#104 Dayton (11-3)

avg: 1214.61  •  sd: 102.75  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
130 Towson Win 11-10 1241.83 Feb 3rd Huckin in the Hills X
361 Ohio-B** Win 13-0 553.02 Ignored Feb 3rd Huckin in the Hills X
281 Edinboro** Win 13-3 1050.84 Ignored Feb 3rd Huckin in the Hills X
203 West Virginia Win 10-5 1393.27 Feb 3rd Huckin in the Hills X
281 Edinboro** Win 15-4 1050.84 Ignored Feb 4th Huckin in the Hills X
203 West Virginia Win 15-4 1419.37 Feb 4th Huckin in the Hills X
51 Franciscan Loss 12-15 1195.79 Feb 4th Huckin in the Hills X
232 Cincinnati -B Win 13-3 1306.38 Mar 2nd The Dayton Ultimate Disc Experience The DUDE
375 Denison** Win 13-1 249.51 Ignored Mar 2nd The Dayton Ultimate Disc Experience The DUDE
336 Illinois-B** Win 13-1 719.82 Ignored Mar 2nd The Dayton Ultimate Disc Experience The DUDE
88 Kentucky Loss 10-12 1064.31 Mar 2nd The Dayton Ultimate Disc Experience The DUDE
205 Ball State Win 15-4 1405.79 Mar 3rd The Dayton Ultimate Disc Experience The DUDE
303 Wright State** Win 15-3 938.47 Ignored Mar 3rd The Dayton Ultimate Disc Experience The DUDE
149 Miami (Ohio) Loss 9-11 786.75 Mar 3rd The Dayton Ultimate Disc Experience The DUDE
**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)