#344 California-Irvine (6-10)

avg: 506.27  •  sd: 45.77  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
265 Cal State-Long Beach Loss 6-8 500.21 Feb 2nd Presidents Day Qualifiers Men
100 California-Santa Cruz Loss 5-10 784.87 Feb 2nd Presidents Day Qualifiers Men
395 California-San Diego-C Win 13-1 859.32 Feb 2nd Presidents Day Qualifiers Men
272 Arizona State-B Loss 8-13 280.31 Feb 3rd Presidents Day Qualifiers Men
407 California-Santa Barbara-B Win 12-9 530.23 Feb 3rd Presidents Day Qualifiers Men
222 Grand Canyon Loss 5-13 319.88 Mar 9th 2019 SoCal Mixer
328 Caltech Win 10-9 689.78 Mar 9th 2019 SoCal Mixer
216 Occidental Loss 5-13 327.34 Mar 9th 2019 SoCal Mixer
353 California-San Diego-B Win 11-10 600.68 Mar 9th 2019 SoCal Mixer
191 Montana State Loss 4-13 424.96 Mar 23rd Trouble in Vegas 2019
169 Chico State Loss 4-13 484.3 Mar 23rd Trouble in Vegas 2019
202 Northern Arizona Loss 6-13 373.07 Mar 23rd Trouble in Vegas 2019
216 Occidental Loss 4-10 327.34 Mar 23rd Trouble in Vegas 2019
425 Cal State-Fullerton Win 7-1 620.63 Mar 24th Trouble in Vegas 2019
406 Colorado School of Mines - B Win 13-2 787.01 Mar 24th Trouble in Vegas 2019
353 California-San Diego-B Loss 11-12 350.68 Mar 24th Trouble in Vegas 2019
**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)