#79 California-Davis (14-6)

avg: 1414.63  •  sd: 58.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
146 Nevada-Reno Win 13-10 1477.44 Jan 27th Santa Barbara Invitational 2018
18 Brigham Young Loss 8-13 1357.23 Jan 27th Santa Barbara Invitational 2018
143 California-San Diego Win 13-11 1389.76 Jan 27th Santa Barbara Invitational 2018
24 Western Washington Win 13-10 2070.21 Jan 27th Santa Barbara Invitational 2018
58 Kansas Loss 8-13 1004.7 Jan 28th Santa Barbara Invitational 2018
25 Victoria Loss 14-15 1606.74 Jan 28th Santa Barbara Invitational 2018
143 California-San Diego Win 13-7 1718.45 Jan 28th Santa Barbara Invitational 2018
208 Occidental Win 9-7 1199.84 Feb 10th Stanford Open 2018
225 California-B Win 13-3 1453.6 Feb 10th Stanford Open 2018
343 Texas-B** Win 13-4 994.96 Ignored Feb 10th Stanford Open 2018
57 Whitman Win 10-9 1631.56 Feb 10th Stanford Open 2018
158 Lewis & Clark Win 11-9 1351.34 Feb 11th Stanford Open 2018
35 Air Force Loss 9-11 1390.37 Feb 11th Stanford Open 2018
85 Colorado College Loss 11-12 1274.36 Feb 11th Stanford Open 2018
146 Nevada-Reno Win 13-11 1378.14 Mar 10th Silicon Valley Rally 2018
165 Humboldt State Win 13-8 1562.84 Mar 10th Silicon Valley Rally 2018
276 San Jose State Win 13-7 1256.36 Mar 10th Silicon Valley Rally 2018
225 California-B Win 13-5 1453.6 Mar 10th Silicon Valley Rally 2018
87 Las Positas Loss 8-13 897.04 Mar 11th Silicon Valley Rally 2018
195 Sonoma State Win 13-4 1563.31 Mar 11th Silicon Valley Rally 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)