#190 Colorado-B (1-11)

avg: 102.25  •  sd: 92.19  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
99 Colorado College** Loss 4-11 319.39 Ignored Feb 18th Snow Melt 2023
143 Denver Loss 7-9 283.06 Feb 18th Snow Melt 2023
48 Whitman** Loss 1-11 748.04 Ignored Feb 18th Snow Melt 2023
123 Arizona Loss 2-4 189.81 Feb 19th Snow Melt 2023
167 Colorado Mines Loss 5-7 -2.35 Feb 19th Snow Melt 2023
102 Iowa Loss 8-13 363.35 Mar 18th Womens Centex1
175 LSU Win 9-8 355.13 Mar 18th Womens Centex1
112 Rice** Loss 1-13 201.42 Ignored Mar 18th Womens Centex1
106 Texas State Loss 6-9 421.11 Mar 18th Womens Centex1
159 Illinois Loss 5-10 -202.32 Mar 19th Womens Centex1
183 Texas-San Antonio Loss 6-8 -141.51 Mar 19th Womens Centex1
203 Northwestern-B Loss 4-6 -460.81 Mar 19th Womens Centex1
**Blowout Eligible


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)