#10 Traffic (14-8)

avg: 2101.06  •  sd: 79.23  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
4 Molly Brown Win 14-9 2807.9 Jul 8th TCT Pro Elite Challenge West 2023
13 Nightlock Win 14-9 2305.39 Jul 8th TCT Pro Elite Challenge West 2023
11 Seattle Riot Win 14-7 2567.51 Jul 8th TCT Pro Elite Challenge West 2023
27 Underground** Win 15-2 2025.92 Ignored Jul 9th TCT Pro Elite Challenge West 2023
6 Flipside Win 13-11 2496.47 Jul 9th TCT Pro Elite Challenge West 2023
4 Molly Brown Loss 10-15 1880.43 Jul 9th TCT Pro Elite Challenge West 2023
5 Brute Squad Loss 11-15 1921.66 Aug 4th 2023 US Open Club Championships ICC
1 Scandal Loss 8-15 2041.89 Aug 4th 2023 US Open Club Championships ICC
13 Nightlock Win 13-11 2060.37 Aug 5th 2023 US Open Club Championships ICC
9 Schwa Loss 11-14 1788.37 Aug 6th 2023 US Open Club Championships ICC
14 Parcha Win 14-12 2046.63 Sep 2nd TCT Pro Championships 2023
2 Phoenix Loss 9-14 2005.88 Sep 2nd TCT Pro Championships 2023
1 Scandal Loss 11-15 2225.53 Sep 2nd TCT Pro Championships 2023
8 6ixers Loss 6-14 1505.93 Sep 3rd TCT Pro Championships 2023
5 Brute Squad Loss 8-15 1738.02 Sep 3rd TCT Pro Championships 2023
16 Grit Win 12-10 1928.26 Sep 3rd TCT Pro Championships 2023
14 Parcha Win 13-8 2321.83 Sep 4th TCT Pro Championships 2023
84 Seattle Soul** Win 13-0 869.19 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
51 Seven Devils** Win 13-2 1507.19 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
11 Seattle Riot Win 13-11 2213.46 Sep 23rd 2023 Northwest Womens Regional Championship
19 Dark Sky Win 15-8 2146.05 Sep 24th 2023 Northwest Womens Regional Championship
11 Seattle Riot Win 15-12 2285.11 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)