#348 Wright State (4-18)

avg: 455.5  •  sd: 68.61  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
323 Cincinnati -B Win 15-7 1190.77 Mar 1st The Dayton Ultimate Disc Experience DUDE
167 Dayton** Loss 4-15 591.95 Ignored Mar 1st The Dayton Ultimate Disc Experience DUDE
272 Wooster Loss 3-15 192.52 Mar 1st The Dayton Ultimate Disc Experience DUDE
294 Ball State Win 12-11 839.61 Mar 2nd The Dayton Ultimate Disc Experience DUDE
167 Dayton** Loss 2-15 591.95 Ignored Mar 2nd The Dayton Ultimate Disc Experience DUDE
272 Wooster Loss 11-14 479.18 Mar 2nd The Dayton Ultimate Disc Experience DUDE
218 Miami (Ohio) Loss 7-11 529.29 Mar 15th Spring Spook
144 Oberlin Loss 6-11 723.59 Mar 15th Spring Spook
323 Cincinnati -B Loss 7-13 33.24 Mar 16th Spring Spook
153 Kentucky** Loss 3-15 648.49 Ignored Mar 16th Spring Spook
218 Miami (Ohio) Loss 6-12 416.87 Mar 16th Spring Spook
390 Bradley Win 10-6 662.59 Mar 29th Corny Classic College 2025
257 Illinois-B Loss 5-9 316.68 Mar 29th Corny Classic College 2025
410 Illinois-C** Win 13-3 408.04 Ignored Mar 29th Corny Classic College 2025
298 Knox Loss 7-11 229.66 Mar 29th Corny Classic College 2025
347 North Park Loss 2-3 333.81 Mar 30th Corny Classic College 2025
215 Akron Loss 6-12 418.61 Apr 12th Ohio D I Mens Conferences 2025
37 Cincinnati** Loss 4-13 1196.25 Ignored Apr 12th Ohio D I Mens Conferences 2025
218 Miami (Ohio) Loss 6-12 416.87 Apr 12th Ohio D I Mens Conferences 2025
76 Ohio State** Loss 4-13 970.01 Ignored Apr 12th Ohio D I Mens Conferences 2025
244 Kent State Loss 3-15 291.15 Apr 13th Ohio D I Mens Conferences 2025
263 Toledo Loss 10-15 366.83 Apr 13th Ohio D I Mens Conferences 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)