#120 Denver (14-4)

avg: 928.84  •  sd: 63.53  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
256 Arizona-B** Win 13-0 600 Ignored Jan 25th New Year Fest 2020
54 New Mexico Loss 4-12 797.01 Jan 25th New Year Fest 2020
86 San Diego State University Win 7-6 1299.11 Jan 25th New Year Fest 2020
130 Northern Arizona Win 10-5 1435.72 Jan 25th New Year Fest 2020
148 Arizona State Win 9-7 1012.9 Jan 26th New Year Fest 2020
183 Arizona Win 10-4 1045.82 Jan 26th New Year Fest 2020
54 New Mexico Loss 5-10 823.11 Jan 26th New Year Fest 2020
178 Tulsa Win 10-7 873.92 Feb 22nd Dust Bowl 2020
200 Oklahoma Win 8-5 719.96 Feb 22nd Dust Bowl 2020
167 Air Force Win 9-8 675.71 Feb 22nd Dust Bowl 2020
229 Missouri** Win 7-2 467.53 Ignored Feb 22nd Dust Bowl 2020
73 Truman State Loss 5-12 676.3 Feb 23rd Dust Bowl 2020
135 John Brown Win 9-5 1354.08 Feb 23rd Dust Bowl 2020
192 Grinnell Win 12-9 669.15 Feb 23rd Dust Bowl 2020
211 Colorado School of Mines** Win 15-0 758.93 Ignored Feb 23rd Dust Bowl 2020
211 Colorado School of Mines Win 8-4 723.73 Mar 7th Air Force Invite 2020
167 Air Force Win 7-2 1150.71 Mar 7th Air Force Invite 2020
40 Colorado College Loss 5-11 953.68 Mar 7th Air Force Invite 2020
**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)