#173 Pacific Lutheran (4-7)

avg: 793.46  •  sd: 85.88  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
170 Seattle Loss 9-12 459.01 Feb 22nd PLU Womens BBQ 2025
121 Puget Sound Loss 4-13 571.73 Feb 22nd PLU Womens BBQ 2025
207 Washington-B Loss 7-8 385.57 Feb 22nd PLU Womens BBQ 2025
170 Seattle Win 13-10 1132.52 Feb 23rd PLU Womens BBQ 2025
121 Puget Sound Loss 5-13 571.73 Feb 23rd PLU Womens BBQ 2025
61 Lewis & Clark** Loss 2-13 1056.43 Ignored Mar 8th PACcon
65 Portland** Loss 3-12 1026.91 Ignored Mar 8th PACcon
207 Washington-B Win 10-6 1006.72 Mar 8th PACcon
206 Cal Poly-Humboldt Win 9-6 935.42 Mar 9th PACcon
234 Lewis & Clark -B Win 7-4 726.83 Mar 9th PACcon
54 Oregon State Loss 7-11 1248.56 Mar 9th PACcon
**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)