#402 Cleveland State (2-10)

avg: 33.02  •  sd: 113.13  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
210 Miami (Ohio)** Loss 1-13 317.03 Ignored Mar 10th Boogienights 2018
371 Wright State Loss 10-13 -33.47 Mar 10th Boogienights 2018
428 Dayton-B Win 15-0 326.09 Mar 11th Boogienights 2018
328 Kent State Loss 5-15 -130.85 Mar 11th Boogienights 2018
77 Michigan-B** Loss 2-15 815.18 Ignored Mar 11th Boogienights 2018
383 Indiana-B Loss 7-8 86.77 Mar 24th 2018 B Team Brodown
220 Dartmouth-B** Loss 2-13 273.7 Ignored Mar 24th 2018 B Team Brodown
362 Carnegie Mellon University-B Loss 4-11 -264.5 Mar 24th 2018 B Team Brodown
108 Franciscan** Loss 3-13 701.89 Ignored Mar 24th 2018 B Team Brodown
350 California-Pennsylvania Loss 6-10 -128.82 Mar 24th 2018 B Team Brodown
349 Ohio State-B Loss 1-13 -232.24 Mar 25th 2018 B Team Brodown
404 George Washington-B Win 13-0 616.21 Mar 25th 2018 B Team Brodown
**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)