#90 Southern California (4-16)

avg: 1270.22  •  sd: 72.3  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
4 Cal Poly-SLO** Loss 3-13 1578.18 Ignored Jan 25th Santa Barbara Invite 2020
32 Dartmouth Loss 6-13 1083.44 Jan 25th Santa Barbara Invite 2020
42 Utah Loss 9-13 1181.27 Jan 25th Santa Barbara Invite 2020
52 Tulane Win 13-10 1822.92 Jan 25th Santa Barbara Invite 2020
53 Case Western Reserve Loss 9-11 1230.46 Jan 26th Santa Barbara Invite 2020
64 Victoria Loss 9-13 991.12 Jan 26th Santa Barbara Invite 2020
39 California-San Diego Loss 5-11 1017.36 Feb 15th Presidents Day Invite 2020
36 California-Santa Cruz Loss 6-14 1028.84 Feb 15th Presidents Day Invite 2020
6 Oregon** Loss 5-15 1494.05 Ignored Feb 15th Presidents Day Invite 2020
28 California-Santa Barbara Loss 4-12 1116.49 Feb 16th Presidents Day Invite 2020
54 California-Davis Loss 5-11 878.06 Feb 16th Presidents Day Invite 2020
42 Utah Loss 4-12 999.84 Feb 16th Presidents Day Invite 2020
57 Illinois Win 12-9 1798.27 Feb 17th Presidents Day Invite 2020
160 San Diego State Win 15-2 1590.19 Feb 17th Presidents Day Invite 2020
15 California** Loss 4-13 1286.17 Ignored Mar 7th Stanford Invite 2020
19 Oregon State Loss 6-13 1228.12 Mar 7th Stanford Invite 2020
2 Washington** Loss 2-13 1676.45 Ignored Mar 7th Stanford Invite 2020
36 California-Santa Cruz Loss 8-12 1187.69 Mar 8th Stanford Invite 2020
59 Whitman Loss 9-12 1094.76 Mar 8th Stanford Invite 2020
42 Utah Win 9-6 2018.41 Mar 8th Stanford Invite 2020
**Blowout Eligible

FAQ

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
  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
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)