#173 California-Davis (5-19)

avg: 1182.36  •  sd: 60.65  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
91 Cal Poly-CCWR Loss 1-13 893.01 Feb 1st Pres Day Quals men
279 California-B Win 13-6 1377.31 Feb 1st Pres Day Quals men
46 Stanford Loss 7-13 1207.83 Feb 1st Pres Day Quals men
65 Grand Canyon Loss 6-13 1045.05 Feb 2nd Pres Day Quals men
136 California-Irvine Loss 8-13 824.06 Feb 2nd Pres Day Quals men
211 UCLA-B Loss 5-9 496.23 Feb 2nd Pres Day Quals men
6 Cal Poly-SLO Loss 6-13 1667.97 Feb 15th Presidents Day Invite 2025
34 Utah** Loss 0-13 1240.15 Ignored Feb 15th Presidents Day Invite 2025
2 Oregon** Loss 5-13 1785.23 Ignored Feb 16th Presidents Day Invite 2025
34 Utah** Loss 1-13 1240.15 Ignored Feb 16th Presidents Day Invite 2025
16 Northeastern** Loss 0-13 1456.69 Ignored Feb 16th Presidents Day Invite 2025
31 California-Santa Barbara Loss 7-13 1304.35 Feb 17th Presidents Day Invite 2025
41 California-San Diego** Loss 3-13 1192.31 Ignored Feb 17th Presidents Day Invite 2025
200 Cal Poly-Humboldt Win 12-7 1585.33 Apr 12th NorCal D I Mens Conferences 2025
9 California-Santa Cruz** Loss 3-15 1585.14 Ignored Apr 12th NorCal D I Mens Conferences 2025
46 Stanford Loss 7-14 1182.48 Apr 12th NorCal D I Mens Conferences 2025
276 Chico State Win 12-8 1225.26 Apr 13th NorCal D I Mens Conferences 2025
124 San Jose State Loss 8-10 1096.27 Apr 13th NorCal D I Mens Conferences 2025
261 Nevada-Reno Win 11-3 1431.64 Apr 13th NorCal D I Mens Conferences 2025
119 Cal Poly-SLO-B Win 12-11 1492.2 Apr 26th Southwest D I College Mens Regionals 2025
31 California-Santa Barbara** Loss 4-15 1261.88 Ignored Apr 26th Southwest D I College Mens Regionals 2025
54 UCLA Loss 1-15 1104.08 Apr 26th Southwest D I College Mens Regionals 2025
84 Southern California Loss 10-15 1065.75 Apr 26th Southwest D I College Mens Regionals 2025
127 Santa Clara Loss 8-15 776.8 Apr 27th Southwest D I College Mens Regionals 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)