#354 Washington-C (2-9)

avg: 354.31  •  sd: 84.86  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
121 Puget Sound** Loss 1-13 656.91 Ignored Jan 20th Flat Tail Open Tournament 2018
55 Oregon State** Loss 0-13 918.18 Ignored Jan 20th Flat Tail Open Tournament 2018
271 Central Washington Loss 1-13 106.87 Jan 20th Flat Tail Open Tournament 2018
226 Western Washington-B Loss 5-13 237.24 Jan 20th Flat Tail Open Tournament 2018
121 Puget Sound** Loss 4-13 656.91 Ignored Jan 21st Flat Tail Open Tournament 2018
- Oregon State-B Win 8-7 304.54 Jan 21st Flat Tail Open Tournament 2018
121 Puget Sound Loss 7-13 699.38 Mar 3rd 18th Annual PLU BBQ Open
205 Gonzaga Loss 1-13 325.34 Mar 3rd 18th Annual PLU BBQ Open
221 Whitworth Loss 8-12 431 Mar 3rd 18th Annual PLU BBQ Open
290 Portland State Loss 4-14 18.12 Mar 4th 18th Annual PLU BBQ Open
405 Pacific Lutheran-B Win 15-3 586.69 Mar 4th 18th Annual PLU BBQ Open
**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)