#13 Nightlock (12-12)

avg: 1831.52  •  sd: 69.62  •  top 16/20: 96.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 Underground Win 11-7 1892.81 Jul 8th TCT Pro Elite Challenge West 2023
11 Seattle Riot Loss 10-15 1531.02 Jul 8th TCT Pro Elite Challenge West 2023
10 Traffic Loss 9-14 1627.19 Jul 8th TCT Pro Elite Challenge West 2023
4 Molly Brown Loss 11-15 1952.87 Jul 9th TCT Pro Elite Challenge West 2023
77 Portland Rain Check** Win 15-1 1010.41 Ignored Jul 9th TCT Pro Elite Challenge West 2023
9 Schwa Win 12-11 2226.71 Jul 9th TCT Pro Elite Challenge West 2023
11 Seattle Riot Loss 11-14 1671.28 Jul 9th TCT Pro Elite Challenge West 2023
8 6ixers Loss 11-15 1724.77 Aug 4th 2023 US Open Club Championships ICC
4 Molly Brown Loss 6-15 1734.03 Aug 4th 2023 US Open Club Championships ICC
9 Schwa Loss 8-15 1536.9 Aug 4th 2023 US Open Club Championships ICC
10 Traffic Loss 11-13 1872.22 Aug 5th 2023 US Open Club Championships ICC
9 Schwa Loss 6-15 1501.71 Aug 5th 2023 US Open Club Championships ICC
27 Underground Win 10-6 1922.08 Aug 19th TCT Elite Select Challenge 2023
16 Grit Win 15-9 2205.62 Aug 19th TCT Elite Select Challenge 2023
34 Indy Rogue** Win 15-4 1786.34 Ignored Aug 19th TCT Elite Select Challenge 2023
23 Flight Win 12-7 2059.91 Aug 20th TCT Elite Select Challenge 2023
11 Seattle Riot Loss 10-15 1531.02 Aug 20th TCT Elite Select Challenge 2023
14 Parcha Win 10-7 2215.33 Aug 20th TCT Elite Select Challenge 2023
20 Wildfire Win 15-5 2160.61 Sep 23rd 2023 Southwest Womens Regional Championship
6 Flipside Loss 3-15 1667.63 Sep 23rd 2023 Southwest Womens Regional Championship
89 Tempo** Win 15-1 738.45 Ignored Sep 23rd 2023 Southwest Womens Regional Championship
20 Wildfire Win 14-13 1685.61 Sep 24th 2023 Southwest Womens Regional Championship
38 FAB** Win 15-3 1719.26 Ignored Sep 24th 2023 Southwest Womens Regional Championship
3 Fury** Loss 5-15 1854.65 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)