#118 Kentucky (15-7)

avg: 1415.92  •  sd: 67.05  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
271 Cincinnati -B Win 13-5 1457.68 Mar 2nd The Dayton Ultimate Disc Experience The DUDE
182 Dayton Win 12-10 1428.52 Mar 2nd The Dayton Ultimate Disc Experience The DUDE
409 Denison** Win 13-3 410.55 Ignored Mar 2nd The Dayton Ultimate Disc Experience The DUDE
369 Illinois-B** Win 13-3 960.32 Ignored Mar 2nd The Dayton Ultimate Disc Experience The DUDE
222 Ball State Win 12-10 1281.79 Mar 3rd The Dayton Ultimate Disc Experience The DUDE
157 Miami (Ohio) Win 12-9 1635.13 Mar 3rd The Dayton Ultimate Disc Experience The DUDE
95 Arkansas Win 7-6 1659.59 Mar 30th Huck Finn 2024
53 Colorado State Loss 8-10 1493.38 Mar 30th Huck Finn 2024
67 Stanford Loss 10-11 1546.57 Mar 30th Huck Finn 2024
119 Colorado College Win 10-7 1797.82 Mar 31st Huck Finn 2024
233 Oklahoma Win 13-8 1501.86 Mar 31st Huck Finn 2024
222 Ball State Win 13-9 1462.23 Apr 13th East Plains D I Mens Conferences 2024
83 Indiana Loss 8-11 1215.71 Apr 13th East Plains D I Mens Conferences 2024
96 Notre Dame Win 12-11 1655.99 Apr 13th East Plains D I Mens Conferences 2024
62 Purdue Loss 7-12 1169.24 Apr 13th East Plains D I Mens Conferences 2024
39 Illinois Loss 7-15 1285.37 Apr 27th Great Lakes D I College Mens Regionals 2024
145 Southern Illinois-Edwardsville Loss 8-9 1206.75 Apr 27th Great Lakes D I College Mens Regionals 2024
236 Loyola-Chicago Win 10-9 1115.9 Apr 27th Great Lakes D I College Mens Regionals 2024
96 Notre Dame Loss 7-15 930.99 Apr 27th Great Lakes D I College Mens Regionals 2024
135 Grand Valley Win 15-14 1496.15 Apr 28th Great Lakes D I College Mens Regionals 2024
254 Michigan-B Win 15-7 1528.81 Apr 28th Great Lakes D I College Mens Regionals 2024
145 Southern Illinois-Edwardsville Win 15-11 1712.91 Apr 28th Great Lakes D I College Mens Regionals 2024
**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)