#21 Cal Poly-SLO (17-8)

avg: 1943.59  •  sd: 89  •  top 16/20: 51.9%

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# Opponent Result Game Rating Status Date Event
42 Chicago Loss 7-13 1006.96 Jan 26th Santa Barbara Invite 2019
19 UCLA Win 14-12 2186.81 Jan 26th Santa Barbara Invite 2019
13 Stanford Win 13-9 2474.2 Jan 26th Santa Barbara Invite 2019
15 Wisconsin Loss 8-13 1525.8 Jan 27th Santa Barbara Invite 2019
23 California Win 10-7 2307.58 Jan 27th Santa Barbara Invite 2019
19 UCLA Win 15-14 2090.86 Jan 27th Santa Barbara Invite 2019
158 Claremont** Win 13-1 1426.85 Ignored Feb 9th Stanford Open 2019
234 Nevada-Reno** Win 13-1 871.07 Ignored Feb 9th Stanford Open 2019
68 Lewis & Clark Win 11-10 1455.23 Feb 9th Stanford Open 2019
54 Puget Sound Win 8-7 1617.13 Feb 10th Stanford Open 2019
55 Portland Win 6-4 1853.58 Feb 10th Stanford Open 2019
30 Utah Win 9-7 2038.2 Feb 16th Presidents Day Invite 2019
13 Stanford Win 8-5 2509.24 Feb 16th Presidents Day Invite 2019
14 Colorado Win 9-6 2465.42 Feb 17th Presidents Day Invite 2019
34 Colorado College Win 11-4 2303.78 Feb 17th Presidents Day Invite 2019
13 Stanford Win 7-6 2180.63 Feb 17th Presidents Day Invite 2019
2 California-San Diego Loss 7-12 1898.55 Feb 18th Presidents Day Invite 2019
17 Vermont Win 9-8 2138.2 Feb 18th Presidents Day Invite 2019
9 Texas Win 10-9 2269 Mar 2nd Stanford Invite 2019
6 British Columbia Loss 8-10 1969.1 Mar 2nd Stanford Invite 2019
23 California Loss 4-8 1353.11 Mar 2nd Stanford Invite 2019
38 Florida Win 12-6 2190.42 Mar 2nd Stanford Invite 2019
4 California-Santa Barbara Loss 8-13 1784.33 Mar 3rd Stanford Invite 2019
13 Stanford Loss 6-11 1508.94 Mar 3rd Stanford Invite 2019
23 California Loss 9-11 1668.71 Mar 3rd Stanford Invite 2019
**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)