#373 Northwestern-B (4-14)

avg: 332.42  •  sd: 102.33  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
63 Iowa** Loss 0-13 1086.69 Ignored Mar 2nd Midwest Throwdown 2024
185 Minnesota-Duluth** Loss 2-13 577.59 Ignored Mar 2nd Midwest Throwdown 2024
45 St Olaf** Loss 4-13 1211.34 Ignored Mar 2nd Midwest Throwdown 2024
353 Carleton College-Karls-C Loss 6-9 75.78 Mar 3rd Midwest Throwdown 2024
294 Knox Loss 5-7 409.67 Mar 3rd Midwest Throwdown 2024
227 St John's (Minnesota) Loss 6-9 611.25 Mar 3rd Midwest Throwdown 2024
101 Colorado Mines** Loss 1-13 912.08 Ignored Mar 30th Old Capitol Open 2024
122 Minnesota-B** Loss 3-13 800.63 Ignored Mar 30th Old Capitol Open 2024
414 Wisconsin-Milwaukee-B** Win 9-3 135.23 Ignored Mar 30th Old Capitol Open 2024
227 St John's (Minnesota)** Loss 0-13 429.82 Ignored Mar 30th Old Capitol Open 2024
368 Iowa State-B Loss 6-9 -17.9 Mar 31st Old Capitol Open 2024
337 Wisconsin-Stevens Point Loss 4-12 -34.32 Mar 31st Old Capitol Open 2024
357 Michigan State-B Loss 7-11 7.75 Apr 13th Great Lakes Dev Mens Conferences 2024
384 Notre Dame-B Win 15-10 712.17 Apr 13th Great Lakes Dev Mens Conferences 2024
323 Purdue-B Loss 6-14 30.64 Apr 13th Great Lakes Dev Mens Conferences 2024
369 Illinois-B Win 11-9 609.52 Apr 14th Great Lakes Dev Mens Conferences 2024
323 Purdue-B Win 13-9 1049.21 Apr 14th Great Lakes Dev Mens Conferences 2024
323 Purdue-B Loss 7-12 110.13 Apr 14th Great Lakes Dev Mens 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)