#151 Cal Poly-SLO-B (9-10)

avg: 1033.94  •  sd: 53.59  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
323 California-Santa Cruz-B** Win 13-0 839.49 Ignored Jan 20th Pres Day Quals
285 Southern California-B** Win 13-2 1027.27 Ignored Jan 20th Pres Day Quals
134 California-Irvine Win 12-8 1550.25 Jan 20th Pres Day Quals
212 San Diego State Win 13-1 1370.92 Jan 21st Pres Day Quals
160 Santa Clara Win 9-8 1118.03 Jan 21st Pres Day Quals
53 Colorado State Loss 7-15 870.56 Jan 27th Santa Barbara Invite 2024
30 Utah** Loss 3-15 1076.99 Ignored Jan 27th Santa Barbara Invite 2024
54 California-Santa Barbara Loss 6-15 869.64 Jan 27th Santa Barbara Invite 2024
15 California** Loss 4-15 1324.22 Ignored Jan 27th Santa Barbara Invite 2024
65 Stanford Loss 7-15 804.93 Jan 28th Santa Barbara Invite 2024
79 Grand Canyon Loss 10-15 887.09 Jan 28th Santa Barbara Invite 2024
349 Cal Poly-Humboldt** Win 13-4 627.1 Ignored Feb 3rd Stanford Open 2024
202 California-B Win 11-9 1075.73 Feb 3rd Stanford Open 2024
297 Occidental** Win 13-2 965.99 Ignored Feb 3rd Stanford Open 2024
19 Washington University** Loss 5-12 1265.17 Ignored Mar 2nd Stanford Invite 2024
65 Stanford Loss 4-8 840.13 Mar 2nd Stanford Invite 2024
63 Western Washington Loss 6-13 822.23 Mar 2nd Stanford Invite 2024
160 Santa Clara Win 13-10 1321.18 Mar 3rd Stanford Invite 2024
115 Southern California Loss 10-12 946.67 Mar 3rd Stanford Invite 2024
**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)