#225 California-B (8-9)

avg: 853.6  •  sd: 50.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
208 Occidental Loss 4-13 320.5 Feb 10th Stanford Open 2018
79 California-Davis Loss 3-13 814.63 Feb 10th Stanford Open 2018
57 Whitman Loss 7-11 1039.66 Feb 10th Stanford Open 2018
343 Texas-B Win 13-8 891.12 Feb 10th Stanford Open 2018
397 California-Santa Barbara-B** Win 13-5 707.16 Ignored Feb 11th Stanford Open 2018
146 Nevada-Reno Loss 11-12 1024.3 Mar 10th Silicon Valley Rally 2018
165 Humboldt State Loss 11-13 837.84 Mar 10th Silicon Valley Rally 2018
276 San Jose State Win 11-8 1064.44 Mar 10th Silicon Valley Rally 2018
79 California-Davis Loss 5-13 814.63 Mar 10th Silicon Valley Rally 2018
360 Fresno State Win 13-9 761.12 Mar 11th Silicon Valley Rally 2018
366 Arizona-B Win 13-5 916.02 Mar 24th Trouble in Vegas 2018
316 Cal Poly-SLO-B Win 7-6 648.91 Mar 24th Trouble in Vegas 2018
159 Colorado-B Loss 7-10 704.9 Mar 24th Trouble in Vegas 2018
129 Claremont Loss 6-9 777.3 Mar 24th Trouble in Vegas 2018
263 Sacramento State Win 9-8 866.99 Mar 25th Trouble in Vegas 2018
131 Chico State Loss 6-9 770.08 Mar 25th Trouble in Vegas 2018
214 California-Santa Cruz Win 9-6 1324.14 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)