#19 Sub Zero (11-5)

avg: 1865.58  •  sd: 91.16  •  top 16/20: 21.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
7 DiG Loss 9-15 1652.03 Jul 15th TCT Pro Elite Challenge East 2023
13 Vault Loss 9-15 1488.87 Jul 15th TCT Pro Elite Challenge East 2023
38 Phantom Win 15-9 2140.46 Jul 15th TCT Pro Elite Challenge East 2023
51 TireBizFriz Win 14-9 2026.37 Jul 16th TCT Pro Elite Challenge East 2023
14 Sockeye Loss 9-15 1484.39 Aug 19th TCT Elite Select Challenge 2023
28 Tanasi Win 15-10 2186.05 Aug 19th TCT Elite Select Challenge 2023
50 H.I.P Win 15-12 1854 Aug 19th TCT Elite Select Challenge 2023
7 DiG Loss 8-14 1631.48 Aug 20th TCT Elite Select Challenge 2023
13 Vault Win 13-12 2129.36 Aug 20th TCT Elite Select Challenge 2023
27 Omen Win 15-10 2193.39 Aug 20th TCT Elite Select Challenge 2023
170 Rubicon Rapids** Win 13-5 1430.18 Ignored Sep 9th 2023 Mens Northwest Plains Sectional Championship
219 THE BODY** Win 13-4 1131.59 Ignored Sep 9th 2023 Mens Northwest Plains Sectional Championship
156 NOMAD** Win 13-2 1489.95 Ignored Sep 9th 2023 Mens Northwest Plains Sectional Championship
175 Dinkytown Doughboys** Win 13-1 1420.2 Ignored Sep 10th 2023 Mens Northwest Plains Sectional Championship
29 Mallard Loss 14-15 1603.26 Sep 10th 2023 Mens Northwest Plains Sectional Championship
73 Knights of Ni Win 15-6 1985.85 Sep 10th 2023 Mens Northwest Plains Sectional 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)