#191 Wisconsin-Milwaukee (6-5)

avg: 932.96  •  sd: 107.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
220 Northwestern-B Loss 7-8 598.7 Mar 3rd Midwest Throwdown 2018
236 Drake Win 8-3 1179.42 Mar 3rd Midwest Throwdown 2018
251 Tulsa Win 11-3 1018.35 Mar 3rd Midwest Throwdown 2018
143 Truman State Loss 3-8 630.82 Mar 3rd Midwest Throwdown 2018
220 Northwestern-B Win 9-1 1323.7 Mar 4th Midwest Throwdown 2018
151 Grinnell College Loss 7-8 1055.11 Mar 4th Midwest Throwdown 2018
168 Luther Loss 7-12 538.85 Mar 4th Midwest Throwdown 2018
201 SUNY-Fredonia Win 7-5 1209.09 Mar 17th Rip Tide 2018
201 SUNY-Fredonia Win 7-3 1480.95 Mar 17th Rip Tide 2018
201 SUNY-Fredonia Win 6-5 1005.95 Mar 17th Rip Tide 2018
201 SUNY-Fredonia Loss 3-7 280.95 Mar 17th Rip Tide 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)