#218 Miami (Ohio) (14-10)

avg: 996.18  •  sd: 61.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
294 Ball State Win 13-4 1314.61 Mar 1st The Dayton Ultimate Disc Experience DUDE
328 Case Western Reserve-B Win 13-1 1175.65 Mar 1st The Dayton Ultimate Disc Experience DUDE
360 Dayton-B Win 13-2 996.22 Ignored Mar 1st The Dayton Ultimate Disc Experience DUDE
365 SUNY-Buffalo-B** Win 11-2 971.68 Ignored Mar 1st The Dayton Ultimate Disc Experience DUDE
323 Cincinnati -B Win 13-8 1086.93 Mar 2nd The Dayton Ultimate Disc Experience DUDE
167 Dayton Loss 10-14 793.25 Mar 2nd The Dayton Ultimate Disc Experience DUDE
272 Wooster Win 15-9 1308 Mar 2nd The Dayton Ultimate Disc Experience DUDE
244 Kent State Win 7-6 1016.15 Mar 15th Spring Spook
348 Wright State Win 11-7 922.39 Mar 15th Spring Spook
215 Akron Loss 7-13 440.39 Mar 16th Spring Spook
348 Wright State Win 12-6 1034.81 Mar 16th Spring Spook
263 Toledo Win 15-4 1420.43 Mar 16th Spring Spook
215 Akron Loss 5-8 544.31 Apr 12th Ohio D I Mens Conferences 2025
37 Cincinnati Loss 6-13 1196.25 Apr 12th Ohio D I Mens Conferences 2025
348 Wright State Win 12-6 1034.81 Apr 12th Ohio D I Mens Conferences 2025
76 Ohio State Loss 4-13 970.01 Apr 12th Ohio D I Mens Conferences 2025
215 Akron Win 15-8 1562.73 Apr 13th Ohio D I Mens Conferences 2025
263 Toledo Win 12-10 1058.55 Apr 13th Ohio D I Mens Conferences 2025
179 Ohio Win 11-9 1400.41 Apr 13th Ohio D I Mens Conferences 2025
96 Lehigh Loss 9-14 990.75 Apr 26th Ohio Valley D I College Mens Regionals 2025
22 Penn State** Loss 4-15 1372.46 Ignored Apr 26th Ohio Valley D I College Mens Regionals 2025
166 Pennsylvania Loss 7-13 636.54 Apr 26th Ohio Valley D I College Mens Regionals 2025
215 Akron Loss 12-13 872.92 Apr 27th Ohio Valley D I College Mens Regionals 2025
244 Kent State Loss 9-15 375.67 Apr 27th Ohio Valley D I College Mens Regionals 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)