#125 St Benedict (7-4)

avg: 1012.69  •  sd: 110.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
116 Air Force Win 11-8 1412.82 Mar 2nd Midwest Throwdown 2019
148 Marquette Loss 5-9 351.88 Mar 2nd Midwest Throwdown 2019
35 Truman State Loss 7-9 1405.11 Mar 2nd Midwest Throwdown 2019
215 Olivet Nazarene Win 5-1 1033.06 Mar 23rd Meltdown 2019
267 Illinois-Chicago** Win 11-2 486.17 Ignored Mar 23rd Meltdown 2019
126 North Park Win 10-8 1274.8 Mar 23rd Meltdown 2019
177 Wisconsin-La Crosse Win 11-1 1269 Mar 23rd Meltdown 2019
222 Valparaiso** Win 11-3 984.15 Ignored Mar 23rd Meltdown 2019
126 North Park Loss 7-13 454.6 Mar 24th Meltdown 2019
78 Winona State Loss 7-10 888.49 Mar 24th Meltdown 2019
218 Loyola-Chicago Win 13-3 1023.24 Mar 24th Meltdown 2019
**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)