#138 Oregon State (6-6)

avg: 934.17  •  sd: 68.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
30 Utah** Loss 4-14 1158.87 Ignored Feb 2nd Big Sky Brawl 2019
74 Denver Loss 7-10 932.21 Feb 2nd Big Sky Brawl 2019
152 Montana State Win 9-8 986.75 Feb 2nd Big Sky Brawl 2019
120 Arizona State Loss 6-9 608.61 Feb 3rd Big Sky Brawl 2019
216 Montana Win 15-1 1032.17 Feb 3rd Big Sky Brawl 2019
74 Denver Loss 6-12 742.57 Feb 3rd Big Sky Brawl 2019
129 Pacific Lutheran Win 10-8 1262.2 Feb 23rd 2019 PLU Womens BBQ
68 Lewis & Clark Loss 2-12 730.23 Feb 23rd 2019 PLU Womens BBQ
186 Western Washington-B Win 13-7 1168.67 Feb 23rd 2019 PLU Womens BBQ
- Pacific Lutheran-B** Win 15-0 429.88 Ignored Feb 24th 2019 PLU Womens BBQ
54 Puget Sound Loss 8-15 927.32 Feb 24th 2019 PLU Womens BBQ
186 Western Washington-B Win 11-9 860.34 Feb 24th 2019 PLU Womens BBQ
**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)