#32 Brigham Young (6-14)

avg: 1710.48  •  sd: 47.07  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
4 California-Santa Barbara Loss 5-13 1680.49 Jan 25th Santa Barbara Invite 2019
108 Southern California** Win 13-4 1672.45 Ignored Jan 26th Santa Barbara Invite 2019
24 Washington Loss 5-13 1272.59 Jan 26th Santa Barbara Invite 2019
48 California-Santa Cruz Win 12-9 1866.18 Jan 26th Santa Barbara Invite 2019
23 California Loss 4-13 1317.92 Jan 26th Santa Barbara Invite 2019
1 North Carolina Loss 5-11 1930.07 Feb 9th Queen City Tune Up 2019 Women
20 North Carolina-Wilmington Loss 5-8 1506.57 Feb 9th Queen City Tune Up 2019 Women
43 Georgia Tech Win 10-8 1818.26 Feb 9th Queen City Tune Up 2019 Women
91 Case Western Reserve Win 9-6 1621.23 Feb 9th Queen City Tune Up 2019 Women
9 Texas Loss 4-13 1544 Mar 2nd Stanford Invite 2019
2 California-San Diego Loss 9-13 2000.5 Mar 2nd Stanford Invite 2019
13 Stanford Loss 8-11 1690.02 Mar 2nd Stanford Invite 2019
38 Florida Win 11-7 2078.01 Mar 2nd Stanford Invite 2019
16 Oregon Loss 11-15 1636.56 Mar 29th NW Challenge Tier 1 Womens
7 Western Washington Loss 6-15 1599.67 Mar 29th NW Challenge Tier 1 Womens
5 Carleton College-Syzygy Loss 6-15 1665.5 Mar 29th NW Challenge Tier 1 Womens
6 British Columbia Loss 8-15 1666.96 Mar 29th NW Challenge Tier 1 Womens
3 Ohio State** Loss 3-15 1773 Ignored Mar 30th NW Challenge Tier 1 Womens
11 Pittsburgh Loss 8-12 1642.11 Mar 30th NW Challenge Tier 1 Womens
50 Whitman Win 13-7 2066.43 Mar 30th NW Challenge Tier 1 Womens
**Blowout Eligible

FAQ

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
  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
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)