#3 Brigham Young (20-1)

avg: 2117.51  •  sd: 45.89  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
82 California-Santa Barbara** Win 13-1 1875.26 Ignored Jan 27th Santa Barbara Invitational 2023
8 Cal Poly-SLO Win 13-7 2490.48 Jan 27th Santa Barbara Invitational 2023
110 Southern California** Win 15-6 1731.56 Ignored Jan 28th Santa Barbara Invitational 2023
33 Victoria Win 14-9 2099.88 Jan 28th Santa Barbara Invitational 2023
47 Case Western Reserve Win 15-7 2055.38 Jan 28th Santa Barbara Invitational 2023
14 UCLA Win 15-11 2211.99 Jan 28th Santa Barbara Invitational 2023
12 Carleton College Win 13-11 2092.33 Feb 3rd Florida Warm Up 2023
58 Auburn Win 13-7 1957.58 Feb 3rd Florida Warm Up 2023
11 Pittsburgh Win 13-12 1992.72 Feb 3rd Florida Warm Up 2023
90 Virginia Tech** Win 13-4 1845.75 Ignored Feb 3rd Florida Warm Up 2023
5 Vermont Win 13-11 2239.55 Feb 4th Florida Warm Up 2023
18 Brown Win 13-6 2346.16 Feb 4th Florida Warm Up 2023
106 Florida State** Win 13-4 1760.45 Ignored Feb 4th Florida Warm Up 2023
112 Illinois** Win 13-2 1724.34 Ignored Feb 4th Florida Warm Up 2023
12 Carleton College Win 13-10 2191.63 Mar 17th Centex 2023
29 Wisconsin Win 13-10 1994.81 Mar 17th Centex 2023
4 Texas Loss 12-13 1896.51 Mar 17th Centex 2023
13 Tufts Win 13-10 2186.32 Mar 18th Centex 2023
6 Colorado Win 13-11 2221.24 Mar 18th Centex 2023
50 Virginia Win 13-6 2043.6 Mar 18th Centex 2023
28 Georgia Tech Win 13-10 2000.93 Mar 18th Centex 2023
**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)