#90 Colorado State (9-9)

avg: 1217.13  •  sd: 83.57  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
37 Washington University Loss 5-12 1070.56 Jan 26th Santa Barbara Invite 2019
39 California-Davis Loss 7-11 1126.39 Jan 26th Santa Barbara Invite 2019
4 California-Santa Barbara** Loss 5-13 1680.49 Ignored Jan 26th Santa Barbara Invite 2019
84 Victoria Loss 6-11 698.66 Jan 27th Santa Barbara Invite 2019
108 Southern California Win 11-7 1539.34 Jan 27th Santa Barbara Invite 2019
120 Arizona State Win 10-5 1601.07 Feb 2nd Big Sky Brawl 2019
123 Boise State Win 9-7 1298.71 Feb 2nd Big Sky Brawl 2019
216 Montana** Win 14-2 1032.17 Ignored Feb 2nd Big Sky Brawl 2019
152 Montana State Win 14-3 1461.75 Feb 3rd Big Sky Brawl 2019
123 Boise State Win 13-2 1619.37 Feb 3rd Big Sky Brawl 2019
30 Utah Win 10-8 2021.53 Feb 3rd Big Sky Brawl 2019
59 Duke Loss 6-13 845.96 Mar 23rd Womens College Centex 2019
43 Georgia Tech Loss 5-8 1101.99 Mar 23rd Womens College Centex 2019
72 Texas-Dallas Win 9-8 1451.4 Mar 23rd Womens College Centex 2019
89 Iowa State Loss 6-15 623.1 Mar 24th Womens College Centex 2019
34 Colorado College Loss 6-10 1207.62 Mar 24th Womens College Centex 2019
102 LSU Win 13-12 1244.43 Mar 24th Womens College Centex 2019
72 Texas-Dallas Loss 7-9 1047.06 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)