#203 Wheaton (Illinois) (7-4)

avg: 972.12  •  sd: 93.99  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
360 Illinois-Chicago Win 11-6 977.89 Mar 22nd Meltdown 2019
351 Southern Illinois-Edwardsville Win 13-7 1034.33 Mar 22nd Meltdown 2019
97 Grand Valley State Loss 6-13 763.8 Mar 22nd Meltdown 2019
427 Wisconsin-Stout** Win 13-3 603.93 Ignored Mar 22nd Meltdown 2019
237 Loyola-Chicago Win 10-9 1024.86 Mar 24th Meltdown 2019
198 Valparaiso Win 7-3 1598.06 Mar 24th Meltdown 2019
109 Truman State Loss 3-13 723.3 Mar 24th Meltdown 2019
112 Wisconsin-Whitewater Loss 8-10 1043.55 Mar 24th Meltdown 2019
302 Rose-Hulman Win 13-6 1252.23 Mar 30th Black Penguins Classic 2019
346 Marquette-B Win 11-5 1100.18 Mar 30th Black Penguins Classic 2019
198 Valparaiso Loss 3-13 398.06 Mar 30th Black Penguins Classic 2019
**Blowout Eligible


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
  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
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)