#86 Colorado-B (19-6)

avg: 1493.4  •  sd: 71.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
154 Brigham Young-B Win 13-5 1844.54 Jan 25th New Year Fest 2025
109 San Diego State Win 12-11 1535.79 Jan 25th New Year Fest 2025
269 Grand Canyon-B** Win 13-3 1398.15 Ignored Jan 25th New Year Fest 2025
187 Arizona Win 13-6 1706.58 Jan 26th New Year Fest 2025
318 Arizona State-B** Win 13-1 1202.98 Ignored Jan 26th New Year Fest 2025
109 San Diego State Win 13-8 1906.95 Jan 26th New Year Fest 2025
99 Tennessee-Chattanooga Loss 7-13 896.08 Feb 22nd Mardi Gras XXXVII
283 Texas-San Antonio Win 13-6 1360.62 Feb 22nd Mardi Gras XXXVII
146 South Florida Win 13-6 1869.39 Feb 22nd Mardi Gras XXXVII
130 LSU Win 13-8 1819.31 Feb 22nd Mardi Gras XXXVII
245 Texas-Dallas Win 11-10 1011.38 Mar 15th Mens Centex 2025
336 Texas A&M-B Win 13-6 1156.45 Mar 15th Mens Centex 2025
191 Oklahoma State Win 13-3 1683.79 Mar 15th Mens Centex 2025
92 Texas A&M Win 13-1 2077.88 Mar 15th Mens Centex 2025
50 Tulane Loss 12-15 1407.6 Mar 16th Mens Centex 2025
133 North Texas Win 15-10 1766.6 Mar 16th Mens Centex 2025
75 Iowa State Loss 3-13 982.5 Mar 16th Mens Centex 2025
357 Colorado Mesa** Win 15-3 1003.64 Ignored Apr 12th Rocky Mountain D I Mens Conferences 2025
42 Colorado State Loss 11-14 1453.31 Apr 12th Rocky Mountain D I Mens Conferences 2025
378 Denver-B** Win 15-4 864.1 Ignored Apr 12th Rocky Mountain D I Mens Conferences 2025
259 Colorado State-B** Win 15-5 1440.19 Ignored Apr 13th Rocky Mountain D I Mens Conferences 2025
114 Denver Win 14-10 1774.12 Apr 13th Rocky Mountain D I Mens Conferences 2025
42 Colorado State Loss 8-15 1201.84 Apr 26th South Central D I College Mens Regionals 2025
160 Kansas Win 14-7 1800.08 Apr 26th South Central D I College Mens Regionals 2025
92 Texas A&M Loss 9-13 1059.31 Apr 27th South Central D I 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)