#7 Oregon (19-4)

avg: 1968.08  •  sd: 47.1  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
66 Stanford Win 15-4 1972.5 Jan 28th Santa Barbara Invitational 2023
45 Western Washington Win 11-9 1738.46 Jan 28th Santa Barbara Invitational 2023
62 Northwestern Win 15-3 1987.69 Jan 28th Santa Barbara Invitational 2023
26 California Loss 10-11 1568.11 Jan 28th Santa Barbara Invitational 2023
57 Utah Win 15-8 1971.26 Jan 29th Santa Barbara Invitational 2023
9 California-Santa Cruz Loss 12-13 1770.46 Jan 29th Santa Barbara Invitational 2023
20 Washington Win 12-9 2087.42 Jan 29th Santa Barbara Invitational 2023
33 Victoria Win 10-6 2122.17 Jan 29th Santa Barbara Invitational 2023
38 Emory Win 13-9 1954.39 Feb 18th President’s Day Invite
9 California-Santa Cruz Win 13-9 2314.03 Feb 18th President’s Day Invite
43 Grand Canyon Win 14-8 2047.94 Feb 18th President’s Day Invite
82 California-Santa Barbara** Win 15-4 1875.26 Ignored Feb 19th President’s Day Invite
26 California Win 13-9 2111.68 Feb 19th President’s Day Invite
31 Oregon State Win 11-8 1996.63 Feb 19th President’s Day Invite
14 UCLA Win 10-9 1955.83 Feb 19th President’s Day Invite
6 Colorado Win 14-13 2117.4 Feb 20th President’s Day Invite
8 Cal Poly-SLO Win 12-9 2278.31 Feb 20th President’s Day Invite
30 Utah State Win 13-8 2146.86 Mar 4th Stanford Invite Mens
81 Santa Clara Win 11-7 1742.82 Mar 4th Stanford Invite Mens
33 Victoria Win 13-9 2044.58 Mar 4th Stanford Invite Mens
26 California Win 13-6 2293.11 Mar 5th Stanford Invite Mens
9 California-Santa Cruz Loss 7-11 1428.57 Mar 5th Stanford Invite Mens
20 Washington Loss 10-11 1617.05 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)