#30 Utah (11-7)

avg: 1758.87  •  sd: 87.13  •  top 16/20: 2.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
138 Oregon State** Win 14-4 1534.17 Ignored Feb 2nd Big Sky Brawl 2019
152 Montana State Win 13-6 1461.75 Feb 2nd Big Sky Brawl 2019
74 Denver Win 10-8 1584.54 Feb 2nd Big Sky Brawl 2019
120 Arizona State** Win 10-3 1627.17 Ignored Feb 3rd Big Sky Brawl 2019
90 Colorado State Loss 8-10 954.46 Feb 3rd Big Sky Brawl 2019
216 Montana** Win 15-0 1032.17 Ignored Feb 3rd Big Sky Brawl 2019
21 Cal Poly-SLO Loss 7-9 1664.26 Feb 16th Presidents Day Invite 2019
13 Stanford Loss 6-8 1755.14 Feb 16th Presidents Day Invite 2019
29 Northwestern Win 9-6 2186.18 Feb 17th Presidents Day Invite 2019
19 UCLA Loss 6-8 1665.36 Feb 17th Presidents Day Invite 2019
23 California Loss 4-8 1353.11 Feb 18th Presidents Day Invite 2019
40 Michigan Win 10-5 2143.33 Mar 23rd Womens College Centex 2019
72 Texas-Dallas Win 13-6 1926.4 Mar 23rd Womens College Centex 2019
43 Georgia Tech Win 10-4 2155.59 Mar 23rd Womens College Centex 2019
14 Colorado Win 11-10 2171.85 Mar 24th Womens College Centex 2019
10 Northeastern Loss 8-13 1611.79 Mar 24th Womens College Centex 2019
22 Tufts Loss 7-14 1351.72 Mar 24th Womens College Centex 2019
42 Chicago Win 14-10 1963.2 Mar 24th Womens College Centex 2019
**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)