#13 California (28-14)

avg: 2103.63  •  sd: 47.49  •  top 16/20: 98.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
46 Stanford Win 12-7 2285.88 Jan 25th Santa Barbara Invite 2025
34 Utah Win 13-9 2258.72 Jan 25th Santa Barbara Invite 2025
10 British Columbia Win 12-4 2751.93 Jan 26th Santa Barbara Invite 2025
6 Cal Poly-SLO Loss 4-7 1771.81 Jan 26th Santa Barbara Invite 2025
58 Illinois Win 13-6 2272.94 Jan 26th Santa Barbara Invite 2025
9 California-Santa Cruz Loss 10-11 2060.14 Feb 15th Presidents Day Invite 2025
42 Colorado State Win 12-8 2225.91 Feb 15th Presidents Day Invite 2025
16 Northeastern Win 9-8 2181.69 Feb 15th Presidents Day Invite 2025
25 Victoria Win 13-11 2187.69 Feb 16th Presidents Day Invite 2025
42 Colorado State Win 13-6 2384.76 Feb 16th Presidents Day Invite 2025
5 Colorado Win 13-10 2687.05 Feb 16th Presidents Day Invite 2025
11 Oregon State Loss 10-12 1897.61 Feb 16th Presidents Day Invite 2025
8 Washington Loss 9-11 1967.38 Feb 17th Presidents Day Invite 2025
9 California-Santa Cruz Loss 7-11 1718.25 Feb 17th Presidents Day Invite 2025
143 Wisconsin-Milwaukee** Win 13-5 1888.58 Ignored Mar 8th Stanford Invite 2025 Mens
31 California-Santa Barbara Win 13-8 2358.04 Mar 8th Stanford Invite 2025 Mens
23 Western Washington Loss 10-11 1863.28 Mar 8th Stanford Invite 2025 Mens
127 Santa Clara Win 13-6 1941.61 Mar 8th Stanford Invite 2025 Mens
6 Cal Poly-SLO Loss 8-13 1771.81 Mar 9th Stanford Invite 2025 Mens
45 Virginia Tech Win 10-9 1900.6 Mar 9th Stanford Invite 2025 Mens
41 California-San Diego Win 12-8 2233.47 Mar 9th Stanford Invite 2025 Mens
22 Penn State Win 13-6 2592.96 Mar 29th Easterns 2025
16 Northeastern Win 13-8 2552.85 Mar 29th Easterns 2025
1 Massachusetts Loss 8-13 1920.52 Mar 29th Easterns 2025
62 North Carolina State Win 13-7 2208.86 Mar 29th Easterns 2025
6 Cal Poly-SLO Loss 11-15 1886.81 Mar 30th Easterns 2025
4 North Carolina Loss 12-15 2059.34 Mar 30th Easterns 2025
19 Tufts Loss 9-13 1611.96 Mar 30th Easterns 2025
276 Chico State** Win 15-3 1384.1 Ignored Apr 12th NorCal D I Mens Conferences 2025
127 Santa Clara** Win 15-4 1941.61 Ignored Apr 12th NorCal D I Mens Conferences 2025
261 Nevada-Reno** Win 15-3 1431.64 Ignored Apr 12th NorCal D I Mens Conferences 2025
124 San Jose State** Win 13-3 1958.94 Ignored Apr 12th NorCal D I Mens Conferences 2025
46 Stanford Win 14-12 1986.32 Apr 13th NorCal D I Mens Conferences 2025
9 California-Santa Cruz Loss 9-15 1669.66 Apr 13th NorCal D I Mens Conferences 2025
46 Stanford Win 15-10 2218.97 Apr 13th NorCal D I Mens Conferences 2025
6 Cal Poly-SLO Loss 13-15 2053.79 Apr 26th Southwest D I College Mens Regionals 2025
211 UCLA-B** Win 15-4 1625.29 Ignored Apr 26th Southwest D I College Mens Regionals 2025
46 Stanford Win 15-7 2365.36 Apr 26th Southwest D I College Mens Regionals 2025
41 California-San Diego Win 15-11 2173.48 Apr 26th Southwest D I College Mens Regionals 2025
9 California-Santa Cruz Loss 12-14 1964.18 Apr 27th Southwest D I College Mens Regionals 2025
54 UCLA Win 14-11 2017.42 Apr 27th Southwest D I College Mens Regionals 2025
31 California-Santa Barbara Win 13-11 2090.72 Apr 27th Southwest D I College Mens 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)