#41 California-San Diego (8-16)

avg: 1621.47  •  sd: 49.44  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
29 Utah Valley Loss 11-13 1523.41 Jan 25th Santa Barbara Invite 2025
33 California-Santa Barbara Win 12-11 1785.93 Jan 25th Santa Barbara Invite 2025
9 California-Santa Cruz Loss 11-13 1792.34 Jan 25th Santa Barbara Invite 2025
55 UCLA Loss 5-8 1081.63 Jan 26th Santa Barbara Invite 2025
18 Northeastern Loss 11-13 1666.86 Feb 15th Presidents Day Invite 2025
33 California-Santa Barbara Loss 5-7 1332.79 Feb 15th Presidents Day Invite 2025
6 Cal Poly-SLO Loss 10-13 1799.47 Feb 16th Presidents Day Invite 2025
10 Oregon State Loss 10-11 1856.68 Feb 16th Presidents Day Invite 2025
42 Stanford Loss 10-11 1492.36 Feb 16th Presidents Day Invite 2025
173 California-Davis** Win 13-3 1545.35 Ignored Feb 17th Presidents Day Invite 2025
23 Victoria Loss 10-11 1723.42 Feb 17th Presidents Day Invite 2025
118 British Columbia -B Win 13-9 1604.58 Mar 8th Stanford Invite 2025 Mens
6 Cal Poly-SLO Loss 9-13 1709.05 Mar 8th Stanford Invite 2025 Mens
53 Whitman Loss 9-13 1129.72 Mar 8th Stanford Invite 2025 Mens
85 Southern California Win 12-9 1662.52 Mar 8th Stanford Invite 2025 Mens
14 California Loss 8-12 1528.07 Mar 9th Stanford Invite 2025 Mens
22 Western Washington Loss 9-13 1431.4 Mar 9th Stanford Invite 2025 Mens
42 Stanford Win 11-10 1742.36 Mar 9th Stanford Invite 2025 Mens
26 Utah Win 15-13 2009.76 Mar 22nd Northwest Challenge 2025 mens
7 Washington Loss 7-15 1516.13 Mar 22nd Northwest Challenge 2025 mens
32 Virginia Win 13-11 1908.71 Mar 22nd Northwest Challenge 2025 mens
22 Western Washington Loss 11-15 1468.8 Mar 22nd Northwest Challenge 2025 mens
40 Wisconsin Win 15-11 2009.17 Mar 23rd Northwest Challenge 2025 mens
26 Utah Loss 9-15 1280.1 Mar 23rd Northwest Challenge 2025 mens
**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)