#290 Colorado-C (5-5)

avg: 768.41  •  sd: 126.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
119 Colorado College** Loss 4-13 808.16 Ignored Mar 2nd Snow Melt 2024
137 Kansas Loss 4-13 765.35 Mar 2nd Snow Melt 2024
381 Denver-B Win 8-5 741.38 Mar 2nd Snow Melt 2024
405 Colorado State-B Win 13-9 374.29 Mar 3rd Snow Melt 2024
1 Colorado** Loss 3-15 1899.73 Ignored Apr 13th Rocky Mountain D I Mens Conferences 2024
360 Colorado Mesa Win 15-8 1012.44 Apr 13th Rocky Mountain D I Mens Conferences 2024
156 Denver Loss 6-15 696.21 Apr 13th Rocky Mountain D I Mens Conferences 2024
360 Colorado Mesa Win 15-9 963.11 Apr 14th Rocky Mountain D I Mens Conferences 2024
405 Colorado State-B** Win 15-5 555.72 Ignored Apr 14th Rocky Mountain D I Mens Conferences 2024
156 Denver Loss 0-15 696.21 Apr 14th Rocky Mountain D I Mens Conferences 2024
**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)