#12 Nemesis (12-4)

avg: 1931.46  •  sd: 155.98  •  top 16/20: 93.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
23 Flight Win 13-7 2096.93 Jul 15th TCT Pro Elite Challenge East 2023
72 KnoxFusion** Win 15-4 1093.2 Ignored Jul 15th TCT Pro Elite Challenge East 2023
2 Phoenix Loss 3-12 1879.74 Jul 15th TCT Pro Elite Challenge East 2023
1 Scandal** Loss 6-15 2006.7 Ignored Jul 16th TCT Pro Elite Challenge East 2023
25 Colorado Small Batch Win 15-8 2024.28 Aug 19th TCT Elite Select Challenge 2023
29 Pop Win 15-7 2002.13 Aug 19th TCT Elite Select Challenge 2023
11 Seattle Riot Win 14-13 2109.62 Aug 19th TCT Elite Select Challenge 2023
7 BENT Loss 9-13 1712.86 Aug 20th TCT Elite Select Challenge 2023
16 Grit Win 14-11 2003.48 Aug 20th TCT Elite Select Challenge 2023
9 Schwa Loss 10-15 1648.1 Aug 20th TCT Elite Select Challenge 2023
34 Indy Rogue** Win 15-5 1786.34 Ignored Sep 23rd 2023 Great Lakes Womens Regional Championship
80 Notorious C.L.E.** Win 15-1 902.98 Ignored Sep 23rd 2023 Great Lakes Womens Regional Championship
102 Solstice** Win 15-0 281.02 Ignored Sep 23rd 2023 Great Lakes Womens Regional Championship
83 Autonomous** Win 15-1 876.44 Ignored Sep 24th 2023 Great Lakes Womens Regional Championship
62 Dish** Win 15-2 1302.78 Ignored Sep 24th 2023 Great Lakes Womens Regional Championship
31 Rival Win 15-8 1931.24 Sep 24th 2023 Great Lakes 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)