#65 Utah (11-7)

avg: 1781.92  •  sd: 71.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
61 California-Davis Loss 7-9 1538.58 Feb 10th Stanford Open 2018
77 Brown Win 8-4 2253.73 Feb 10th Stanford Open 2018
249 California-B** Win 13-2 1024.8 Ignored Feb 10th Stanford Open 2018
76 Pacific Lutheran Win 9-8 1817.71 Feb 10th Stanford Open 2018
138 Santa Clara Win 11-8 1625.15 Feb 11th Stanford Open 2018
77 Brown Win 9-7 1968.26 Feb 11th Stanford Open 2018
61 California-Davis Loss 3-12 1217.92 Feb 11th Stanford Open 2018
68 Boise State Loss 11-13 1533.8 Mar 3rd Big Sky Brawl 2018
- Montana State Win 15-4 1837.94 Mar 3rd Big Sky Brawl 2018
196 Idaho** Win 12-2 1526.06 Ignored Mar 3rd Big Sky Brawl 2018
196 Idaho** Win 11-4 1526.06 Ignored Mar 4th Big Sky Brawl 2018
68 Boise State Win 9-7 2041.98 Mar 4th Big Sky Brawl 2018
34 Northeastern Loss 9-10 1929.9 Mar 24th Womens Centex 2018
11 Texas Loss 8-13 1975.59 Mar 24th Womens Centex 2018
32 Florida Loss 8-11 1714.63 Mar 24th Womens Centex 2018
78 Boston University Loss 10-11 1549.47 Mar 24th Womens Centex 2018
55 Iowa State Win 14-13 1971.46 Mar 25th Womens Centex 2018
109 Texas Christian Win 14-11 1752.88 Mar 25th Womens Centex 2018
**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)