#73 California-Santa Barbara (3-17)

avg: 1491.64  •  sd: 54.9  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
2 Brigham Young** Loss 1-13 1718.3 Ignored Jan 27th Santa Barbara Invitational 2023
16 British Columbia Loss 2-14 1392.55 Jan 28th Santa Barbara Invitational 2023
53 Utah Loss 5-9 1090.93 Jan 28th Santa Barbara Invitational 2023
7 Cal Poly-SLO Loss 7-15 1575.35 Jan 28th Santa Barbara Invitational 2023
42 Grand Canyon Win 11-9 1954.49 Jan 28th Santa Barbara Invitational 2023
58 California-San Diego Loss 9-10 1456.41 Jan 29th Santa Barbara Invitational 2023
57 Stanford Loss 5-10 1008.35 Jan 29th Santa Barbara Invitational 2023
47 Colorado State Loss 10-11 1522.22 Feb 18th President’s Day Invite
7 Cal Poly-SLO Loss 5-10 1601.45 Feb 18th President’s Day Invite
17 Washington Loss 8-12 1548.99 Feb 18th President’s Day Invite
9 Oregon** Loss 4-15 1537.14 Ignored Feb 19th President’s Day Invite
32 Oregon State Loss 9-14 1331.86 Feb 19th President’s Day Invite
18 California Loss 6-14 1361.57 Feb 19th President’s Day Invite
58 California-San Diego Win 11-10 1706.41 Feb 20th President’s Day Invite
57 Stanford Loss 9-13 1163.68 Feb 20th President’s Day Invite
32 Oregon State Loss 7-12 1285.22 Mar 4th Stanford Invite Mens
7 Cal Poly-SLO** Loss 2-13 1575.35 Ignored Mar 4th Stanford Invite Mens
18 California Loss 10-11 1836.57 Mar 4th Stanford Invite Mens
57 Stanford Loss 8-9 1457.25 Mar 5th Stanford Invite Mens
109 Southern California Win 11-6 1870.61 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)