#19 Colorado State (8-8)

avg: 1687  •  sd: 64.75  •  top 16/20: 72.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
8 Stanford Loss 6-13 1507.22 Feb 18th President’s Day Invite
23 Carleton College-Eclipse Loss 9-10 1480.11 Feb 18th President’s Day Invite
11 Oregon Loss 8-10 1707.11 Feb 18th President’s Day Invite
29 UCLA Win 10-9 1661.89 Feb 18th President’s Day Invite
85 Southern California** Win 10-1 1556.99 Ignored Feb 19th President’s Day Invite
24 California-Davis Loss 8-11 1227.63 Feb 19th President’s Day Invite
12 California-Santa Barbara Loss 8-11 1567.17 Feb 19th President’s Day Invite
71 Utah Win 13-4 1696.07 Feb 20th President’s Day Invite
49 Texas Loss 8-9 1204.63 Feb 20th President’s Day Invite
30 California Win 13-7 2089.44 Mar 18th Womens Centex1
32 Ohio State Win 13-7 2072.97 Mar 18th Womens Centex1
44 Pennsylvania Win 13-10 1695.73 Mar 18th Womens Centex1
10 Northeastern Loss 11-13 1781.48 Mar 18th Womens Centex1
34 Michigan Win 15-10 1951.33 Mar 19th Womens Centex1
14 Virginia Win 15-14 1933.64 Mar 19th Womens Centex1
10 Northeastern Loss 7-15 1410.32 Mar 19th Womens Centex1
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)