#332 California-San Diego-B (7-11)

avg: 442.18  •  sd: 50.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Santa Clara** Loss 1-13 899.77 Ignored Feb 3rd Presidents Day Qualifier 2018
195 Sonoma State Loss 8-13 467.15 Feb 3rd Presidents Day Qualifier 2018
316 Cal Poly-SLO-B Loss 8-10 261.24 Feb 3rd Presidents Day Qualifier 2018
397 California-Santa Barbara-B Win 13-9 525.73 Feb 4th Presidents Day Qualifier 2018
394 California-San Diego-C Win 12-11 247.22 Feb 4th Presidents Day Qualifier 2018
407 California-Santa Cruz-B Win 13-6 535.22 Feb 4th Presidents Day Qualifier 2018
382 UCLA-B Win 9-6 633.75 Feb 4th Presidents Day Qualifier 2018
208 Occidental Loss 4-11 320.5 Feb 24th SoCal Mixer 2018
320 Caltech Win 10-8 767.52 Feb 24th SoCal Mixer 2018
394 California-San Diego-C Win 11-4 722.22 Feb 24th SoCal Mixer 2018
382 UCLA-B Win 9-4 815.18 Feb 24th SoCal Mixer 2018
129 Claremont** Loss 4-11 595.86 Ignored Feb 24th SoCal Mixer 2018
235 Arizona State-B Loss 5-10 228.76 Mar 24th Trouble in Vegas 2018
333 California-Davis-B Loss 7-8 305.48 Mar 24th Trouble in Vegas 2018
176 Colorado State-B Loss 5-10 452.72 Mar 24th Trouble in Vegas 2018
316 Cal Poly-SLO-B Loss 9-10 398.91 Mar 25th Trouble in Vegas 2018
329 California-Irvine Loss 7-8 339.87 Mar 25th Trouble in Vegas 2018
355 Colorado Mesa University Loss 6-7 229.28 Mar 25th Trouble in Vegas 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)