#186 Arizona (11-8)

avg: 1120  •  sd: 53.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
316 Arizona State-B Win 16-3 1218.09 Jan 25th New Year Fest 2025
249 Northern Arizona Win 14-12 1106.51 Jan 25th New Year Fest 2025
116 Denver Loss 8-17 784.14 Jan 25th New Year Fest 2025
156 Brigham Young-B Win 13-11 1472.52 Jan 25th New Year Fest 2025
269 Grand Canyon-B Win 13-5 1407.39 Jan 26th New Year Fest 2025
86 Colorado-B Loss 6-13 909.79 Jan 26th New Year Fest 2025
109 San Diego State Loss 9-10 1303.77 Feb 15th Vice Presidents Day Invite 2025
237 Loyola Marymount Win 10-5 1499.27 Feb 15th Vice Presidents Day Invite 2025
84 Southern California Loss 5-11 919.35 Feb 15th Vice Presidents Day Invite 2025
124 San Jose State Loss 7-11 892.05 Feb 16th Vice Presidents Day Invite 2025
237 Loyola Marymount Win 9-7 1204.71 Feb 16th Vice Presidents Day Invite 2025
249 Northern Arizona Win 13-9 1304.12 Mar 29th Sinvite 2025
285 Southern California-B Win 12-4 1353.46 Mar 29th Sinvite 2025
376 San Diego State-B** Win 12-5 866.51 Ignored Mar 29th Sinvite 2025
156 Brigham Young-B Loss 10-11 1118.68 Mar 29th Sinvite 2025
350 California-San Diego-B** Win 10-4 1036.28 Ignored Mar 30th Sinvite 2025
285 Southern California-B Win 11-10 878.46 Mar 30th Sinvite 2025
120 Arizona State Loss 11-12 1240.92 Apr 13th Desert D I Mens Conferences 2025
249 Northern Arizona Loss 8-10 622.89 Apr 13th Desert D I Mens Conferences 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)