#210 Miami (Ohio) (8-2)

avg: 917.03  •  sd: 100.83  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
331 Denison Win 12-11 569.67 Mar 3rd DiscThrow Inferno 2K18
416 Kettering** Win 15-3 455.32 Ignored Mar 3rd DiscThrow Inferno 2K18
173 Oberlin Loss 11-15 652.58 Mar 3rd DiscThrow Inferno 2K18
268 Allegheny Win 13-8 1212.78 Mar 4th DiscThrow Inferno 2K18
228 Wooster Win 13-11 1065.06 Mar 4th DiscThrow Inferno 2K18
108 Franciscan Win 15-14 1426.89 Mar 4th DiscThrow Inferno 2K18
402 Cleveland State** Win 13-1 633.02 Ignored Mar 10th Boogienights 2018
371 Wright State Win 13-11 523.52 Mar 10th Boogienights 2018
328 Kent State Win 14-8 1005.19 Mar 11th Boogienights 2018
77 Michigan-B Loss 9-15 899.7 Mar 11th Boogienights 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)