#71 Utah (4-21)

avg: 1096.07  •  sd: 72.25  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
54 Cal Poly-SLO Win 14-2 1852.19 Jan 28th Santa Barbara Invitational 2023
12 California-Santa Barbara** Loss 4-14 1332.78 Ignored Jan 28th Santa Barbara Invitational 2023
74 Lewis & Clark Win 12-7 1569.75 Jan 28th Santa Barbara Invitational 2023
29 UCLA Loss 8-12 1095.74 Jan 28th Santa Barbara Invitational 2023
30 California Loss 8-9 1406.91 Jan 29th Santa Barbara Invitational 2023
18 California-San Diego Loss 7-10 1307.83 Jan 29th Santa Barbara Invitational 2023
17 Victoria** Loss 3-11 1125.75 Ignored Jan 29th Santa Barbara Invitational 2023
3 Colorado** Loss 3-14 1740.96 Ignored Feb 18th President’s Day Invite
54 Cal Poly-SLO Loss 5-8 798.58 Feb 18th President’s Day Invite
30 California Loss 3-13 931.91 Feb 18th President’s Day Invite
24 California-Davis Loss 4-11 993.24 Feb 18th President’s Day Invite
8 Stanford** Loss 4-11 1507.22 Ignored Feb 19th President’s Day Invite
54 Cal Poly-SLO Win 6-5 1377.19 Feb 19th President’s Day Invite
25 Duke Loss 4-9 968.74 Feb 19th President’s Day Invite
19 Colorado State Loss 4-13 1087 Feb 20th President’s Day Invite
29 UCLA Loss 6-10 1040.73 Feb 20th President’s Day Invite
- Idaho Win 5-4 946.76 Mar 4th Big Sky Brawl1
- Montana State Loss 5-8 889.55 Mar 4th Big Sky Brawl1
75 Nevada-Reno Loss 7-8 915.03 Mar 4th Big Sky Brawl1
76 Oregon State Loss 3-7 439.43 Mar 4th Big Sky Brawl1
8 Stanford** Loss 3-13 1507.22 Ignored Mar 25th Northwest Challenge1
3 Colorado** Loss 0-13 1740.96 Ignored Mar 25th Northwest Challenge1
17 Victoria Loss 7-13 1168.22 Mar 25th Northwest Challenge1
49 Texas Loss 10-11 1204.63 Mar 26th Northwest Challenge1
29 UCLA Loss 6-13 936.89 Mar 26th Northwest Challenge1
**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)