#55 St Olaf (14-9)

avg: 1441.22  •  sd: 96.63  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
191 Colorado Mines** Win 12-3 1070.45 Ignored Feb 22nd Dust Bowl 2025
256 John Brown** Win 12-2 434.14 Ignored Feb 22nd Dust Bowl 2025
111 Kansas Win 12-3 1598.48 Feb 22nd Dust Bowl 2025
43 Washington University Loss 4-6 1194.74 Feb 23rd Dust Bowl 2025
28 Missouri Loss 5-6 1606.99 Mar 1st Midwest Throwdown 2025
117 Northwestern Win 7-3 1539.88 Mar 1st Midwest Throwdown 2025
43 Washington University Loss 3-10 960.35 Mar 1st Midwest Throwdown 2025
145 Wisconsin-La Crosse** Win 13-2 1324.19 Ignored Mar 1st Midwest Throwdown 2025
92 Iowa State Win 8-4 1676.54 Mar 2nd Midwest Throwdown 2025
101 Macalester Win 10-6 1550.64 Mar 2nd Midwest Throwdown 2025
43 Washington University Loss 6-12 981.04 Mar 2nd Midwest Throwdown 2025
42 Duke Loss 9-10 1440.5 Mar 29th East Coast Invite 2025
36 Haverford/Bryn Mawr Loss 7-10 1229.39 Mar 29th East Coast Invite 2025
39 Wesleyan Loss 11-14 1273.99 Mar 29th East Coast Invite 2025
113 West Chester Win 14-6 1551.97 Mar 29th East Coast Invite 2025
45 American Loss 2-5 942.41 Mar 30th East Coast Invite 2025
31 Pittsburgh Loss 7-8 1569.22 Mar 30th East Coast Invite 2025
109 Temple Win 10-6 1517.85 Mar 30th East Coast Invite 2025
227 Carleton College-C** Win 13-1 780.25 Ignored Apr 12th North Central D III Womens Conferences 2025
101 Macalester Win 12-3 1654.48 Apr 12th North Central D III Womens Conferences 2025
128 Winona State Win 7-4 1340.76 Apr 12th North Central D III Womens Conferences 2025
259 St Thomas** Win 15-0 228.59 Ignored Apr 12th North Central D III Womens Conferences 2025
46 Carleton College-Eclipse Win 14-10 1937.56 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)