#163 Washington-B (11-11)

avg: 1211.9  •  sd: 60.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
279 Chico State Win 12-3 1366.49 Feb 8th Stanford Open Mens
109 San Diego State Loss 7-8 1285.79 Feb 8th Stanford Open Mens
333 Cal State-Long Beach** Win 13-5 1165.67 Ignored Feb 9th Stanford Open Mens
281 California-B Win 13-8 1257.98 Feb 9th Stanford Open Mens
342 Stanford-B** Win 13-3 1112.29 Ignored Feb 9th Stanford Open Mens
141 Puget Sound Loss 11-15 906.89 Mar 29th Northwest Challenge D3
309 Whitworth Win 13-8 1132.81 Mar 29th Northwest Challenge D3
108 British Columbia -B Loss 7-15 819.01 Mar 30th Northwest Challenge D3
251 Portland Win 12-10 1102.19 Mar 30th Northwest Challenge D3
1 Oregon** Loss 3-13 1774.43 Ignored Apr 19th Cascadia D I Mens Conferences 2025
225 Oregon-B Win 13-8 1451.6 Apr 19th Cascadia D I Mens Conferences 2025
381 Portland State** Win 13-1 839.95 Ignored Apr 19th Cascadia D I Mens Conferences 2025
24 Victoria Loss 6-13 1350.02 Apr 19th Cascadia D I Mens Conferences 2025
108 British Columbia -B Loss 10-14 1020.31 Apr 20th Cascadia D I Mens Conferences 2025
225 Oregon-B Win 15-9 1470.92 Apr 20th Cascadia D I Mens Conferences 2025
247 Oregon State-B Win 15-8 1442.41 Apr 20th Cascadia D I Mens Conferences 2025
16 British Columbia** Loss 4-15 1430.23 Ignored May 3rd Northwest D I College Mens Regionals 2025
108 British Columbia -B Win 13-10 1747.15 May 3rd Northwest D I College Mens Regionals 2025
23 Western Washington Loss 7-15 1370.42 May 3rd Northwest D I College Mens Regionals 2025
108 British Columbia -B Loss 1-15 819.01 May 4th Northwest D I College Mens Regionals 2025
137 Montana State Loss 7-15 705.33 May 4th Northwest D I College Mens Regionals 2025
24 Victoria Loss 7-15 1350.02 May 4th Northwest D I College Mens Regionals 2025
**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)