#60 Oregon (6-13)

avg: 1346.76  •  sd: 55.4  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
133 Oregon State Win 15-6 1439.4 Jan 25th Pacific Confrontational Invite 2020
176 Portland State** Win 15-4 1089.22 Ignored Jan 25th Pacific Confrontational Invite 2020
44 Whitman Win 13-12 1651.67 Jan 25th Pacific Confrontational Invite 2020
44 Whitman Loss 5-11 926.67 Jan 26th Pacific Confrontational Invite 2020
133 Oregon State Win 11-5 1439.4 Jan 26th Pacific Confrontational Invite 2020
35 Utah Loss 5-13 974.56 Feb 15th Presidents Day Invite 2020
61 Massachusetts Loss 9-10 1205.06 Feb 15th Presidents Day Invite 2020
39 Cal Poly-SLO Loss 7-8 1431.98 Feb 15th Presidents Day Invite 2020
10 California-Santa Barbara** Loss 4-14 1441.89 Ignored Feb 15th Presidents Day Invite 2020
45 Chicago Loss 5-10 943.63 Feb 16th Presidents Day Invite 2020
19 Colorado Loss 3-12 1336.04 Feb 16th Presidents Day Invite 2020
90 Southern California Win 9-6 1543.24 Feb 17th Presidents Day Invite 2020
40 Colorado College Loss 9-10 1428.68 Feb 17th Presidents Day Invite 2020
3 Tufts** Loss 4-13 1668.54 Ignored Mar 7th Stanford Invite 2020
17 British Columbia** Loss 3-13 1354.58 Ignored Mar 7th Stanford Invite 2020
29 California Win 9-8 1805.52 Mar 7th Stanford Invite 2020
10 California-Santa Barbara Loss 6-11 1495.19 Mar 7th Stanford Invite 2020
13 Pittsburgh** Loss 5-13 1394.81 Ignored Mar 8th Stanford Invite 2020
26 Texas Loss 5-12 1163.42 Mar 8th Stanford Invite 2020
**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)