#163 Wisconsin- La Crosse (4-4)

avg: 1078.77  •  sd: 77.05  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
261 Drake Win 11-2 1347.36 Feb 17th Ugly Dome 2018
101 Minnesota-B Loss 4-11 730.15 Feb 17th Ugly Dome 2018
219 Macalester Win 11-7 1341.76 Feb 17th Ugly Dome 2018
306 Carleton College-Hot Karls Win 11-1 1160.36 Feb 17th Ugly Dome 2018
363 Wisconsin-Oshkosh** Win 11-1 932.91 Ignored Feb 17th Ugly Dome 2018
70 Arkansas Loss 12-14 1218.57 Mar 31st Huck Finn 2018
88 Alabama-Huntsville Loss 8-15 823.5 Mar 31st Huck Finn 2018
62 Vermont Loss 10-15 1012.23 Mar 31st Huck Finn 2018
**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)