#54 Oregon State (14-6)

avg: 1715.45  •  sd: 78.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
51 British Columbia-B Win 8-7 1869.01 Feb 1st Stanford Open Womens
133 Cal Poly-SLO-B Win 13-7 1618 Feb 1st Stanford Open Womens
35 Carleton College-Eclipse Win 9-8 2076.89 Feb 1st Stanford Open Womens
51 British Columbia-B Win 10-7 2133.67 Feb 2nd Stanford Open Womens
135 Stanford-B Win 8-5 1501.59 Feb 2nd Stanford Open Womens
68 Santa Clara Loss 9-11 1338.34 Feb 2nd Stanford Open Womens
65 Portland Win 9-7 1906.24 Feb 2nd Stanford Open Womens
72 Colorado College Win 10-6 2052.38 Feb 8th DIII Grand Prix 2025
61 Lewis & Clark Win 12-10 1894.56 Feb 8th DIII Grand Prix 2025
121 Puget Sound Win 12-5 1771.73 Feb 8th DIII Grand Prix 2025
44 Whitman Loss 7-13 1273.38 Feb 8th DIII Grand Prix 2025
35 Carleton College-Eclipse Loss 10-12 1713.76 Feb 9th DIII Grand Prix 2025
122 Claremont Win 13-5 1750.79 Feb 9th DIII Grand Prix 2025
65 Portland Loss 10-12 1388.78 Feb 9th DIII Grand Prix 2025
206 Cal Poly-Humboldt** Win 13-2 1116.85 Ignored Mar 8th PACcon
234 Lewis & Clark -B** Win 11-2 830.67 Ignored Mar 8th PACcon
22 Western Washington Loss 10-12 2012.55 Mar 8th PACcon
61 Lewis & Clark Win 11-10 1781.43 Mar 9th PACcon
173 Pacific Lutheran Win 11-7 1260.36 Mar 9th PACcon
22 Western Washington Loss 5-13 1650.67 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)