#255 Toledo (8-5)

avg: 715.6  •  sd: 86.35  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
145 Carthage Loss 5-12 572.13 Mar 25th Old Capitol Open
321 Minnesota-C Win 13-6 904.01 Mar 25th Old Capitol Open
207 Illinois State Win 10-7 1300.53 Mar 25th Old Capitol Open
341 Iowa State-B Win 12-6 749.93 Mar 25th Old Capitol Open
277 Loyola-Chicago Win 12-6 1171.73 Mar 26th Old Capitol Open
207 Illinois State Loss 4-10 310.86 Mar 26th Old Capitol Open
226 Wisconsin-La Crosse Win 12-10 1042.29 Mar 26th Old Capitol Open
313 Illinois-B Loss 9-10 229.3 Apr 1st Illinois Invite1
192 Wright State Loss 3-4 851.57 Apr 1st Illinois Invite1
- St. Thomas Win 10-9 542.73 Apr 1st Illinois Invite1
279 Wisconsin-Platteville Win 11-10 710.54 Apr 2nd Illinois Invite1
215 North Park Loss 8-12 440.39 Apr 2nd Illinois Invite1
301 Purdue-B Win 9-8 569.41 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)