#50 Stanford (8-16)

avg: 1632.74  •  sd: 56.34  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
56 California-San Diego Win 12-9 1938.13 Jan 26th Santa Barbara Invite 2019
30 Victoria Loss 8-13 1269.74 Jan 26th Santa Barbara Invite 2019
45 California-Santa Barbara Loss 11-13 1434.41 Jan 26th Santa Barbara Invite 2019
16 Southern California Loss 8-13 1479.99 Jan 27th Santa Barbara Invite 2019
29 Texas-Dallas Win 13-11 2000.75 Jan 27th Santa Barbara Invite 2019
10 Washington Loss 11-12 1919.51 Jan 27th Santa Barbara Invite 2019
111 Washington University Win 10-9 1438.46 Feb 9th Stanford Open 2019
254 Cal Poly-Pomona Win 13-11 1069.03 Feb 9th Stanford Open 2019
125 Colorado School of Mines Win 11-10 1403.32 Feb 9th Stanford Open 2019
62 Duke Win 8-6 1851.5 Feb 10th Stanford Open 2019
116 Nevada-Reno Loss 5-6 1168.72 Feb 10th Stanford Open 2019
6 Brigham Young Loss 10-13 1806.59 Mar 1st Stanford Invite 2019
1 North Carolina Loss 2-13 1631.92 Mar 2nd Stanford Invite 2019
30 Victoria Loss 11-13 1537.06 Mar 2nd Stanford Invite 2019
13 Wisconsin Loss 10-11 1875.97 Mar 2nd Stanford Invite 2019
49 Northwestern Loss 11-12 1512.69 Mar 3rd Stanford Invite 2019
10 Washington Loss 6-9 1625.94 Mar 3rd Stanford Invite 2019
17 Minnesota Loss 6-13 1351.05 Mar 3rd Stanford Invite 2019
59 Oregon State Loss 12-14 1341.23 Mar 23rd 2019 NW Challenge Mens Tier 1
6 Brigham Young Loss 8-13 1638.57 Mar 23rd 2019 NW Challenge Mens Tier 1
42 British Columbia Win 13-7 2231.14 Mar 23rd 2019 NW Challenge Mens Tier 1
5 Cal Poly-SLO Loss 10-12 1906.34 Mar 24th 2019 NW Challenge Mens Tier 1
51 Western Washington Loss 12-13 1504.76 Mar 24th 2019 NW Challenge Mens Tier 1
58 Whitman Win 13-10 1907.79 Mar 25th 2019 NW Challenge Mens Tier 1
**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)