#262 California-B (7-17)

avg: 558.86  •  sd: 41.51  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
92 Cal Poly-CCWR** Loss 2-13 684.01 Feb 1st Pres Day Quals men
42 Stanford** Loss 4-13 1017.36 Ignored Feb 1st Pres Day Quals men
173 California-Davis Loss 6-13 345.35 Feb 1st Pres Day Quals men
92 Cal Poly-CCWR** Loss 3-13 684.01 Feb 2nd Pres Day Quals men
191 Cal Poly-Pomona Loss 6-13 255.68 Feb 2nd Pres Day Quals men
339 California-San Diego-B Win 12-9 528.86 Feb 2nd Pres Day Quals men
175 Cal Poly-Humboldt Loss 1-13 336.36 Feb 8th Stanford Open Mens
111 San Jose State Loss 7-11 739.82 Feb 8th Stanford Open Mens
238 Loyola Marymount Win 12-10 887.62 Feb 9th Stanford Open Mens
266 Chico State Win 10-9 672.51 Feb 9th Stanford Open Mens
162 Washington-B Loss 8-13 486.11 Feb 9th Stanford Open Mens
167 Brigham Young-B Loss 3-13 368.26 Mar 1st Snow Melt 2025
76 Colorado College** Loss 5-13 769.71 Ignored Mar 1st Snow Melt 2025
359 Denver-B Win 12-5 612.93 Mar 1st Snow Melt 2025
261 Colorado State-B Loss 8-9 439.61 Mar 1st Snow Melt 2025
34 Lewis & Clark** Loss 2-15 1059.3 Ignored Mar 2nd Snow Melt 2025
124 Denver Loss 6-15 546.36 Mar 2nd Snow Melt 2025
368 Colorado Mines-B Win 14-7 511.92 Mar 2nd Snow Melt 2025
175 Cal Poly-Humboldt Loss 9-13 517.79 Mar 15th Silicon Valley Rally 2025
111 San Jose State Loss 6-10 710.55 Mar 15th Silicon Valley Rally 2025
137 California-Santa Cruz-B Loss 5-10 520.24 Mar 15th Silicon Valley Rally 2025
266 Chico State Win 9-8 672.51 Mar 15th Silicon Valley Rally 2025
137 California-Santa Cruz-B Loss 5-10 520.24 Mar 16th Silicon Valley Rally 2025
266 Chico State Win 9-8 672.51 Mar 16th Silicon Valley Rally 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)