#332 Milwaukee School of Engineering (3-8)

avg: 559.2  •  sd: 62.18  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
291 Pacific Lutheran Win 12-11 828.37 Mar 9th D III Midwestern Invite 2019
71 Michigan Tech** Loss 4-12 890.99 Ignored Mar 9th D III Midwestern Invite 2019
186 Macalester Loss 9-11 782.42 Mar 9th D III Midwestern Invite 2019
177 Winona State Loss 7-10 672.38 Mar 10th D III Midwestern Invite 2019
177 Winona State Loss 6-13 462.04 Mar 23rd Meltdown 2019
86 Marquette** Loss 3-13 826.08 Ignored Mar 23rd Meltdown 2019
237 Loyola-Chicago Loss 5-13 299.86 Mar 23rd Meltdown 2019
424 Coe Win 11-2 635.35 Mar 23rd Meltdown 2019
351 Southern Illinois-Edwardsville Loss 8-9 351.79 Mar 24th Meltdown 2019
427 Wisconsin-Stout Win 13-9 422.5 Mar 24th Meltdown 2019
276 North Park Loss 10-11 644.64 Mar 24th Meltdown 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)