#371 Wright State (4-7)

avg: 294.68  •  sd: 69.02  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
210 Miami (Ohio) Loss 11-13 688.19 Mar 10th Boogienights 2018
402 Cleveland State Win 13-10 361.17 Mar 10th Boogienights 2018
428 Dayton-B Win 15-2 326.09 Mar 11th Boogienights 2018
328 Kent State Loss 10-15 15.55 Mar 11th Boogienights 2018
147 Akron** Loss 4-15 548.15 Ignored Mar 11th Boogienights 2018
396 Kentucky-B Win 11-10 234.63 Mar 24th CWRUL Memorial 2018
277 Eastern Michigan Loss 9-11 431.54 Mar 24th CWRUL Memorial 2018
293 Trine Loss 8-11 238.12 Mar 24th CWRUL Memorial 2018
299 John Carroll Loss 8-15 23.64 Mar 25th CWRUL Memorial 2018
288 Ohio Northern Loss 7-15 31.37 Mar 25th CWRUL Memorial 2018
378 Michigan State-B Win 11-8 619.64 Mar 25th CWRUL Memorial 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)