#185 Union (Tennessee) (12-12)

avg: 1129.2  •  sd: 45.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
171 Alabama-Birmingham Loss 12-13 1067.55 Jan 25th T Town Throwdown XX
278 Jacksonville State Win 13-6 1378.21 Jan 25th T Town Throwdown XX
117 Mississippi State Loss 8-13 879.09 Jan 25th T Town Throwdown XX
98 Tennessee-Chattanooga Loss 10-13 1144.08 Jan 25th T Town Throwdown XX
339 Alabama-B Win 13-7 1091.33 Jan 26th T Town Throwdown XX
101 Berry Loss 11-13 1233.6 Jan 26th T Town Throwdown XX
98 Tennessee-Chattanooga Loss 3-15 872.22 Jan 26th T Town Throwdown XX
122 Clemson Loss 12-13 1236.6 Feb 8th Bulldog Brawl
56 Indiana Loss 9-12 1333.54 Feb 8th Bulldog Brawl
196 Kennesaw State Win 11-9 1331.86 Feb 8th Bulldog Brawl
151 Lipscomb Loss 11-13 1043.23 Feb 8th Bulldog Brawl
284 Harding Win 15-3 1361.43 Feb 9th Bulldog Brawl
303 Tennessee Tech Win 15-9 1187.27 Feb 9th Bulldog Brawl
190 Vanderbilt Loss 11-15 725.15 Feb 9th Bulldog Brawl
171 Alabama-Birmingham Loss 8-9 1067.55 Mar 22nd 2025 Annual Magic City Invite
319 Mississippi Win 13-3 1212.05 Mar 22nd 2025 Annual Magic City Invite
320 Mississippi State-B Win 13-4 1203.09 Mar 22nd 2025 Annual Magic City Invite
397 South Florida-B** Win 13-4 702.31 Ignored Mar 22nd 2025 Annual Magic City Invite
339 Alabama-B Win 15-5 1133.8 Mar 23rd 2025 Annual Magic City Invite
193 Miami (Florida) Loss 8-15 523.95 Mar 23rd 2025 Annual Magic City Invite
320 Mississippi State-B Win 11-4 1203.09 Mar 23rd 2025 Annual Magic City Invite
259 Florida Tech Win 15-3 1443.78 Apr 12th Southeast D III Mens Conferences 2025
101 Berry Loss 12-15 1161.95 Apr 13th Southeast D III Mens Conferences 2025
228 Embry-Riddle Win 15-14 1086.8 Apr 13th Southeast D III 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)