#65 Tennessee (8-10)

avg: 1453.54  •  sd: 53.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
61 Alabama-Huntsville Loss 7-12 943.83 Feb 15th Queen City Tune Up 2025
60 Michigan State Win 12-9 1831.71 Feb 15th Queen City Tune Up 2025
15 Washington University Loss 5-13 1351.63 Feb 15th Queen City Tune Up 2025
104 Alabama Win 9-7 1518.3 Feb 16th Queen City Tune Up 2025
48 Maryland Loss 8-9 1440.66 Feb 16th Queen City Tune Up 2025
4 Carleton College Loss 6-13 1603.31 Mar 1st Smoky Mountain Invite 2025
2 Colorado** Loss 5-15 1632.88 Ignored Mar 1st Smoky Mountain Invite 2025
10 Oregon State Loss 9-13 1563.11 Mar 1st Smoky Mountain Invite 2025
20 Vermont Loss 7-13 1300.55 Mar 1st Smoky Mountain Invite 2025
19 Georgia Loss 4-15 1292.49 Mar 2nd Smoky Mountain Invite 2025
31 Minnesota Loss 6-15 1113.47 Mar 2nd Smoky Mountain Invite 2025
13 Texas Loss 9-15 1455.37 Mar 2nd Smoky Mountain Invite 2025
184 East Carolina Win 13-7 1447.24 Mar 29th Needle in a Ho Stack 2025
232 Georgia Southern** Win 13-3 1278.26 Ignored Mar 29th Needle in a Ho Stack 2025
199 North Carolina State-B** Win 11-3 1417.07 Ignored Mar 29th Needle in a Ho Stack 2025
96 Appalachian State Win 12-10 1512.55 Mar 30th Needle in a Ho Stack 2025
257 East Tennessee State** Win 15-0 1180.24 Ignored Mar 30th Needle in a Ho Stack 2025
94 Tennessee-Chattanooga Win 15-8 1844.37 Mar 30th Needle in a Ho Stack 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)