#15 Western Washington (9-14)

avg: 2132.74  •  sd: 62.7  •  top 16/20: 98.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
28 California Win 12-5 2456.51 Feb 17th Presidents Day Invite 2024
85 California-San Diego-B** Win 15-1 1939.27 Ignored Feb 17th Presidents Day Invite 2024
7 Colorado Loss 9-13 2007.68 Feb 17th Presidents Day Invite 2024
5 Stanford Loss 4-15 1919.88 Feb 18th Presidents Day Invite 2024
14 California-San Diego Loss 12-13 2017.18 Feb 18th Presidents Day Invite 2024
9 California-Santa Barbara Loss 8-12 1885.25 Feb 18th Presidents Day Invite 2024
7 Colorado Loss 7-9 2146.91 Feb 19th Presidents Day Invite 2024
25 Utah Win 15-4 2486.02 Feb 19th Presidents Day Invite 2024
1 British Columbia** Loss 0-13 2170.47 Ignored Mar 16th NW Challenge 2024
21 Northeastern Win 13-12 2084.88 Mar 16th NW Challenge 2024
6 North Carolina Loss 4-13 1899.4 Mar 17th NW Challenge 2024
21 Northeastern Win 13-11 2188.72 Mar 17th NW Challenge 2024
4 Oregon Loss 11-13 2340.21 Mar 17th NW Challenge 2024
26 Wisconsin Win 13-9 2295.74 Mar 17th NW Challenge 2024
1 British Columbia** Loss 5-15 2170.47 Ignored Apr 13th Cascadia D I Womens Conferences 2024
13 Victoria Loss 9-12 1808 Apr 13th Cascadia D I Womens Conferences 2024
10 Washington Loss 9-14 1784.22 Apr 13th Cascadia D I Womens Conferences 2024
154 Oregon State** Win 15-4 1459.31 Ignored Apr 14th Cascadia D I Womens Conferences 2024
1 British Columbia Win 13-11 2999.31 May 4th Northwest D I College Womens Regionals 2024
25 Utah Win 12-10 2124.15 May 4th Northwest D I College Womens Regionals 2024
13 Victoria Loss 12-13 2028.37 May 4th Northwest D I College Womens Regionals 2024
4 Oregon Loss 7-10 2179.39 May 5th Northwest D I College Womens Regionals 2024
10 Washington Loss 8-12 1816.93 May 5th Northwest D I College Womens Regionals 2024
**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)