#103 Truman State (7-4)

avg: 1353.36  •  sd: 138.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
189 Luther Win 12-7 1515.55 Mar 4th Midwest Throwdown 2023
35 Missouri Loss 6-9 1368.26 Mar 4th Midwest Throwdown 2023
365 Grinnell-B** Win 13-1 109.44 Ignored Mar 4th Midwest Throwdown 2023
35 Missouri Loss 7-8 1661.83 Mar 5th Midwest Throwdown 2023
48 Iowa State Loss 7-11 1179.45 Mar 5th Midwest Throwdown 2023
297 Michigan State-B** Win 13-1 1084.66 Ignored Apr 1st King of the Hill
121 Michigan Tech Win 13-12 1418.3 Apr 1st King of the Hill
214 Wheaton (Illinois) Win 10-5 1459.39 Apr 1st King of the Hill
121 Michigan Tech Loss 10-15 839.7 Apr 2nd King of the Hill
335 Southern Illinois-Edwardsville** Win 15-5 835 Ignored Apr 2nd King of the Hill
214 Wheaton (Illinois) Win 15-5 1485.49 Apr 2nd King of the Hill
**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)