#310 Knox (5-6)

avg: 380.45  •  sd: 102.55  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
48 Iowa State** Loss 1-13 1046.34 Ignored Mar 4th Midwest Throwdown 2023
210 Wisconsin-Eau Claire Loss 5-13 296.3 Mar 4th Midwest Throwdown 2023
296 Wisconsin-B Loss 7-10 112.16 Mar 4th Midwest Throwdown 2023
246 Northern Iowa Loss 4-8 184.04 Mar 5th Midwest Throwdown 2023
365 Grinnell-B** Win 11-2 109.44 Ignored Mar 5th Midwest Throwdown 2023
280 Ball State Loss 2-12 -21.53 Apr 1st Illinois Invite1
301 Purdue-B Win 7-6 569.41 Apr 1st Illinois Invite1
360 Wisconsin-Milwaukee-B Win 7-5 179.13 Apr 1st Illinois Invite1
215 North Park Loss 8-13 385.38 Apr 2nd Illinois Invite1
279 Wisconsin-Platteville Win 12-9 930.9 Apr 2nd Illinois Invite1
346 Notre Dame-B Win 7-1 692.32 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)