#190 Colorado-B (1-11)

avg: 229.18  •  sd: 98.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
91 Colorado College** Loss 4-11 455.06 Ignored Feb 18th Snow Melt 2023
123 Denver Loss 7-9 541.69 Feb 18th Snow Melt 2023
43 Whitman** Loss 1-11 899 Ignored Feb 18th Snow Melt 2023
117 Arizona Loss 2-4 388.43 Feb 19th Snow Melt 2023
164 Colorado Mines Loss 5-7 159.09 Feb 19th Snow Melt 2023
104 Iowa Loss 8-13 470.7 Mar 18th Womens Centex1
179 LSU Win 9-8 472.6 Mar 18th Womens Centex1
112 Rice** Loss 1-13 307.62 Ignored Mar 18th Womens Centex1
109 Texas State Loss 6-9 528.22 Mar 18th Womens Centex1
171 Illinois Loss 5-10 -164.91 Mar 19th Womens Centex1
182 Texas-San Antonio Loss 6-8 -24.62 Mar 19th Womens Centex1
207 Northwestern-B Loss 4-6 -355.05 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)