#29 Utah State (14-12)

avg: 1838.27  •  sd: 47.93  •  top 16/20: 3%

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# Opponent Result Game Rating Status Date Event
151 Arizona State Win 13-6 1746.59 Jan 28th Santa Barbara Invitational 2023
58 California-San Diego Win 13-6 2181.41 Jan 28th Santa Barbara Invitational 2023
10 California-Santa Cruz Loss 11-13 1860.9 Jan 28th Santa Barbara Invitational 2023
17 Washington Win 14-12 2211.1 Jan 28th Santa Barbara Invitational 2023
53 Utah Win 5-3 2038.56 Jan 29th Santa Barbara Invitational 2023
54 Northwestern Win 13-12 1741.19 Jan 29th Santa Barbara Invitational 2023
44 Victoria Loss 11-12 1571.72 Jan 29th Santa Barbara Invitational 2023
15 UCLA Loss 10-12 1790.17 Feb 18th President’s Day Invite
18 California Loss 9-12 1616.2 Feb 18th President’s Day Invite
46 Western Washington Win 11-8 2054.14 Feb 18th President’s Day Invite
57 Stanford Win 12-9 1927.61 Feb 19th President’s Day Invite
7 Cal Poly-SLO Loss 9-10 2050.35 Feb 19th President’s Day Invite
10 California-Santa Cruz Loss 8-12 1648.58 Feb 19th President’s Day Invite
47 Colorado State Win 10-8 1909.89 Feb 20th President’s Day Invite
18 California Loss 10-13 1633.43 Feb 20th President’s Day Invite
9 Oregon Loss 8-13 1640.98 Mar 4th Stanford Invite Mens
44 Victoria Win 13-10 2024.87 Mar 4th Stanford Invite Mens
78 Santa Clara Win 13-4 2075.08 Mar 4th Stanford Invite Mens
16 British Columbia Loss 11-12 1867.55 Mar 5th Stanford Invite Mens
58 California-San Diego Loss 10-11 1456.41 Mar 5th Stanford Invite Mens
16 British Columbia Win 11-8 2358.15 Apr 1st Northwest Challenge Mens
129 Gonzaga Win 14-9 1730.44 Apr 1st Northwest Challenge Mens
46 Western Washington Win 13-10 2016.68 Apr 1st Northwest Challenge Mens
53 Utah Loss 10-12 1381.87 Apr 2nd Northwest Challenge Mens
44 Victoria Win 11-8 2062.33 Apr 2nd Northwest Challenge Mens
17 Washington Loss 6-15 1390.14 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)