#89 Colorado College (18-8)

avg: 1485.28  •  sd: 62.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
319 Pacific Lutheran** Win 13-4 1201.14 Ignored Feb 8th DIII Grand Prix 2025
36 Lewis & Clark Loss 8-12 1355.9 Feb 8th DIII Grand Prix 2025
141 Puget Sound Win 13-10 1616.2 Feb 8th DIII Grand Prix 2025
107 Claremont Win 13-2 2028.35 Feb 9th DIII Grand Prix 2025
43 Whitman Win 11-9 2014.08 Feb 9th DIII Grand Prix 2025
199 Occidental Win 13-6 1658.14 Feb 9th DIII Grand Prix 2025
154 Brigham Young-B Win 13-6 1844.54 Feb 28th Snow Melt 2025
281 California-B** Win 13-5 1361.82 Ignored Mar 1st Snow Melt 2025
378 Denver-B** Win 13-4 864.1 Ignored Mar 1st Snow Melt 2025
117 Colorado Mines Loss 12-13 1238.26 Mar 1st Snow Melt 2025
114 Denver Win 15-14 1500.42 Mar 2nd Snow Melt 2025
36 Lewis & Clark Loss 9-15 1281.58 Mar 2nd Snow Melt 2025
137 Montana State Win 13-10 1633.47 Mar 2nd Snow Melt 2025
121 Arizona State Win 9-8 1472.3 Mar 15th Mens Centex 2025
118 Mississippi State Win 8-5 1810.85 Mar 15th Mens Centex 2025
59 Illinois Loss 4-11 1050.19 Mar 15th Mens Centex 2025
40 Middlebury Win 10-8 2036.95 Mar 15th Mens Centex 2025
49 Chicago Loss 10-15 1275.72 Mar 16th Mens Centex 2025
59 Illinois Loss 11-15 1269.03 Mar 16th Mens Centex 2025
230 Air Force Win 15-5 1527.9 Apr 12th Rocky Mountain D III Mens Conferences 2025
117 Colorado Mines Win 14-10 1761.96 Apr 12th Rocky Mountain D III Mens Conferences 2025
230 Air Force Win 14-6 1527.9 Apr 26th South Central D III College Mens Regionals 2025
195 John Brown Win 12-11 1194.63 Apr 26th South Central D III College Mens Regionals 2025
117 Colorado Mines Win 11-9 1612.47 Apr 27th South Central D III College Mens Regionals 2025
57 Oklahoma Christian Loss 5-12 1056.39 Apr 27th South Central D III College Mens Regionals 2025
87 Missouri S&T Loss 8-12 1049.93 Apr 27th South Central D III College Mens Regionals 2025
**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)