#287 Portland (2-10)

avg: 543.82  •  sd: 66.57  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
113 Claremont** Loss 2-10 712.42 Ignored Feb 4th Stanford Open
78 Santa Clara** Loss 4-13 875.08 Ignored Feb 4th Stanford Open
291 California-Santa Cruz-B Win 10-8 785.72 Feb 4th Stanford Open
320 Stanford-B Win 8-7 438.66 Feb 4th Stanford Open
241 Humboldt State Loss 2-12 157.23 Feb 5th Stanford Open
138 Occidental Loss 7-10 808.61 Feb 5th Stanford Open
159 Puget Sound Loss 8-12 673.84 Feb 5th Stanford Open
253 Oregon State-B Loss 10-12 479.49 Mar 4th PLU BBQ Mens
303 Whitworth Loss 9-11 185.41 Mar 4th PLU BBQ Mens
137 Portland State** Loss 4-11 624.16 Ignored Mar 4th PLU BBQ Mens
213 Reed Loss 12-13 764.21 Mar 5th PLU BBQ Mens
222 Seattle Loss 11-13 623.36 Mar 5th PLU BBQ Mens
**Blowout Eligible


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)