#171 Truman State (9-3)

avg: 1038.6  •  sd: 39.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
334 Illinois State-B** Win 15-3 1030.32 Ignored Mar 3rd Midwest Throwdown 2018
306 Carleton College-Hot Karls Win 15-9 1075.84 Mar 3rd Midwest Throwdown 2018
201 Wisconsin-Eau Claire Win 14-13 1057.63 Mar 3rd Midwest Throwdown 2018
45 Illinois State Loss 7-15 986.15 Mar 4th Midwest Throwdown 2018
233 Missouri Win 13-11 1041.24 Mar 4th Midwest Throwdown 2018
69 Carleton College-GoP Loss 2-15 849.46 Mar 4th Midwest Throwdown 2018
346 Illinois-Chicago Win 9-6 807.2 Mar 24th Meltdown 2018
- Chicago-B** Win 10-3 1038.3 Ignored Mar 24th Meltdown 2018
246 Winona State Win 11-9 1029.01 Mar 24th Meltdown 2018
239 Bradley Win 11-3 1392.45 Mar 25th Meltdown 2018
118 Wisconsin-Whitewater Loss 9-11 1015.62 Mar 25th Meltdown 2018
188 Wisconsin-B Win 10-9 1105.67 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)