#35 St Olaf (20-4)

avg: 1778.48  •  sd: 91.71  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
77 Missouri Win 8-4 1933.77 Mar 2nd Midwest Throwdown 2024
231 Wisconsin-B** Win 13-1 740.11 Ignored Mar 2nd Midwest Throwdown 2024
70 Northwestern Loss 5-6 1294.28 Mar 2nd Midwest Throwdown 2024
113 Saint Louis Win 7-4 1627.44 Mar 3rd Midwest Throwdown 2024
177 Missouri State** Win 11-2 1282.97 Ignored Mar 3rd Midwest Throwdown 2024
45 Macalester Win 5-2 2242.54 Mar 3rd Midwest Throwdown 2024
133 Truman State Loss 4-5 856.98 Mar 3rd Midwest Throwdown 2024
188 Wake Forest** Win 13-0 1166.45 Ignored Mar 23rd Needle in a Ho Stack 2024
50 Georgetown Win 6-1 2201.27 Mar 23rd Needle in a Ho Stack 2024
162 Emory** Win 13-0 1416.03 Ignored Mar 24th Needle in a Ho Stack 2024
89 Virginia Tech Win 8-5 1760.52 Mar 24th Needle in a Ho Stack 2024
74 Davidson Win 10-5 1949.02 Mar 24th Needle in a Ho Stack 2024
31 Alabama-Huntsville Win 9-6 2250.67 Mar 24th Needle in a Ho Stack 2024
128 Grinnell** Win 9-3 1639.8 Ignored Mar 30th Old Capitol Open 2024
161 Wisconsin-Milwaukee** Win 11-0 1417.87 Ignored Mar 30th Old Capitol Open 2024
102 Iowa Win 9-2 1812.27 Mar 30th Old Capitol Open 2024
83 Kansas Win 8-1 1943 Mar 31st Old Capitol Open 2024
45 Macalester Win 6-4 2008.15 Mar 31st Old Capitol Open 2024
40 Minnesota Win 7-3 2313.8 Mar 31st Old Capitol Open 2024
128 Grinnell** Win 15-4 1639.8 Ignored Apr 13th North Central D III Womens Conferences 2024
227 Carleton College-C** Win 15-1 782.75 Ignored Apr 13th North Central D III Womens Conferences 2024
106 Michigan Tech Win 14-6 1788.38 Apr 13th North Central D III Womens Conferences 2024
45 Macalester Loss 6-7 1517.54 Apr 14th North Central D III Womens Conferences 2024
37 Carleton College-Eclipse Loss 2-15 1149.41 Apr 14th North Central D III Womens Conferences 2024
**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)