#279 Wisconsin-Platteville (5-7)

avg: 585.54  •  sd: 80.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
40 Colorado College** Loss 4-13 1135.34 Ignored Mar 4th Midwest Throwdown 2023
94 Saint Louis** Loss 3-13 824.8 Ignored Mar 4th Midwest Throwdown 2023
334 Northwestern-B Win 11-9 486.49 Mar 4th Midwest Throwdown 2023
142 Carleton College-CHOP Loss 5-11 583.5 Mar 5th Midwest Throwdown 2023
90 Chicago** Loss 3-13 833.78 Ignored Mar 5th Midwest Throwdown 2023
325 Washington University-B Win 9-7 565.82 Mar 5th Midwest Throwdown 2023
328 Marquette-B Win 11-4 878.63 Apr 1st Illinois Invite1
199 Nebraska Loss 8-11 579.51 Apr 1st Illinois Invite1
272 Ohio Win 6-5 744.98 Apr 1st Illinois Invite1
310 Knox Loss 9-12 35.09 Apr 2nd Illinois Invite1
313 Illinois-B Win 9-5 883.36 Apr 2nd Illinois Invite1
255 Toledo Loss 10-11 590.6 Apr 2nd Illinois Invite1
**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)