#8 Colorado (13-6)

avg: 2095.44  •  sd: 73.35  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
271 San Diego State** Win 13-2 1380.82 Ignored Feb 16th Presidents Day Invite 2019
93 California-Davis Win 12-6 1956.86 Feb 16th Presidents Day Invite 2019
16 Southern California Loss 6-8 1675.66 Feb 17th Presidents Day Invite 2019
21 California Loss 6-7 1718.46 Feb 17th Presidents Day Invite 2019
56 California-San Diego Win 11-4 2192.76 Feb 18th Presidents Day Invite 2019
34 UCLA Loss 8-9 1603.73 Feb 18th Presidents Day Invite 2019
17 Minnesota Win 10-9 2076.05 Mar 2nd Stanford Invite 2019
5 Cal Poly-SLO Loss 10-11 2019.46 Mar 2nd Stanford Invite 2019
12 Texas Loss 7-10 1620.24 Mar 2nd Stanford Invite 2019
2 Brown Loss 9-11 1979.95 Mar 3rd Stanford Invite 2019
7 Carleton College-CUT Win 11-8 2484.25 Mar 3rd Stanford Invite 2019
13 Wisconsin Win 10-7 2390.63 Mar 3rd Stanford Invite 2019
3 Oregon Win 11-10 2313.99 Mar 3rd Stanford Invite 2019
27 LSU Win 13-8 2273.9 Mar 16th Centex 2019 Men
29 Texas-Dallas Win 13-9 2190.47 Mar 16th Centex 2019 Men
19 Colorado State Win 13-12 2024.55 Mar 16th Centex 2019 Men
40 Dartmouth Win 14-10 2085.17 Mar 17th Centex 2019 Men
12 Texas Win 15-7 2609.9 Mar 17th Centex 2019 Men
13 Wisconsin Win 14-13 2125.97 Mar 17th Centex 2019 Men
**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)