#43 Southern California (10-15)

avg: 1990.28  •  sd: 59.65  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
2 California-San Diego** Loss 4-13 2132.82 Ignored Jan 27th Santa Barbara Invitational 2018
35 Cal Poly-SLO Loss 10-12 1798.43 Jan 27th Santa Barbara Invitational 2018
28 Washington University Loss 5-13 1513.89 Jan 27th Santa Barbara Invitational 2018
87 California-Santa Cruz Win 14-5 2187.75 Jan 28th Santa Barbara Invitational 2018
61 California-Davis Win 12-9 2163.28 Jan 28th Santa Barbara Invitational 2018
26 California Loss 9-10 2008.23 Feb 17th Presidents Day Invitational Tournament 2018
20 Washington Loss 5-14 1640.23 Feb 17th Presidents Day Invitational Tournament 2018
9 Colorado Loss 6-10 2003.53 Feb 17th Presidents Day Invitational Tournament 2018
79 Chicago Win 7-6 1778.38 Feb 18th Presidents Day Invitational Tournament 2018
61 California-Davis Win 11-9 2067.12 Feb 18th Presidents Day Invitational Tournament 2018
20 Washington Win 9-6 2658.8 Feb 18th Presidents Day Invitational Tournament 2018
55 Iowa State Win 8-6 2146.95 Feb 19th Presidents Day Invitational Tournament 2018
22 Minnesota Loss 3-8 1628.83 Feb 19th Presidents Day Invitational Tournament 2018
2 California-San Diego Loss 6-12 2153.51 Mar 3rd Stanford Invite 2018
12 Carleton College Loss 3-13 1821.97 Mar 3rd Stanford Invite 2018
20 Washington Loss 7-11 1773.34 Mar 3rd Stanford Invite 2018
14 Whitman Win 8-7 2511.14 Mar 4th Stanford Invite 2018
26 California Win 10-8 2395.9 Mar 4th Stanford Invite 2018
61 California-Davis Win 9-5 2346.98 Mar 4th Stanford Invite 2018
69 Boston College Win 10-5 2324.25 Mar 24th NW Challenge 2018
26 California Loss 8-13 1637.07 Mar 24th NW Challenge 2018
4 Stanford** Loss 6-15 2095.52 Ignored Mar 24th NW Challenge 2018
3 North Carolina** Loss 5-15 2130.5 Ignored Mar 24th NW Challenge 2018
38 Victoria Loss 10-13 1692.06 Mar 25th NW Challenge 2018
21 Michigan Loss 7-12 1717.92 Mar 25th NW Challenge 2018
**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)