#198 Marquette (5-7)

avg: 908.8  •  sd: 79.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
81 Purdue Loss 3-7 1036.67 Mar 3rd Midwest Throwdown 2018
238 Washington University-B Win 7-6 695.12 Mar 3rd Midwest Throwdown 2018
106 St Benedict Loss 7-8 1346.97 Mar 3rd Midwest Throwdown 2018
155 Minnesota-Duluth Loss 4-8 583.24 Mar 3rd Midwest Throwdown 2018
225 Wisconsin-B Win 9-8 818.57 Mar 4th Midwest Throwdown 2018
206 Missouri Loss 7-10 429.03 Mar 4th Midwest Throwdown 2018
210 Central Michigan Win 7-6 920.8 Mar 24th Meltdown 2018
190 Knox Loss 4-5 813.21 Mar 24th Meltdown 2018
142 North Park Win 8-7 1363.37 Mar 24th Meltdown 2018
146 DePaul Loss 8-9 1074.94 Mar 24th Meltdown 2018
209 Northern Michigan Loss 8-9 671.17 Mar 25th Meltdown 2018
193 Valparaiso Win 9-8 1055.66 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)