#381 Denver-B (5-11)

avg: 287.78  •  sd: 120.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
350 Arizona State-B Win 10-7 901.08 Jan 27th New Year Fest 40
101 Colorado Mines** Loss 4-13 912.08 Ignored Jan 27th New Year Fest 40
58 Utah Valley** Loss 1-13 1137.54 Ignored Jan 27th New Year Fest 40
247 Northern Arizona** Loss 4-13 365.2 Ignored Jan 27th New Year Fest 40
219 Arizona** Loss 3-9 452.48 Ignored Jan 28th New Year Fest 40
350 Arizona State-B Loss 4-9 -88.58 Jan 28th New Year Fest 40
119 Colorado College** Loss 0-13 808.16 Ignored Mar 2nd Snow Melt 2024
405 Colorado State-B Win 8-7 80.72 Mar 2nd Snow Melt 2024
290 Colorado-C Loss 5-8 314.81 Mar 2nd Snow Melt 2024
137 Kansas** Loss 5-13 765.35 Ignored Mar 2nd Snow Melt 2024
405 Colorado State-B Win 8-7 80.72 Mar 3rd Snow Melt 2024
53 Colorado State** Loss 1-15 1156.05 Ignored Apr 13th Rocky Mountain D I Mens Conferences 2024
405 Colorado State-B Win 14-6 555.72 Apr 13th Rocky Mountain D I Mens Conferences 2024
100 Colorado-B** Loss 1-15 913.43 Ignored Apr 13th Rocky Mountain D I Mens Conferences 2024
360 Colorado Mesa Loss 6-15 -152.37 Apr 14th Rocky Mountain D I Mens Conferences 2024
405 Colorado State-B Win 15-5 555.72 Apr 14th Rocky Mountain D I Mens Conferences 2024
**Blowout Eligible

FAQ

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)