#194 St Olaf-B (4-13)

avg: 471.63  •  sd: 80.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
197 Harding Win 9-7 736.86 Feb 22nd Dust Bowl 2025
92 Iowa State** Loss 2-10 512.02 Ignored Feb 22nd Dust Bowl 2025
28 Missouri** Loss 1-13 1134.08 Ignored Feb 22nd Dust Bowl 2025
92 Iowa State Loss 4-8 547.21 Feb 23rd Dust Bowl 2025
200 Truman State Loss 4-5 311.94 Feb 23rd Dust Bowl 2025
44 Washington University** Loss 5-12 963.11 Ignored Feb 23rd Dust Bowl 2025
121 Loyola-Chicago Loss 4-11 329.71 Mar 22nd Meltdown 2025
28 Missouri** Loss 1-13 1134.08 Ignored Mar 22nd Meltdown 2025
209 Wheaton (Illinois) Win 6-5 420.13 Mar 22nd Meltdown 2025
129 Winona State Loss 1-10 245.74 Mar 22nd Meltdown 2025
28 Missouri** Loss 1-7 1134.08 Ignored Mar 23rd Meltdown 2025
129 Winona State Loss 1-6 245.74 Mar 23rd Meltdown 2025
46 Carleton College-Eclipse** Loss 3-15 945.04 Ignored Apr 12th North Central D III Womens Conferences 2025
173 Grinnell Loss 9-10 468.47 Apr 12th North Central D III Womens Conferences 2025
88 Michigan Tech** Loss 0-15 535.42 Ignored Apr 12th North Central D III Womens Conferences 2025
228 Carleton College-C Win 15-5 780.92 Apr 13th North Central D III Womens Conferences 2025
259 St Thomas** Win 15-1 229.26 Ignored Apr 13th North Central D III Womens Conferences 2025
**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)