#346 Marquette-B (2-9)

avg: 500.18  •  sd: 89.63  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
112 Wisconsin-Whitewater** Loss 4-13 706.21 Ignored Mar 22nd Meltdown 2019
198 Valparaiso Loss 4-13 398.06 Mar 22nd Meltdown 2019
329 Northern Illinois Loss 7-8 436.93 Mar 22nd Meltdown 2019
309 Illinois State-B Loss 6-9 214.65 Mar 22nd Meltdown 2019
360 Illinois-Chicago Win 8-4 996.01 Mar 24th Meltdown 2019
351 Southern Illinois-Edwardsville Loss 6-7 351.79 Mar 24th Meltdown 2019
316 Purdue-B Win 11-5 1199.8 Mar 24th Meltdown 2019
237 Loyola-Chicago Loss 4-9 299.86 Mar 24th Meltdown 2019
302 Rose-Hulman Loss 9-11 403.03 Mar 30th Black Penguins Classic 2019
203 Wheaton (Illinois) Loss 5-11 372.12 Mar 30th Black Penguins Classic 2019
198 Valparaiso Loss 2-13 398.06 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)