#229 Oregon State-B (0-9)

avg: 56.3  •  sd: 133.91  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
212 Cal Poly-Humboldt Loss 8-15 -303.95 Jan 27th Trouble in Corvegas
134 Washington State** Loss 5-15 247.81 Ignored Jan 27th Trouble in Corvegas
192 Portland State Loss 11-15 15.55 Jan 27th Trouble in Corvegas
22 Oregon State** Loss 0-15 1124.15 Ignored Jan 27th Trouble in Corvegas
212 Cal Poly-Humboldt Loss 10-12 22.74 Jan 28th Trouble in Corvegas
109 Western Washington** Loss 1-15 405.09 Jan 28th Trouble in Corvegas
- San Jose State** Loss 4-13 135.07 Feb 3rd Stanford Open 2024
159 Portland Loss 7-12 144.44 Feb 3rd Stanford Open 2024
170 Claremont Loss 3-13 -29.62 Feb 3rd Stanford Open 2024
**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)