#56 Colorado State (7-13)

avg: 1367.79  •  sd: 50.2  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
94 California-Santa Barbara Win 11-10 1334.54 Feb 18th President’s Day Invite
8 Cal Poly-SLO Loss 9-12 1521.32 Feb 18th President’s Day Invite
19 Washington Loss 6-13 1075.21 Feb 18th President’s Day Invite
74 California-San Diego Win 12-11 1438.09 Feb 19th President’s Day Invite
44 Grand Canyon Loss 7-9 1165.17 Feb 19th President’s Day Invite
14 UCLA Loss 5-14 1163.56 Feb 19th President’s Day Invite
42 Emory Loss 10-12 1216.46 Feb 20th President’s Day Invite
30 Utah State Loss 8-10 1322.07 Feb 20th President’s Day Invite
16 British Columbia Loss 9-13 1292.17 Mar 4th Stanford Invite Mens
19 Washington Win 13-12 1800.21 Mar 4th Stanford Invite Mens
76 Stanford Win 10-8 1569.17 Mar 4th Stanford Invite Mens
24 California Loss 9-13 1207.76 Mar 5th Stanford Invite Mens
28 Wisconsin Loss 7-12 1075.38 Mar 5th Stanford Invite Mens
46 Colorado College Loss 8-10 1165.83 Mar 18th Centex 2023
124 Illinois Win 13-6 1656.06 Mar 18th Centex 2023
28 Wisconsin Loss 11-12 1470.9 Mar 18th Centex 2023
40 Florida Loss 10-12 1224.38 Mar 18th Centex 2023
104 Tulane Win 12-9 1487.04 Mar 19th Centex 2023
71 Northwestern Loss 13-15 1107.5 Mar 19th Centex 2023
52 Virginia Win 14-8 1914.03 Mar 19th Centex 2023
**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)