#191 Cal Poly-Pomona (4-7)

avg: 855.68  •  sd: 74.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
85 Southern California Loss 5-13 717.15 Feb 1st Pres Day Quals men
140 Santa Clara Loss 7-13 518.86 Feb 1st Pres Day Quals men
280 California-Santa Barbara-B Win 12-10 702.71 Feb 1st Pres Day Quals men
332 San Diego State-B Win 12-8 664.98 Feb 2nd Pres Day Quals men
262 California-B Win 13-6 1158.86 Feb 2nd Pres Day Quals men
137 California-Santa Cruz-B Loss 4-12 494.14 Feb 2nd Pres Day Quals men
129 Arizona State Loss 6-7 999.63 Feb 15th Vice Presidents Day Invite 2025
85 Southern California Loss 10-11 1192.15 Feb 15th Vice Presidents Day Invite 2025
106 San Diego State Loss 11-12 1105.08 Feb 15th Vice Presidents Day Invite 2025
114 California-Irvine Loss 2-13 597.3 Feb 16th Vice Presidents Day Invite 2025
238 Loyola Marymount Win 11-6 1196.2 Feb 16th Vice Presidents Day Invite 2025
**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)