#76 Utah (14-10)

avg: 1473.73  •  sd: 66.54  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
56 California-San Diego Loss 7-8 1467.76 Feb 16th Presidents Day Invite 2019
45 California-Santa Barbara Win 9-6 2081.82 Feb 16th Presidents Day Invite 2019
5 Cal Poly-SLO** Loss 3-10 1544.46 Ignored Feb 17th Presidents Day Invite 2019
100 California-Santa Cruz Win 8-7 1483.77 Feb 17th Presidents Day Invite 2019
93 California-Davis Win 8-6 1678.04 Feb 18th Presidents Day Invite 2019
37 Illinois Win 10-9 1845.39 Feb 18th Presidents Day Invite 2019
200 Montana Win 12-8 1425.39 Mar 2nd Big Sky Brawl 2019
305 Boise State** Win 15-4 1242.33 Ignored Mar 2nd Big Sky Brawl 2019
133 Utah State Win 10-9 1370.27 Mar 2nd Big Sky Brawl 2019
191 Montana State Win 11-10 1149.96 Mar 3rd Big Sky Brawl 2019
280 Idaho Win 15-7 1354.23 Mar 3rd Big Sky Brawl 2019
238 Denver Win 13-10 1225.94 Mar 3rd Big Sky Brawl 2019
46 Iowa State Win 13-9 2077.8 Mar 16th Centex 2019 Men
19 Colorado State Loss 11-12 1774.55 Mar 16th Centex 2019 Men
80 Oklahoma Win 12-7 1972.48 Mar 16th Centex 2019 Men
82 Texas State Loss 10-11 1317.65 Mar 16th Centex 2019 Men
31 Texas A&M Loss 9-15 1232.93 Mar 17th Centex 2019 Men
29 Texas-Dallas Win 14-12 1992.86 Mar 17th Centex 2019 Men
74 Arizona Loss 7-13 921.56 Mar 23rd Trouble in Vegas 2019
125 Colorado School of Mines Loss 9-10 1153.32 Mar 23rd Trouble in Vegas 2019
34 UCLA Loss 10-13 1400.59 Mar 23rd Trouble in Vegas 2019
16 Southern California Loss 7-13 1418.62 Mar 24th Trouble in Vegas 2019
116 Nevada-Reno Loss 9-12 948.35 Mar 24th Trouble in Vegas 2019
184 California-B Win 13-9 1451.08 Mar 24th Trouble in Vegas 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)