#122 Tennessee (13-3)

avg: 1255.55  •  sd: 81.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
149 Davidson Win 10-9 1265.86 Jan 27th Joint Summit XXXIII College Open
116 Appalachian State Win 10-6 1770.62 Jan 27th Joint Summit XXXIII College Open
303 Charleston Win 11-6 1118.18 Jan 27th Joint Summit XXXIII College Open
185 Georgia-B Win 11-2 1583.95 Jan 27th Joint Summit XXXIII College Open
347 Radford Win 11-7 845.17 Feb 17th Chucktown Throwdown XV
249 North Greenville Win 13-2 1369.37 Feb 17th Chucktown Throwdown XV
303 Charleston Win 12-6 1150.79 Feb 17th Chucktown Throwdown XV
272 Miami Win 13-1 1301.69 Feb 17th Chucktown Throwdown XV
223 High Point Win 15-4 1463.1 Feb 18th Chucktown Throwdown XV
193 Liberty Win 10-7 1356.16 Feb 18th Chucktown Throwdown XV
116 Appalachian State Loss 7-13 716.93 Feb 18th Chucktown Throwdown XV
181 Ball State Win 12-3 1610.76 Mar 24th Indy Invite College Men 2018
95 Purdue Loss 6-13 752.93 Mar 24th Indy Invite College Men 2018
233 Missouri Win 13-3 1412.4 Mar 24th Indy Invite College Men 2018
124 Indiana Win 11-7 1693.16 Mar 25th Indy Invite College Men 2018
73 Michigan State Loss 8-15 854.74 Mar 25th Indy Invite College Men 2018
**Blowout Eligible

FAQ

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
  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
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)