#56 California-San Diego (10-8)

avg: 1592.76  •  sd: 62.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
50 Stanford Loss 9-12 1287.38 Jan 26th Santa Barbara Invite 2019
45 California-Santa Barbara Loss 8-13 1167.09 Jan 26th Santa Barbara Invite 2019
30 Victoria Loss 8-13 1269.74 Jan 26th Santa Barbara Invite 2019
90 Santa Clara Win 14-13 1511.86 Jan 27th Santa Barbara Invite 2019
40 Dartmouth Loss 10-13 1358.33 Jan 27th Santa Barbara Invite 2019
5 Cal Poly-SLO Loss 9-10 2019.46 Feb 16th Presidents Day Invite 2019
76 Utah Win 8-7 1598.73 Feb 16th Presidents Day Invite 2019
45 California-Santa Barbara Win 8-5 2116.86 Feb 17th Presidents Day Invite 2019
3 Oregon Loss 4-12 1588.99 Feb 17th Presidents Day Invite 2019
42 British Columbia Win 11-10 1798.61 Feb 18th Presidents Day Invite 2019
8 Colorado Loss 4-11 1495.44 Feb 18th Presidents Day Invite 2019
94 Appalachian State Win 13-9 1791 Mar 23rd College Southerns XVIII
321 Carleton Hot Karls** Win 13-5 1189.49 Ignored Mar 23rd College Southerns XVIII
165 Georgia Southern Win 13-7 1649.44 Mar 23rd College Southerns XVIII
173 Georgia College Win 13-6 1669.11 Mar 23rd College Southerns XVIII
94 Appalachian State Win 14-13 1497.43 Mar 24th College Southerns XVIII
69 Emory Win 12-10 1746.58 Mar 24th College Southerns XVIII
78 Carleton College-GoP Loss 12-13 1332.72 Mar 24th College Southerns XVIII
**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)