#216 Cal Poly-Pomona (4-12)

avg: 1015.56  •  sd: 66.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
84 Southern California Loss 5-13 919.35 Feb 1st Pres Day Quals men
127 Santa Clara Loss 7-13 784.08 Feb 1st Pres Day Quals men
299 California-Santa Barbara-B Win 12-10 925.88 Feb 1st Pres Day Quals men
376 San Diego State-B Win 12-8 707.66 Feb 2nd Pres Day Quals men
279 California-B Win 13-6 1377.31 Feb 2nd Pres Day Quals men
175 California-Santa Cruz-B Loss 4-12 580.4 Feb 2nd Pres Day Quals men
120 Arizona State Loss 6-7 1240.92 Feb 15th Vice Presidents Day Invite 2025
84 Southern California Loss 10-11 1394.35 Feb 15th Vice Presidents Day Invite 2025
109 San Diego State Loss 11-12 1303.77 Feb 15th Vice Presidents Day Invite 2025
136 California-Irvine Loss 2-13 720.22 Feb 16th Vice Presidents Day Invite 2025
237 Loyola Marymount Win 11-6 1472.07 Feb 16th Vice Presidents Day Invite 2025
237 Loyola Marymount Loss 9-11 676.17 Apr 12th SoCal D I Mens Conferences 2025
31 California-Santa Barbara Loss 6-13 1261.88 Apr 12th SoCal D I Mens Conferences 2025
84 Southern California Loss 2-13 919.35 Apr 12th SoCal D I Mens Conferences 2025
54 UCLA** Loss 4-13 1104.08 Ignored Apr 12th SoCal D I Mens Conferences 2025
136 California-Irvine Loss 12-15 1019.73 Apr 13th SoCal D I Mens Conferences 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)