#4 Molly Brown (20-4)

avg: 2334.03  •  sd: 104.22  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 Underground** Win 15-3 2025.92 Ignored Jul 8th TCT Pro Elite Challenge West 2023
11 Seattle Riot Win 14-8 2520.65 Jul 8th TCT Pro Elite Challenge West 2023
10 Traffic Loss 9-14 1627.19 Jul 8th TCT Pro Elite Challenge West 2023
3 Fury Win 14-13 2579.65 Jul 9th TCT Pro Elite Challenge West 2023
13 Nightlock Win 15-11 2212.69 Jul 9th TCT Pro Elite Challenge West 2023
10 Traffic Win 15-10 2554.66 Jul 9th TCT Pro Elite Challenge West 2023
6 Flipside Win 15-13 2481.81 Aug 4th 2023 US Open Club Championships ICC
9 Schwa Win 15-8 2666.52 Aug 4th 2023 US Open Club Championships ICC
13 Nightlock Win 15-6 2431.52 Aug 4th 2023 US Open Club Championships ICC
8 6ixers Win 15-12 2406.42 Aug 5th 2023 US Open Club Championships ICC
5 Brute Squad Loss 11-15 1921.66 Aug 5th 2023 US Open Club Championships ICC
6 Flipside Win 15-10 2721.23 Aug 6th 2023 US Open Club Championships ICC
3 Fury Loss 12-15 2154.16 Sep 2nd TCT Pro Championships 2023
16 Grit Win 14-9 2164.01 Sep 2nd TCT Pro Championships 2023
11 Seattle Riot Win 15-14 2109.62 Sep 2nd TCT Pro Championships 2023
8 6ixers Win 13-11 2334.77 Sep 3rd TCT Pro Championships 2023
5 Brute Squad Win 15-12 2603.32 Sep 3rd TCT Pro Championships 2023
2 Phoenix Loss 12-15 2179.25 Sep 3rd TCT Pro Championships 2023
3 Fury Win 15-12 2755.15 Sep 4th TCT Pro Championships 2023
25 Colorado Small Batch** Win 12-5 2059.47 Ignored Sep 23rd 2023 South Central Womens Regional
91 Firewheel** Win 15-1 674.38 Ignored Sep 23rd 2023 South Central Womens Regional
49 Trainwreck** Win 15-0 1534.06 Ignored Sep 23rd 2023 South Central Womens Regional
26 Vengeance Win 13-6 2033.95 Sep 24th 2023 South Central Womens Regional
64 TWISTED** Win 15-1 1289.75 Ignored Sep 24th 2023 South Central Womens Regional
**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)