#64 St. Olaf (19-6)

avg: 1568  •  sd: 86.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
142 Carleton College-CHOP Win 9-7 1462.84 Feb 11th Ugly Dome
183 Minnesota-B Loss 7-13 453.43 Feb 11th Ugly Dome
321 Minnesota-C** Win 13-2 904.01 Ignored Feb 11th Ugly Dome
210 Wisconsin-Eau Claire Win 12-7 1416.81 Feb 11th Ugly Dome
294 Winona State** Win 13-4 1105.83 Ignored Feb 11th Ugly Dome
318 Carleton College-Karls-C** Win 13-1 925.43 Ignored Mar 4th Midwest Throwdown 2023
90 Chicago Win 11-9 1682.99 Mar 4th Midwest Throwdown 2023
118 Marquette Win 10-7 1690.44 Mar 4th Midwest Throwdown 2023
40 Colorado College Loss 4-13 1135.34 Mar 5th Midwest Throwdown 2023
54 Northwestern Win 12-8 2057.34 Mar 5th Midwest Throwdown 2023
68 Wisconsin-Milwaukee Win 10-9 1674.93 Mar 5th Midwest Throwdown 2023
183 Minnesota-B Win 13-2 1610.96 Mar 25th Old Capitol Open
93 Iowa Win 8-7 1551.29 Mar 25th Old Capitol Open
94 Saint Louis Win 10-7 1814.47 Mar 25th Old Capitol Open
145 Carthage Win 10-9 1297.13 Mar 26th Old Capitol Open
93 Iowa Win 11-6 1972.98 Mar 26th Old Capitol Open
199 Nebraska Loss 8-9 820.12 Mar 26th Old Capitol Open
115 Michigan State Win 10-3 1910.66 Apr 1st Huck Finn1
92 Missouri S&T Loss 8-9 1304.82 Apr 1st Huck Finn1
98 Kentucky Win 8-3 2016.81 Apr 1st Huck Finn1
112 Illinois Win 11-6 1862.67 Apr 1st Huck Finn1
85 Alabama Win 7-3 2046.99 Apr 2nd Huck Finn1
61 Emory Win 11-8 1942.59 Apr 2nd Huck Finn1
38 Purdue Loss 8-12 1331.95 Apr 2nd Huck Finn1
49 Notre Dame Loss 10-13 1315.11 Apr 2nd Huck Finn1
**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)