#7 Brigham Young (21-2)

avg: 2246.17  •  sd: 61.09  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
31 California-Santa Barbara Win 13-7 2419.41 Jan 24th Santa Barbara Invite 2025
6 Cal Poly-SLO Win 13-11 2496.81 Jan 24th Santa Barbara Invite 2025
58 Illinois Win 13-8 2169.1 Jan 25th Santa Barbara Invite 2025
8 Washington Loss 10-13 1888.45 Jan 25th Santa Barbara Invite 2025
31 California-Santa Barbara Win 13-7 2419.41 Jan 25th Santa Barbara Invite 2025
42 Colorado State Win 13-5 2384.76 Jan 25th Santa Barbara Invite 2025
27 Minnesota Win 13-7 2449.31 Jan 31st Florida Warm Up 2025
12 Washington University Win 13-10 2432.44 Jan 31st Florida Warm Up 2025
52 Purdue Win 13-7 2271.6 Jan 31st Florida Warm Up 2025
29 Pittsburgh Win 10-7 2268.48 Jan 31st Florida Warm Up 2025
47 Emory Win 13-8 2252.15 Feb 1st Florida Warm Up 2025
50 Tulane Win 13-8 2225.28 Feb 1st Florida Warm Up 2025
20 Vermont Win 13-7 2586.01 Feb 1st Florida Warm Up 2025
126 Central Florida** Win 13-3 1946.48 Ignored Feb 1st Florida Warm Up 2025
23 Western Washington Win 14-13 2113.28 Mar 21st Northwest Challenge 2025 mens
11 Oregon State Win 15-14 2260.73 Mar 21st Northwest Challenge 2025 mens
8 Washington Loss 12-15 1916.1 Mar 21st Northwest Challenge 2025 mens
31 California-Santa Barbara Win 15-11 2243.04 Mar 22nd Northwest Challenge 2025 mens
138 Gonzaga Win 15-11 1694.37 Mar 22nd Northwest Challenge 2025 mens
44 Wisconsin Win 15-9 2292.71 Mar 22nd Northwest Challenge 2025 mens
138 Gonzaga** Win 15-5 1913.2 Ignored Apr 19th Big Sky D I Mens Conferences 2025
289 Montana** Win 15-2 1335.93 Ignored Apr 19th Big Sky D I Mens Conferences 2025
32 Utah State Win 15-6 2452.16 Apr 19th Big Sky D I Mens Conferences 2025
**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)