#113 Southern California (7-13)

avg: 1454.03  •  sd: 53.63  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
33 California-Santa Cruz Loss 7-15 1307.18 Jan 27th Santa Barbara Invite 2024
7 Oregon Loss 7-15 1730.08 Jan 27th Santa Barbara Invite 2024
67 Stanford Win 12-9 2016.93 Jan 27th Santa Barbara Invite 2024
40 Victoria Loss 7-13 1299.95 Jan 27th Santa Barbara Invite 2024
61 Chicago Loss 10-13 1366.19 Jan 28th Santa Barbara Invite 2024
106 Northwestern Loss 8-13 986.75 Jan 28th Santa Barbara Invite 2024
71 Grand Canyon Loss 9-11 1380.36 Mar 2nd Stanford Invite 2024
7 Oregon** Loss 2-13 1730.08 Ignored Mar 2nd Stanford Invite 2024
43 Tulane Loss 4-12 1238.42 Mar 2nd Stanford Invite 2024
121 Cal Poly-SLO-B Win 12-10 1639.37 Mar 3rd Stanford Invite 2024
144 Santa Clara Win 12-9 1681.56 Mar 3rd Stanford Invite 2024
255 Cal State-Long Beach Win 13-5 1522.79 Apr 13th SoCal D I Mens Conferences 2024
60 California-Santa Barbara Loss 7-10 1325.93 Apr 13th SoCal D I Mens Conferences 2024
211 San Diego State Win 12-6 1659.45 Apr 13th SoCal D I Mens Conferences 2024
24 UCLA Loss 7-13 1463.17 Apr 13th SoCal D I Mens Conferences 2024
125 California-Irvine Loss 8-10 1129.25 Apr 14th SoCal D I Mens Conferences 2024
192 Loyola Marymount Win 13-7 1713.43 Apr 14th SoCal D I Mens Conferences 2024
121 Cal Poly-SLO-B Loss 9-10 1276.25 Apr 27th Southwest D I College Mens Regionals 2024
24 UCLA Loss 9-15 1505.22 Apr 27th Southwest D I College Mens Regionals 2024
158 UCLA-B Win 9-8 1414.07 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)