#40 Colorado College (6-5)

avg: 1553.68  •  sd: 93.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
14 UCLA Loss 8-9 1863.29 Feb 15th Presidents Day Invite 2020
45 Chicago Win 9-7 1796.86 Feb 15th Presidents Day Invite 2020
16 Western Washington Loss 6-12 1382.86 Feb 15th Presidents Day Invite 2020
44 Whitman Loss 8-9 1401.67 Feb 15th Presidents Day Invite 2020
90 Southern California Win 13-7 1682.2 Feb 16th Presidents Day Invite 2020
39 Cal Poly-SLO Loss 10-11 1431.98 Feb 16th Presidents Day Invite 2020
35 Utah Loss 9-10 1449.56 Feb 17th Presidents Day Invite 2020
60 Oregon Win 10-9 1471.76 Feb 17th Presidents Day Invite 2020
120 Denver Win 11-5 1528.84 Mar 7th Air Force Invite 2020
167 Air Force** Win 13-3 1150.71 Ignored Mar 7th Air Force Invite 2020
211 Colorado School of Mines** Win 13-0 758.93 Ignored Mar 7th Air Force Invite 2020
**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)