#43 California-San Diego (6-14)

avg: 1562.26  •  sd: 52.54  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
17 Brigham Young Loss 14-15 1750.44 Jan 27th Santa Barbara Invite 2024
83 Northwestern Win 15-9 1850.96 Jan 27th Santa Barbara Invite 2024
22 Washington Loss 7-15 1219 Jan 27th Santa Barbara Invite 2024
23 UCLA Loss 12-13 1683.44 Jan 27th Santa Barbara Invite 2024
39 Victoria Loss 9-12 1240.37 Jan 28th Santa Barbara Invite 2024
47 Oklahoma Christian Loss 11-12 1395.17 Jan 28th Santa Barbara Invite 2024
53 Colorado State Win 14-12 1691.51 Jan 28th Santa Barbara Invite 2024
3 Colorado** Loss 5-15 1643.12 Ignored Feb 17th Presidents Day Invite 2024
65 Stanford Win 10-7 1794.6 Feb 17th Presidents Day Invite 2024
30 Utah Loss 10-12 1438.87 Feb 17th Presidents Day Invite 2024
24 British Columbia Loss 9-10 1675.53 Feb 18th Presidents Day Invite 2024
134 California-Irvine Win 14-7 1691.98 Feb 18th Presidents Day Invite 2024
54 California-Santa Barbara Win 12-8 1910.8 Feb 18th Presidents Day Invite 2024
24 British Columbia Loss 8-9 1675.53 Feb 19th Presidents Day Invite 2024
23 UCLA Loss 4-15 1208.44 Feb 19th Presidents Day Invite 2024
40 Illinois Loss 10-11 1454.68 Mar 2nd Stanford Invite 2024
33 Wisconsin Loss 11-12 1520.5 Mar 2nd Stanford Invite 2024
44 Tulane Loss 6-7 1416.68 Mar 2nd Stanford Invite 2024
79 Grand Canyon Win 11-4 1940.7 Mar 3rd Stanford Invite 2024
63 Western Washington Loss 8-10 1159.57 Mar 3rd Stanford Invite 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)