#214 California-Santa Cruz (9-9)

avg: 905.57  •  sd: 76.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
235 Arizona State-B Win 11-7 1269.55 Feb 3rd Presidents Day Qualifier 2018
276 San Jose State Win 11-3 1298.83 Feb 3rd Presidents Day Qualifier 2018
394 California-San Diego-C Win 10-6 618.38 Feb 3rd Presidents Day Qualifier 2018
397 California-Santa Barbara-B** Win 13-1 707.16 Ignored Feb 3rd Presidents Day Qualifier 2018
87 Las Positas Loss 8-10 1130.54 Feb 4th Presidents Day Qualifier 2018
235 Arizona State-B Win 13-6 1402.66 Feb 4th Presidents Day Qualifier 2018
100 Arizona Loss 4-8 770.67 Feb 10th Stanford Open 2018
32 California** Loss 5-13 1095.8 Ignored Feb 10th Stanford Open 2018
320 Caltech Win 13-8 1001.01 Feb 10th Stanford Open 2018
146 Nevada-Reno Loss 8-12 708.15 Feb 11th Stanford Open 2018
208 Occidental Win 13-5 1520.5 Feb 11th Stanford Open 2018
263 Sacramento State Win 13-7 1299.52 Feb 11th Stanford Open 2018
100 Arizona Loss 4-11 735.48 Mar 24th Trouble in Vegas 2018
186 Cal Poly-Pomona Loss 4-12 383.1 Mar 24th Trouble in Vegas 2018
67 Utah Loss 6-12 878.66 Mar 24th Trouble in Vegas 2018
128 Colorado School of Mines Loss 7-11 736.97 Mar 24th Trouble in Vegas 2018
272 Miami Win 10-9 826.69 Mar 25th Trouble in Vegas 2018
225 California-B Loss 6-9 435.04 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)