#142 North Park (9-3)

avg: 1163.76  •  sd: 93.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
384 Grinnell** Win 15-1 807.76 Ignored Mar 3rd Midwest Throwdown 2018
139 Luther Win 15-13 1382.22 Mar 3rd Midwest Throwdown 2018
413 Wisconsin-Milwaukee-B** Win 15-2 499.67 Ignored Mar 3rd Midwest Throwdown 2018
45 Illinois State Loss 10-15 1132.55 Mar 4th Midwest Throwdown 2018
114 Minnesota-Duluth Loss 12-13 1156.07 Mar 4th Midwest Throwdown 2018
233 Missouri Win 9-5 1341.46 Mar 4th Midwest Throwdown 2018
304 Olivet Nazarene Win 9-6 986.98 Mar 24th Meltdown 2018
363 Wisconsin-Oshkosh Win 9-5 861.97 Mar 24th Meltdown 2018
- Chicago-B Win 10-5 1012.2 Mar 24th Meltdown 2018
239 Bradley Win 14-6 1392.45 Mar 25th Meltdown 2018
157 St Olaf Loss 7-10 715.67 Mar 25th Meltdown 2018
118 Wisconsin-Whitewater Win 11-8 1630.43 Mar 25th Meltdown 2018
**Blowout Eligible

FAQ

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)