#348 Iowa State-B (4-8)

avg: 369.71  •  sd: 80.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
99 Missouri S&T** Loss 4-15 737.88 Ignored Mar 3rd Midwest Throwdown 2018
170 Kansas State** Loss 4-15 459.64 Ignored Mar 3rd Midwest Throwdown 2018
363 Wisconsin-Oshkosh Loss 11-12 207.91 Mar 3rd Midwest Throwdown 2018
334 Illinois State-B Loss 7-9 150.98 Mar 4th Midwest Throwdown 2018
392 Drury Win 13-11 373.7 Mar 4th Midwest Throwdown 2018
306 Carleton College-Hot Karls Loss 10-14 161.66 Mar 4th Midwest Throwdown 2018
294 Illinois-B Loss 6-8 296.54 Mar 24th Meltdown 2018
49 Marquette** Loss 1-11 948.36 Ignored Mar 24th Meltdown 2018
363 Wisconsin-Oshkosh Win 9-8 457.91 Mar 24th Meltdown 2018
346 Illinois-Chicago Win 9-7 667.97 Mar 25th Meltdown 2018
339 Northern Michigan Win 10-7 800.78 Mar 25th Meltdown 2018
262 Valparaiso Loss 3-15 144.09 Mar 25th Meltdown 2018
**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)