#153 Kentucky (12-11)

avg: 1265.58  •  sd: 53.02  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
217 Akron Win 13-4 1614.04 Mar 15th Spring Spook
321 Cincinnati -B Win 8-6 902.38 Mar 15th Spring Spook
217 Akron Win 15-6 1614.04 Mar 16th Spring Spook
346 Wright State** Win 15-3 1070.23 Ignored Mar 16th Spring Spook
145 Oberlin Loss 7-14 701.91 Mar 16th Spring Spook
39 Cincinnati Loss 8-15 1246.81 Mar 29th Huck Finn 2025
93 Southern Illinois-Edwardsville Loss 7-12 967.12 Mar 29th Huck Finn 2025
87 Missouri S&T Loss 8-12 1066.43 Mar 29th Huck Finn 2025
96 Missouri Win 10-9 1602.57 Mar 29th Huck Finn 2025
117 Mississippi State Loss 10-12 1137.13 Mar 30th Huck Finn 2025
201 Saint Louis Win 10-9 1185.04 Mar 30th Huck Finn 2025
150 Macalester Win 8-6 1577.83 Mar 30th Huck Finn 2025
292 Ball State Win 13-11 959.27 Apr 12th East Plains D I Mens Conferences 2025
56 Indiana Loss 6-13 1078.9 Apr 12th East Plains D I Mens Conferences 2025
79 Notre Dame Loss 10-12 1333.58 Apr 12th East Plains D I Mens Conferences 2025
52 Purdue Loss 3-13 1114.07 Apr 12th East Plains D I Mens Conferences 2025
26 Michigan** Loss 2-13 1327.18 Ignored Apr 26th Great Lakes D I Mens Regionals 2025
61 Michigan State Loss 10-12 1420.52 Apr 26th Great Lakes D I Mens Regionals 2025
296 Loyola-Chicago Win 13-7 1274.57 Apr 26th Great Lakes D I Mens Regionals 2025
52 Purdue Loss 8-15 1149.26 Apr 26th Great Lakes D I Mens Regionals 2025
148 Grand Valley Win 11-9 1528.94 Apr 27th Great Lakes D I Mens Regionals 2025
93 Southern Illinois-Edwardsville Win 15-14 1612.63 Apr 27th Great Lakes D I Mens Regionals 2025
227 Michigan-B Win 15-11 1343.14 Apr 27th Great Lakes D I 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)