#71 Grand Canyon (13-11)

avg: 1629.57  •  sd: 52.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
21 British Columbia Loss 10-13 1721.74 Jan 27th Santa Barbara Invite 2024
5 Cal Poly-SLO** Loss 5-15 1796.85 Ignored Jan 27th Santa Barbara Invite 2024
61 Chicago Loss 9-11 1445.12 Jan 27th Santa Barbara Invite 2024
41 Oklahoma Christian Loss 8-11 1487.26 Jan 27th Santa Barbara Invite 2024
121 Cal Poly-SLO-B Win 15-10 1854.85 Jan 28th Santa Barbara Invite 2024
67 Stanford Win 15-11 2052.73 Jan 28th Santa Barbara Invite 2024
43 Tulane Loss 7-9 1559.08 Mar 2nd Stanford Invite 2024
113 Southern California Win 11-9 1703.24 Mar 2nd Stanford Invite 2024
7 Oregon** Loss 2-13 1730.08 Ignored Mar 2nd Stanford Invite 2024
39 Illinois Loss 6-8 1584.88 Mar 2nd Stanford Invite 2024
34 California-San Diego Loss 4-11 1303.45 Mar 3rd Stanford Invite 2024
60 California-Santa Barbara Loss 4-11 1115.6 Mar 3rd Stanford Invite 2024
255 Cal State-Long Beach** Win 12-2 1522.79 Ignored Mar 30th 2024 Sinvite
332 California-San Diego-B** Win 10-4 1191.08 Ignored Mar 30th 2024 Sinvite
298 Southern California-B** Win 12-2 1320.15 Ignored Mar 30th 2024 Sinvite
124 San Jose State Win 7-5 1720.48 Mar 30th 2024 Sinvite
255 Cal State-Long Beach** Win 13-3 1522.79 Ignored Mar 31st 2024 Sinvite
124 San Jose State Win 11-5 1992.34 Mar 31st 2024 Sinvite
219 Arizona Win 15-11 1433.65 Apr 14th Desert D I Mens Conferences 2024
133 Arizona State Win 12-7 1895.6 Apr 14th Desert D I Mens Conferences 2024
5 Cal Poly-SLO Loss 9-15 1881.37 Apr 27th Southwest D I College Mens Regionals 2024
124 San Jose State Win 12-11 1517.34 Apr 27th Southwest D I College Mens Regionals 2024
144 Santa Clara Loss 11-12 1211.2 Apr 27th Southwest D I College Mens Regionals 2024
125 California-Irvine Win 12-8 1833.07 Apr 28th Southwest D I College Mens Regionals 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)