#156 Denver (9-10)

avg: 1296.21  •  sd: 87.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
219 Arizona Win 12-7 1572.99 Jan 27th New Year Fest 40
180 Brigham Young-B Win 13-6 1794.94 Jan 27th New Year Fest 40
100 Colorado-B Loss 10-12 1275.3 Jan 27th New Year Fest 40
137 Kansas Win 12-7 1885.86 Jan 27th New Year Fest 40
101 Colorado Mines Loss 8-11 1146.47 Jan 28th New Year Fest 40
100 Colorado-B Loss 6-13 913.43 Jan 28th New Year Fest 40
247 Northern Arizona Win 13-4 1565.2 Jan 28th New Year Fest 40
119 Colorado College Win 11-7 1875.05 Mar 2nd Snow Melt 2024
405 Colorado State-B** Win 13-1 555.72 Ignored Mar 2nd Snow Melt 2024
58 Utah Valley Loss 6-13 1137.54 Mar 2nd Snow Melt 2024
100 Colorado-B Loss 8-15 948.62 Mar 3rd Snow Melt 2024
58 Utah Valley Loss 10-11 1612.54 Mar 3rd Snow Melt 2024
1 Colorado** Loss 5-15 1899.73 Ignored Apr 13th Rocky Mountain D I Mens Conferences 2024
360 Colorado Mesa** Win 15-3 1047.63 Ignored Apr 13th Rocky Mountain D I Mens Conferences 2024
290 Colorado-C Win 15-6 1368.41 Apr 13th Rocky Mountain D I Mens Conferences 2024
100 Colorado-B Loss 7-15 913.43 Apr 14th Rocky Mountain D I Mens Conferences 2024
290 Colorado-C Win 15-0 1368.41 Apr 14th Rocky Mountain D I Mens Conferences 2024
161 Saint Louis Loss 10-12 1045.35 Apr 27th South Central D I College Mens Regionals 2024
128 Houston Loss 7-10 998.66 Apr 27th South Central D I College Mens Regionals 2024
**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)