#160 Santa Clara (4-7)

avg: 993.03  •  sd: 94.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
330 California-San Diego-B** Win 13-5 771.51 Ignored Jan 20th Pres Day Quals
164 UCLA-B Loss 9-12 626.22 Jan 20th Pres Day Quals
151 Cal Poly-SLO-B Loss 8-9 908.94 Jan 21st Pres Day Quals
194 California-Davis Win 9-4 1448.06 Feb 3rd Stanford Open 2024
307 Chico State** Win 13-3 917.96 Ignored Feb 3rd Stanford Open 2024
133 Loyola Marymount Win 9-4 1710.24 Feb 3rd Stanford Open 2024
54 California-Santa Barbara Loss 6-13 869.64 Mar 2nd Stanford Invite 2024
35 California-Santa Cruz Loss 7-11 1170.31 Mar 2nd Stanford Invite 2024
117 Vanderbilt Loss 9-11 930.76 Mar 2nd Stanford Invite 2024
151 Cal Poly-SLO-B Loss 10-13 705.79 Mar 3rd Stanford Invite 2024
115 Southern California Loss 9-12 839.42 Mar 3rd Stanford Invite 2024
**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)