#261 Drake (5-6)

avg: 747.36  •  sd: 69.17  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
306 Carleton College-Hot Karls Win 11-9 809.56 Feb 17th Ugly Dome 2018
219 Macalester Loss 8-11 509.26 Feb 17th Ugly Dome 2018
163 Wisconsin- La Crosse Loss 2-11 478.77 Feb 17th Ugly Dome 2018
363 Wisconsin-Oshkosh Win 11-6 879.6 Feb 17th Ugly Dome 2018
101 Minnesota-B Loss 2-11 730.15 Feb 17th Ugly Dome 2018
401 Marquette-B** Win 15-4 641.72 Ignored Mar 3rd Midwest Throwdown 2018
259 Northern Illinois Win 15-9 1267.88 Mar 3rd Midwest Throwdown 2018
96 Missouri State Loss 9-15 835.02 Mar 3rd Midwest Throwdown 2018
384 Grinnell Win 15-6 807.76 Mar 4th Midwest Throwdown 2018
188 Wisconsin-B Loss 3-15 380.67 Mar 4th Midwest Throwdown 2018
201 Wisconsin-Eau Claire Loss 14-16 724.34 Mar 4th Midwest Throwdown 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)