#12 Traffic (20-14)

avg: 1769.07  •  sd: 88.67  •  top 16/20: 96.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
19 Pop Win 13-10 2007.46 Jul 7th TCT Pro Elite Challenge 2018
18 Phoenix Loss 8-13 1201.51 Jul 7th TCT Pro Elite Challenge 2018
9 Schwa Win 12-10 2076.09 Jul 7th TCT Pro Elite Challenge 2018
14 Showdown Win 13-11 1959.97 Jul 8th TCT Pro Elite Challenge 2018
5 Scandal Loss 7-13 1545.57 Jul 8th TCT Pro Elite Challenge 2018
11 Rival Win 11-10 1929.18 Jul 8th TCT Pro Elite Challenge 2018
8 Nightlock Loss 8-12 1479.24 Jul 8th TCT Pro Elite Challenge 2018
23 LOL Win 15-3 2110.22 Aug 25th Bay Area Invite 2018
4 Seattle Riot Win 15-13 2477.95 Aug 25th Bay Area Invite 2018
3 Molly Brown Loss 11-15 1892.41 Aug 25th Bay Area Invite 2018
2 Fury Loss 11-15 1979.54 Aug 26th Bay Area Invite 2018
20 Underground Loss 11-15 1262.38 Aug 26th Bay Area Invite 2018
8 Nightlock Win 15-13 2134.57 Aug 26th Bay Area Invite 2018
72 Seattle END Win 7-3 908.59 Sep 8th Washington Womens Sectional Championship 2018
36 Seattle Soul** Win 13-4 1702.2 Ignored Sep 8th Washington Womens Sectional Championship 2018
77 Sizzle** Win 13-2 584.12 Ignored Sep 8th Washington Womens Sectional Championship 2018
48 Portland Ivy** Win 13-0 1432.97 Ignored Sep 8th Washington Womens Sectional Championship 2018
30 Sneaky House Hippos Win 11-8 1696.32 Sep 9th Washington Womens Sectional Championship 2018
4 Seattle Riot Loss 8-13 1767.62 Sep 9th Washington Womens Sectional Championship 2018
30 Sneaky House Hippos Win 13-8 1826.87 Sep 9th Washington Womens Sectional Championship 2018
26 Elevate Win 13-12 1526.11 Sep 22nd Northwest Womens Regional Championship 2018
48 Portland Ivy** Win 13-5 1432.97 Ignored Sep 22nd Northwest Womens Regional Championship 2018
9 Schwa Loss 8-13 1341.81 Sep 22nd Northwest Womens Regional Championship 2018
30 Sneaky House Hippos Win 13-8 1826.87 Sep 22nd Northwest Womens Regional Championship 2018
4 Seattle Riot Loss 11-12 2138.78 Sep 23rd Northwest Womens Regional Championship 2018
36 Seattle Soul Win 13-6 1702.2 Sep 23rd Northwest Womens Regional Championship 2018
26 Elevate Win 13-7 1958.64 Sep 23rd Northwest Womens Regional Championship 2018
6 6ixers Loss 9-15 1500.87 Oct 18th USA Ultimate National Championships 2018
2 Fury Loss 3-15 1760.71 Oct 18th USA Ultimate National Championships 2018
18 Phoenix Win 15-9 2213.16 Oct 18th USA Ultimate National Championships 2018
7 Ozone Loss 14-15 1811.53 Oct 19th USA Ultimate National Championships 2018
8 Nightlock Loss 10-15 1466.79 Oct 19th USA Ultimate National Championships 2018
11 Rival Loss 9-15 1288.7 Oct 19th USA Ultimate National Championships 2018
16 Heist Win 15-10 2175.68 Oct 20th USA Ultimate National Championships 2018
**Blowout Eligible

FAQ

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
  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
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)