#11 Seattle Riot (13-10)

avg: 1984.62  •  sd: 57.67  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 Underground Win 15-7 2025.92 Jul 8th TCT Pro Elite Challenge West 2023
4 Molly Brown Loss 8-14 1798 Jul 8th TCT Pro Elite Challenge West 2023
13 Nightlock Win 15-10 2285.13 Jul 8th TCT Pro Elite Challenge West 2023
10 Traffic Loss 7-14 1518.17 Jul 8th TCT Pro Elite Challenge West 2023
25 Colorado Small Batch Win 15-7 2059.47 Jul 9th TCT Pro Elite Challenge West 2023
13 Nightlock Win 14-11 2144.86 Jul 9th TCT Pro Elite Challenge West 2023
25 Colorado Small Batch Win 15-5 2059.47 Aug 19th TCT Elite Select Challenge 2023
12 Nemesis Loss 13-14 1806.46 Aug 19th TCT Elite Select Challenge 2023
29 Pop Win 11-5 2002.13 Aug 19th TCT Elite Select Challenge 2023
7 BENT Win 14-13 2256.43 Aug 20th TCT Elite Select Challenge 2023
13 Nightlock Win 15-10 2285.13 Aug 20th TCT Elite Select Challenge 2023
9 Schwa Loss 6-15 1501.71 Aug 20th TCT Elite Select Challenge 2023
8 6ixers Loss 12-15 1805.44 Sep 2nd TCT Pro Championships 2023
3 Fury Loss 6-15 1854.65 Sep 2nd TCT Pro Championships 2023
16 Grit Win 15-8 2254.95 Sep 2nd TCT Pro Championships 2023
4 Molly Brown Loss 14-15 2209.03 Sep 2nd TCT Pro Championships 2023
14 Parcha Loss 10-13 1497.53 Sep 3rd TCT Pro Championships 2023
16 Grit Win 15-10 2143.74 Sep 4th TCT Pro Championships 2023
27 Underground Win 13-4 2025.92 Sep 23rd 2023 Northwest Womens Regional Championship
10 Traffic Loss 11-13 1872.22 Sep 23rd 2023 Northwest Womens Regional Championship
56 Eugene Further** Win 13-3 1445.32 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
10 Traffic Loss 12-15 1800.56 Sep 24th 2023 Northwest Womens Regional Championship
9 Schwa Win 15-12 2402.2 Sep 24th 2023 Northwest 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)