#297 Michigan State-B (2-8)

avg: 484.49  •  sd: 98.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
115 Michigan State** Loss 3-11 710.55 Ignored Mar 19th Meltdown College
212 Grand Valley Loss 3-7 290.63 Mar 19th Meltdown College
236 Eastern Michigan Win 9-7 1046.16 Mar 19th Meltdown College
198 Western Michigan Loss 0-2 348.15 Mar 19th Meltdown College
122 Michigan Tech** Loss 2-13 693.2 Ignored Apr 1st King of the Hill
103 Truman State** Loss 1-13 753.27 Ignored Apr 1st King of the Hill
214 Wheaton (Illinois) Loss 1-13 285.37 Apr 1st King of the Hill
282 Valparaiso Loss 7-15 -25.27 Apr 2nd King of the Hill
335 Southern Illinois-Edwardsville Win 15-7 834.83 Apr 2nd King of the Hill
231 Hillsdale Loss 10-12 553.21 Apr 2nd King of the Hill
**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)