#78 Grand Canyon (13-5)

avg: 1219.9  •  sd: 70.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
180 Arizona Win 10-5 1111.49 Jan 25th New Year Fest 2025
114 Arizona State Win 8-7 1080.88 Jan 25th New Year Fest 2025
242 Arizona-B** Win 13-1 644.21 Ignored Jan 25th New Year Fest 2025
187 Colorado-B Win 11-6 1044.58 Jan 25th New Year Fest 2025
107 Denver Win 10-8 1293.85 Jan 26th New Year Fest 2025
106 San Diego State Loss 6-9 621.15 Jan 26th New Year Fest 2025
114 Arizona State Win 8-4 1520.69 Mar 29th Canyon Classic
114 Arizona State Win 9-3 1555.88 Mar 29th Canyon Classic
134 Northern Arizona Loss 6-7 691.53 Mar 29th Canyon Classic
134 Northern Arizona Win 8-5 1270.14 Mar 29th Canyon Classic
180 Arizona** Win 11-3 1137.6 Ignored Apr 12th Desert D I Womens Conferences 2025
114 Arizona State Win 11-6 1502.58 Apr 12th Desert D I Womens Conferences 2025
10 California-San Diego** Loss 1-15 1513.72 Ignored Apr 26th Southwest D I College Womens Regionals 2025
123 California-San Diego-B Win 13-9 1334.31 Apr 26th Southwest D I College Womens Regionals 2025
106 San Diego State Win 13-8 1535.88 Apr 26th Southwest D I College Womens Regionals 2025
41 Southern California Loss 10-15 1120 Apr 26th Southwest D I College Womens Regionals 2025
114 Arizona State Win 11-9 1205.09 Apr 27th Southwest D I College Womens Regionals 2025
68 Santa Clara Loss 10-13 971.93 Apr 27th Southwest D I College Womens Regionals 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)