#39 Cal Poly-SLO (10-8)

avg: 1498.43  •  sd: 112.57  •  top 16/20: 0.4%

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# Opponent Result Game Rating Status Date Event
12 Brigham Young Loss 12-13 1797.14 Jan 26th Santa Barbara Invite 2024
1 Carleton College** Loss 4-15 1859.36 Ignored Jan 27th Santa Barbara Invite 2024
8 Washington Loss 9-13 1713.94 Jan 27th Santa Barbara Invite 2024
27 California-Davis Loss 7-10 1271.84 Jan 27th Santa Barbara Invite 2024
80 Northwestern Win 13-5 1552.95 Jan 28th Santa Barbara Invite 2024
26 California-San Diego Win 11-7 2135.5 Jan 28th Santa Barbara Invite 2024
62 Southern California Win 12-8 1655.27 Feb 3rd Stanford Open 2024
- Cal Poly-Humboldt** Win 13-0 1.37 Ignored Feb 3rd Stanford Open 2024
45 Portland Win 9-8 1502.3 Feb 3rd Stanford Open 2024
127 California-B** Win 13-1 893.17 Ignored Feb 3rd Stanford Open 2024
62 Southern California Win 11-6 1760.81 Feb 17th Presidents Day Invite 2024
28 Colorado State Loss 5-14 1034.36 Feb 17th Presidents Day Invite 2024
11 California-Santa Barbara Loss 8-14 1440.75 Feb 17th Presidents Day Invite 2024
88 Claremont Win 12-6 1455 Feb 18th Presidents Day Invite 2024
48 California Loss 7-9 1063.7 Feb 18th Presidents Day Invite 2024
108 Denver** Win 15-4 1248.32 Ignored Feb 18th Presidents Day Invite 2024
27 California-Davis Loss 2-9 1061.5 Feb 19th Presidents Day Invite 2024
48 California Win 9-7 1622.37 Feb 19th Presidents Day 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)