 2023
8 6ixers Win 13-9 2524.5 Aug 4th 2023 US Open Club Championships ICC
4 Molly Brown Loss 13-15 2119.85 Aug 4th 2023 US Open Club Championships ICC
4 Molly Brown Loss 10-15 1880.43 Aug 6th 2023 US Open Club Championships ICC
87 Haboob** Win 15-0 756.71 Ignored Sep 23rd 2023 Southwest Womens Regional Championship
45 Rampage** Win 15-6 1573.75 Ignored Sep 23rd 2023 Southwest Womens Regional Championship
13 Nightlock Win 15-3 2431.52 Sep 23rd 2023 Southwest Womens Regional Championship
21 LOL Win 15-7 2159.51 Sep 24th 2023 Southwest Womens Regional Championship
3 Fury Loss 13-15 2240.47 Sep 24th 2023 Southwest 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)