#11 Utah (12-7)

avg: 2478.8  •  sd: 79.17  •  top 16/20: 99.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
17 California-Santa Barbara Loss 6-8 2003.36 Jan 25th Santa Barbara Invite 2025
25 UCLA Win 11-5 2733.45 Jan 25th Santa Barbara Invite 2025
13 Stanford Win 13-5 3075.08 Jan 25th Santa Barbara Invite 2025
14 Cal Poly-SLO Loss 5-7 2114.95 Jan 26th Santa Barbara Invite 2025
4 Colorado Loss 6-9 2331.07 Jan 26th Santa Barbara Invite 2025
1 British Columbia Loss 4-13 2402.29 Feb 15th Presidents Day Invite 2025
16 California-Davis Win 11-7 2773.58 Feb 15th Presidents Day Invite 2025
22 Western Washington Loss 8-9 2125.67 Feb 15th Presidents Day Invite 2025
1 British Columbia Loss 1-13 2402.29 Feb 16th Presidents Day Invite 2025
104 California-San Diego-B** Win 13-0 1866.85 Ignored Feb 16th Presidents Day Invite 2025
25 UCLA Win 10-8 2396.11 Feb 16th Presidents Day Invite 2025
16 California-Davis Win 13-8 2802.85 Feb 17th Presidents Day Invite 2025
10 California-San Diego Win 11-10 2620.97 Feb 17th Presidents Day Invite 2025
12 California-Santa Cruz Loss 10-11 2352.11 Feb 17th Presidents Day Invite 2025
83 Illinois** Win 13-5 2039.05 Ignored Mar 22nd Womens Centex 2025
89 San Diego State** Win 13-4 1995.48 Ignored Mar 22nd Womens Centex 2025
93 Rice** Win 15-4 1945.62 Ignored Mar 23rd Womens Centex 2025
46 Texas-Dallas** Win 15-6 2422.28 Ignored Mar 23rd Womens Centex 2025
25 UCLA Win 15-12 2433.94 Mar 23rd Womens Centex 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)