#20 Washington (11-12)

avg: 1742.05  •  sd: 47.9  •  top 16/20: 46.8%

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# Opponent Result Game Rating Status Date Event
124 Arizona State** Win 15-6 1664.85 Ignored Jan 28th Santa Barbara Invitational 2023
30 Utah State Loss 12-14 1429.74 Jan 28th Santa Barbara Invitational 2023
64 California-San Diego Win 15-11 1760.76 Jan 28th Santa Barbara Invitational 2023
9 California-Santa Cruz Loss 9-12 1550.1 Jan 28th Santa Barbara Invitational 2023
16 British Columbia Loss 9-11 1527.81 Jan 29th Santa Barbara Invitational 2023
47 Case Western Reserve Win 13-10 1783.52 Jan 29th Santa Barbara Invitational 2023
7 Oregon Loss 9-12 1622.71 Jan 29th Santa Barbara Invitational 2023
8 Cal Poly-SLO Loss 9-12 1587.58 Jan 29th Santa Barbara Invitational 2023
52 Colorado State Win 13-6 2036.47 Feb 18th President’s Day Invite
8 Cal Poly-SLO Loss 12-14 1711.99 Feb 18th President’s Day Invite
82 California-Santa Barbara Win 12-8 1716.41 Feb 18th President’s Day Invite
6 Colorado Loss 11-13 1763.56 Feb 19th President’s Day Invite
38 Emory Win 12-10 1773.94 Feb 19th President’s Day Invite
43 Grand Canyon Win 14-9 1985.78 Feb 19th President’s Day Invite
45 Western Washington Loss 10-11 1364.25 Feb 19th President’s Day Invite
6 Colorado Loss 10-12 1754.27 Feb 20th President’s Day Invite
8 Cal Poly-SLO Loss 10-13 1604.8 Feb 20th President’s Day Invite
16 British Columbia Win 13-10 2105.16 Mar 4th Stanford Invite Mens
52 Colorado State Loss 12-13 1311.47 Mar 4th Stanford Invite Mens
66 Stanford Win 12-10 1610.62 Mar 4th Stanford Invite Mens
7 Oregon Win 11-10 2093.08 Mar 5th Stanford Invite Mens
8 Cal Poly-SLO Loss 10-11 1807.95 Mar 5th Stanford Invite Mens
33 Victoria Win 12-6 2205.32 Mar 5th Stanford Invite 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)