#28 St Olaf (17-2)

avg: 1921.34  •  sd: 104.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
64 Missouri Win 8-4 2049.17 Mar 2nd Midwest Throwdown 2024
202 Wisconsin-B** Win 13-1 799.06 Ignored Mar 2nd Midwest Throwdown 2024
82 Northwestern Loss 5-6 1207.46 Mar 2nd Midwest Throwdown 2024
92 Saint Louis Win 7-4 1768.64 Mar 3rd Midwest Throwdown 2024
148 Missouri State** Win 11-2 1426.22 Ignored Mar 3rd Midwest Throwdown 2024
51 Macalester Win 5-2 2208.92 Mar 3rd Midwest Throwdown 2024
109 Truman State Loss 4-5 999.52 Mar 3rd Midwest Throwdown 2024
167 Wake Forest** Win 13-0 1174.52 Ignored Mar 23rd Needle in a Ho Stack 2024
59 Georgetown Win 6-1 2119.65 Mar 23rd Needle in a Ho Stack 2024
149 Emory** Win 13-0 1403.75 Ignored Mar 24th Needle in a Ho Stack 2024
88 Virginia Tech Win 8-5 1750.86 Mar 24th Needle in a Ho Stack 2024
50 Davidson Win 10-5 2187.83 Mar 24th Needle in a Ho Stack 2024
43 Alabama-Huntsville Win 9-6 2119.26 Mar 24th Needle in a Ho Stack 2024
145 Grinnell** Win 9-3 1444.08 Ignored Mar 30th Old Capitol Open 2024
140 Wisconsin-Milwaukee** Win 11-0 1485.46 Ignored Mar 30th Old Capitol Open 2024
95 Iowa** Win 9-2 1852.41 Ignored Mar 30th Old Capitol Open 2024
79 Kansas Win 8-1 1955.6 Mar 31st Old Capitol Open 2024
51 Macalester Win 6-4 1974.53 Mar 31st Old Capitol Open 2024
34 Minnesota Win 7-3 2418.79 Mar 31st Old Capitol Open 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)