#17 Brigham Young (16-11)

avg: 1875.44  •  sd: 35.53  •  top 16/20: 85.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
54 California-Santa Barbara Win 13-10 1797.78 Jan 26th Santa Barbara Invite 2024
5 Cal Poly-SLO Loss 9-14 1700.84 Jan 26th Santa Barbara Invite 2024
43 California-San Diego Win 15-14 1687.26 Jan 27th Santa Barbara Invite 2024
23 UCLA Win 14-12 2029.4 Jan 27th Santa Barbara Invite 2024
83 Northwestern Win 15-2 1935.48 Jan 27th Santa Barbara Invite 2024
22 Washington Loss 14-15 1694 Jan 27th Santa Barbara Invite 2024
21 Tufts Win 13-10 2156.85 Feb 2nd Florida Warm Up 2024
33 Wisconsin Win 13-10 1973.64 Feb 2nd Florida Warm Up 2024
2 Georgia Loss 10-12 2034.69 Feb 2nd Florida Warm Up 2024
4 Massachusetts Loss 8-13 1738.8 Feb 2nd Florida Warm Up 2024
12 Alabama-Huntsville Loss 9-11 1744.47 Feb 3rd Florida Warm Up 2024
42 Michigan Win 13-12 1691.42 Feb 3rd Florida Warm Up 2024
41 Florida Win 13-9 1989.59 Feb 3rd Florida Warm Up 2024
37 Texas A&M Win 13-8 2087.1 Feb 3rd Florida Warm Up 2024
37 Texas A&M Win 13-6 2190.94 Mar 15th College Mens Centex Tier 1
53 Colorado State Win 13-10 1798.7 Mar 15th College Mens Centex Tier 1
14 Texas Loss 12-13 1811.64 Mar 15th College Mens Centex Tier 1
41 Florida Win 13-9 1989.59 Mar 16th College Mens Centex Tier 1
128 Colorado College** Win 13-4 1736 Ignored Mar 16th College Mens Centex Tier 1
44 Tulane Win 13-6 2141.68 Mar 16th College Mens Centex Tier 1
20 Northeastern Win 10-7 2219.99 Mar 16th College Mens Centex Tier 1
10 Carleton College Loss 13-15 1796.16 Mar 22nd Northwest Challenge Mens 2024
18 Oregon State Loss 13-14 1744.3 Mar 22nd Northwest Challenge Mens 2024
22 Washington Loss 14-15 1694 Mar 22nd Northwest Challenge Mens 2024
15 California Loss 8-12 1483.06 Mar 23rd Northwest Challenge Mens 2024
63 Western Washington Win 15-10 1875.84 Mar 23rd Northwest Challenge Mens 2024
6 Oregon Loss 12-15 1808.77 Mar 23rd Northwest Challenge Mens 2024
**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)