#139 Michigan State (10-9)

avg: 1254.26  •  sd: 75.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
188 Mary Washington Win 9-5 1471.92 Feb 10th Black Pearl Invitational 2018
86 Maryland Loss 9-11 1343.1 Feb 10th Black Pearl Invitational 2018
173 East Carolina Win 13-8 1534.33 Feb 10th Black Pearl Invitational 2018
222 Drexel Win 10-2 1303.13 Feb 10th Black Pearl Invitational 2018
255 Christopher Newport** Win 13-2 937.52 Ignored Feb 11th Black Pearl Invitational 2018
90 Georgia College Loss 6-7 1437.37 Feb 11th Black Pearl Invitational 2018
188 Mary Washington Win 8-7 1067.86 Feb 11th Black Pearl Invitational 2018
268 Pennsylvania-B** Win 12-4 418.55 Ignored Feb 24th Commonwealth Cup 2018
80 James Madison Loss 4-15 1047.06 Feb 24th Commonwealth Cup 2018
145 Liberty Loss 7-9 934.69 Feb 25th Commonwealth Cup 2018
148 Virginia Tech Loss 8-10 926.92 Feb 25th Commonwealth Cup 2018
86 Maryland Loss 7-12 1071.79 Feb 25th Commonwealth Cup 2018
132 Cincinnati Win 10-5 1857.57 Mar 24th CWRUL Memorial 2018
192 Ohio Wesleyan Win 13-4 1531.92 Mar 24th CWRUL Memorial 2018
133 Indiana Win 9-8 1403.7 Mar 24th CWRUL Memorial 2018
221 Michigan-B Win 10-1 1309.51 Mar 24th CWRUL Memorial 2018
117 Carnegie Mellon Loss 4-8 835.8 Mar 25th CWRUL Memorial 2018
45 Case Western Reserve** Loss 2-12 1378.58 Ignored Mar 25th CWRUL Memorial 2018
102 Ball State Loss 4-9 904.81 Mar 25th CWRUL Memorial 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)