#63 Western Washington (9-11)

avg: 1422.23  •  sd: 85.65  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
349 Cal Poly-Humboldt** Win 15-5 627.1 Ignored Jan 27th Trouble in Corvegas
18 Oregon State Loss 5-15 1269.3 Jan 27th Trouble in Corvegas
339 Portland State** Win 15-5 705.28 Ignored Jan 27th Trouble in Corvegas
168 Washington State Win 15-9 1469.46 Jan 27th Trouble in Corvegas
18 Oregon State Loss 4-15 1269.3 Jan 28th Trouble in Corvegas
356 Oregon State-B** Win 15-1 591.5 Ignored Jan 28th Trouble in Corvegas
168 Washington State Win 15-12 1254.47 Jan 28th Trouble in Corvegas
151 Cal Poly-SLO-B Win 13-6 1633.94 Mar 2nd Stanford Invite 2024
65 Stanford Win 12-9 1750.3 Mar 2nd Stanford Invite 2024
117 Vanderbilt Loss 8-9 1054.97 Mar 2nd Stanford Invite 2024
19 Washington University Loss 7-13 1307.64 Mar 2nd Stanford Invite 2024
43 California-San Diego Win 10-8 1824.93 Mar 3rd Stanford Invite 2024
54 California-Santa Barbara Win 10-6 1965.8 Mar 3rd Stanford Invite 2024
17 Brigham Young Loss 10-15 1421.84 Mar 23rd Northwest Challenge Mens 2024
24 British Columbia Loss 10-15 1346.92 Mar 23rd Northwest Challenge Mens 2024
15 California Loss 9-15 1408.74 Mar 23rd Northwest Challenge Mens 2024
6 Oregon Loss 8-15 1544.45 Mar 23rd Northwest Challenge Mens 2024
35 California-Santa Cruz Loss 6-15 1037.21 Mar 24th Northwest Challenge Mens 2024
39 Victoria Loss 10-15 1132.13 Mar 24th Northwest Challenge Mens 2024
22 Washington Loss 12-15 1518.5 Mar 24th Northwest Challenge Mens 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)