#46 Western Washington (9-11)

avg: 1688.53  •  sd: 56.65  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
9 Oregon Loss 9-11 1887.93 Jan 28th Santa Barbara Invitational 2023
18 California Loss 9-12 1616.2 Jan 28th Santa Barbara Invitational 2023
54 Northwestern Win 15-10 2069.79 Jan 28th Santa Barbara Invitational 2023
57 Stanford Loss 8-9 1457.25 Jan 28th Santa Barbara Invitational 2023
151 Arizona State Win 15-6 1746.59 Jan 29th Santa Barbara Invitational 2023
42 Grand Canyon Loss 9-12 1359.92 Jan 29th Santa Barbara Invitational 2023
15 UCLA Loss 6-12 1448.98 Feb 18th President’s Day Invite
29 Utah State Loss 8-11 1472.66 Feb 18th President’s Day Invite
18 California Loss 4-10 1361.57 Feb 18th President’s Day Invite
6 Colorado Loss 11-15 1816.4 Feb 19th President’s Day Invite
61 Emory Loss 7-9 1297.64 Feb 19th President’s Day Invite
17 Washington Win 11-10 2115.14 Feb 19th President’s Day Invite
58 California-San Diego Win 11-9 1830.62 Feb 20th President’s Day Invite
57 Stanford Win 13-9 2000.81 Feb 20th President’s Day Invite
29 Utah State Loss 10-13 1510.13 Apr 1st Northwest Challenge Mens
129 Gonzaga Win 11-8 1622.19 Apr 1st Northwest Challenge Mens
125 Washington State Win 10-8 1530.85 Apr 1st Northwest Challenge Mens
16 British Columbia Loss 9-12 1647.18 Apr 2nd Northwest Challenge Mens
86 Dartmouth Win 14-11 1750.3 Apr 2nd Northwest Challenge Mens
17 Washington Win 11-10 2115.14 Apr 2nd Northwest Challenge Mens
**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)