#3 Fury (17-6)

avg: 2454.65  •  sd: 107.33  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
6 Flipside Win 15-10 2721.23 Jul 8th TCT Pro Elite Challenge West 2023
77 Portland Rain Check** Win 15-1 1010.41 Ignored Jul 8th TCT Pro Elite Challenge West 2023
9 Schwa Win 15-7 2701.71 Jul 8th TCT Pro Elite Challenge West 2023
25 Colorado Small Batch** Win 15-1 2059.47 Ignored Jul 9th TCT Pro Elite Challenge West 2023
6 Flipside Loss 9-15 1752.15 Jul 9th TCT Pro Elite Challenge West 2023
4 Molly Brown Loss 13-14 2209.03 Jul 9th TCT Pro Elite Challenge West 2023
5 Brute Squad Win 15-13 2517.01 Aug 4th 2023 US Open Club Championships ICC
1 Scandal Loss 13-14 2481.7 Aug 4th 2023 US Open Club Championships ICC
8 6ixers Win 13-12 2230.93 Aug 5th 2023 US Open Club Championships ICC
5 Brute Squad Win 15-10 2756.43 Aug 5th 2023 US Open Club Championships ICC
1 Scandal Loss 13-15 2392.52 Aug 6th 2023 US Open Club Championships ICC
8 6ixers Win 15-4 2705.93 Sep 2nd TCT Pro Championships 2023
11 Seattle Riot Win 15-6 2584.62 Sep 2nd TCT Pro Championships 2023
4 Molly Brown Win 15-12 2634.52 Sep 2nd TCT Pro Championships 2023
16 Grit** Win 15-5 2290.14 Ignored Sep 3rd TCT Pro Championships 2023
1 Scandal Loss 14-15 2481.7 Sep 3rd TCT Pro Championships 2023
4 Molly Brown Loss 12-15 2033.54 Sep 4th TCT Pro Championships 2023
68 Venom** Win 15-0 1155.5 Ignored Sep 23rd 2023 Southwest Womens Regional Championship
52 Void Cat Rewind** Win 13-0 1493.58 Ignored Sep 23rd 2023 Southwest Womens Regional Championship
38 FAB** Win 13-0 1719.26 Ignored Sep 23rd 2023 Southwest Womens Regional Championship
21 LOL** Win 13-4 2159.51 Ignored Sep 23rd 2023 Southwest Womens Regional Championship
6 Flipside Win 15-13 2481.81 Sep 24th 2023 Southwest Womens Regional Championship
13 Nightlock** Win 15-5 2431.52 Ignored 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)