#79 Grand Canyon (9-9)

avg: 1340.7  •  sd: 64.9  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
24 British Columbia Loss 10-13 1472.38 Jan 27th Santa Barbara Invite 2024
5 Cal Poly-SLO** Loss 5-15 1574.71 Ignored Jan 27th Santa Barbara Invite 2024
67 Chicago Loss 9-11 1137.81 Jan 27th Santa Barbara Invite 2024
47 Oklahoma Christian Loss 8-11 1154.56 Jan 27th Santa Barbara Invite 2024
151 Cal Poly-SLO-B Win 15-10 1487.54 Jan 28th Santa Barbara Invite 2024
65 Stanford Win 15-11 1786.1 Jan 28th Santa Barbara Invite 2024
44 Tulane Loss 7-9 1262.34 Mar 2nd Stanford Invite 2024
115 Southern California Win 11-9 1433.99 Mar 2nd Stanford Invite 2024
6 Oregon** Loss 2-13 1509.26 Ignored Mar 2nd Stanford Invite 2024
40 Illinois Loss 6-8 1279.19 Mar 2nd Stanford Invite 2024
43 California-San Diego Loss 4-11 962.26 Mar 3rd Stanford Invite 2024
54 California-Santa Barbara Loss 4-11 869.64 Mar 3rd Stanford Invite 2024
227 Cal State-Long Beach** Win 12-2 1322 Ignored Mar 30th 2024 Sinvite
330 California-San Diego-B** Win 10-4 771.51 Ignored Mar 30th 2024 Sinvite
285 Southern California-B** Win 12-2 1027.27 Ignored Mar 30th 2024 Sinvite
129 San Jose State Win 7-5 1454.59 Mar 30th 2024 Sinvite
227 Cal State-Long Beach** Win 13-3 1322 Ignored Mar 31st 2024 Sinvite
129 San Jose State Win 11-5 1726.45 Mar 31st 2024 Sinvite
**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)