#9 Colorado (15-5)

avg: 2499.69  •  sd: 64.2  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
26 California Win 13-8 2629.39 Feb 17th Presidents Day Invitational Tournament 2018
20 Washington Win 13-7 2797.77 Feb 17th Presidents Day Invitational Tournament 2018
43 Southern California Win 10-6 2486.44 Feb 17th Presidents Day Invitational Tournament 2018
33 UCLA Win 11-4 2662.69 Feb 18th Presidents Day Invitational Tournament 2018
26 California Win 11-7 2600.12 Feb 18th Presidents Day Invitational Tournament 2018
36 Colorado College Win 14-6 2633.18 Feb 18th Presidents Day Invitational Tournament 2018
11 Texas Win 10-9 2596.75 Feb 19th Presidents Day Invitational Tournament 2018
4 Stanford Loss 5-9 2166.46 Feb 19th Presidents Day Invitational Tournament 2018
14 Whitman Win 11-6 2932.83 Mar 3rd Stanford Invite 2018
5 Oregon Loss 9-12 2265.16 Mar 3rd Stanford Invite 2018
10 Pittsburgh Loss 12-13 2356.75 Mar 3rd Stanford Invite 2018
16 Western Washington Win 13-6 2944.39 Mar 4th Stanford Invite 2018
12 Carleton College Loss 9-13 2003.4 Mar 4th Stanford Invite 2018
20 Washington Win 12-11 2365.23 Mar 4th Stanford Invite 2018
33 UCLA Win 12-5 2662.69 Mar 24th Womens Centex 2018
41 Georgia Tech Win 10-7 2399.08 Mar 24th Womens Centex 2018
123 MIT** Win 13-2 1959.23 Ignored Mar 24th Womens Centex 2018
34 Northeastern Win 14-9 2528.77 Mar 25th Womens Centex 2018
37 Northwestern Win 15-7 2628.18 Mar 25th Womens Centex 2018
32 Florida Loss 12-13 1955.24 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)