#320 Washington University-B (6-19)

avg: 651.34  •  sd: 77.34  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
165 John Brown Loss 6-13 668.53 Feb 17th Dust Bowl 2024
145 Southern Illinois-Edwardsville** Loss 2-13 731.75 Ignored Feb 17th Dust Bowl 2024
105 Wisconsin-Milwaukee** Loss 2-13 884.87 Ignored Feb 17th Dust Bowl 2024
356 Wichita State Loss 7-12 -31.43 Feb 17th Dust Bowl 2024
313 Kansas-B Loss 8-13 171.1 Feb 18th Dust Bowl 2024
161 Saint Louis Loss 7-13 725.94 Feb 18th Dust Bowl 2024
353 Carleton College-Karls-C Win 10-8 757.01 Mar 2nd Midwest Throwdown 2024
193 Grinnell Loss 2-13 554.63 Mar 2nd Midwest Throwdown 2024
107 Iowa State** Loss 4-13 876.13 Ignored Mar 2nd Midwest Throwdown 2024
94 Wisconsin-Eau Claire** Loss 2-13 934.71 Ignored Mar 2nd Midwest Throwdown 2024
63 Iowa** Loss 2-11 1086.69 Ignored Mar 3rd Midwest Throwdown 2024
294 Knox Win 8-7 862.81 Mar 3rd Midwest Throwdown 2024
227 St John's (Minnesota) Loss 6-9 611.25 Mar 3rd Midwest Throwdown 2024
110 Davenport** Loss 3-12 867.57 Ignored Mar 30th Old Capitol Open 2024
400 Iowa-B** Win 12-5 642.04 Ignored Mar 30th Old Capitol Open 2024
200 Northern Iowa Loss 2-13 521.31 Mar 30th Old Capitol Open 2024
337 Wisconsin-Stevens Point Win 10-5 1139.58 Mar 30th Old Capitol Open 2024
278 St Thomas Win 11-4 1441.12 Mar 31st Old Capitol Open 2024
304 Luther College Loss 6-7 569.97 Mar 31st Old Capitol Open 2024
95 Arkansas** Loss 1-15 934.59 Ignored Apr 13th Ozarks D I Mens Conferences 2024
233 Oklahoma Loss 10-11 880.7 Apr 13th Ozarks D I Mens Conferences 2024
20 Washington University** Loss 5-13 1486.16 Ignored Apr 13th Ozarks D I Mens Conferences 2024
356 Wichita State Loss 5-12 -110.92 Apr 13th Ozarks D I Mens Conferences 2024
228 Oklahoma State Loss 6-15 422.23 Apr 14th Ozarks D I Mens Conferences 2024
356 Wichita State Win 15-5 1089.08 Apr 14th Ozarks D I 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)