#312 Portland State (4-7)

avg: 613.97  •  sd: 92.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
192 Gonzaga Loss 4-13 422.54 Jan 26th Flat Tail Open 2019 Mens
121 Puget Sound Loss 6-13 681.02 Jan 26th Flat Tail Open 2019 Mens
446 Lewis & Clark-B** Win 13-1 600 Ignored Jan 26th Flat Tail Open 2019 Mens
180 Humboldt State Loss 9-13 639.86 Jan 26th Flat Tail Open 2019 Mens
383 Washington-C Win 15-7 903.69 Jan 27th Flat Tail Open 2019 Mens
326 Western Washington University-B Win 15-6 1181.73 Jan 27th Flat Tail Open 2019 Mens
168 Whitworth Loss 6-15 487.23 Mar 9th Palouse Open 2019
305 Boise State Loss 11-12 517.33 Mar 9th Palouse Open 2019
326 Western Washington University-B Loss 8-14 45.69 Mar 9th Palouse Open 2019
280 Idaho Win 15-12 1054.72 Mar 10th Palouse Open 2019
305 Boise State Loss 13-15 428.15 Mar 10th Palouse Open 2019
**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)