#258 Olivet Nazarene (7-4)

avg: 830.05  •  sd: 72.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
316 Purdue-B Win 13-8 1095.96 Mar 22nd Meltdown 2019
421 Carthage College Win 13-7 633.87 Mar 22nd Meltdown 2019
109 Truman State Loss 7-13 765.77 Mar 22nd Meltdown 2019
276 North Park Win 12-11 894.64 Mar 22nd Meltdown 2019
329 Northern Illinois Win 11-10 686.93 Mar 24th Meltdown 2019
97 Grand Valley State Loss 9-13 945.23 Mar 24th Meltdown 2019
198 Valparaiso Win 12-8 1439.21 Mar 24th Meltdown 2019
112 Wisconsin-Whitewater Loss 5-13 706.21 Mar 24th Meltdown 2019
376 Indiana Wesleyan Win 9-7 632.76 Mar 30th Black Penguins Classic 2019
386 Southern Indiana Win 11-8 653.1 Mar 30th Black Penguins Classic 2019
215 Butler Loss 10-12 690.21 Mar 30th Black Penguins Classic 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)