#113 Denver (9-16)

avg: 1064.82  •  sd: 67.22  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
67 Arizona Loss 8-9 1308.92 Jan 27th New Year Fest 40
177 Arizona-B Win 11-6 1023.26 Jan 27th New Year Fest 40
65 Grand Canyon Loss 5-9 919.31 Jan 27th New Year Fest 40
83 Northern Arizona Loss 7-10 940.43 Jan 27th New Year Fest 40
73 San Diego State Loss 6-7 1263.37 Jan 28th New Year Fest 40
116 Arizona State Win 7-3 1633.73 Jan 28th New Year Fest 40
24 California-Davis** Loss 2-14 1371.09 Ignored Feb 17th Presidents Day Invite 2024
15 California-San Diego** Loss 2-15 1562.55 Ignored Feb 17th Presidents Day Invite 2024
5 Oregon** Loss 0-15 2005.7 Ignored Feb 17th Presidents Day Invite 2024
23 Cal Poly-SLO** Loss 4-15 1371.29 Ignored Feb 18th Presidents Day Invite 2024
30 California** Loss 2-12 1284.55 Ignored Feb 18th Presidents Day Invite 2024
124 Claremont Loss 6-9 551.25 Feb 18th Presidents Day Invite 2024
55 Southern California Loss 6-10 1059.93 Feb 19th Presidents Day Invite 2024
69 California-San Diego-B Loss 6-9 1003.81 Feb 19th Presidents Day Invite 2024
199 Colorado College-B** Win 11-2 844.9 Ignored Mar 2nd Snow Melt 2024
169 Colorado Mines Win 12-0 1169.56 Mar 2nd Snow Melt 2024
60 Colorado College Loss 5-15 918.43 Mar 3rd Snow Melt 2024
121 John Brown Win 10-5 1580.41 Mar 3rd Snow Melt 2024
55 Southern California Loss 4-14 956.09 Mar 16th Womens Centex 2024
60 Colorado College Loss 6-13 918.43 Mar 16th Womens Centex 2024
213 North Texas** Win 13-2 653.19 Ignored Mar 16th Womens Centex 2024
186 Texas-San Antonio Win 13-7 964.56 Mar 16th Womens Centex 2024
153 Texas A&M Win 11-5 1365.22 Mar 16th Womens Centex 2024
33 Central Florida** Loss 1-13 1219.58 Ignored Mar 17th Womens Centex 2024
158 Texas State Win 10-9 835.11 Mar 17th Womens Centex 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)