#223 High Point (11-6)

avg: 863.1  •  sd: 72.2  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
347 Radford Win 13-3 978.28 Jan 27th Joint Summit XXXIII College Open
11 Emory** Loss 6-15 1320.68 Ignored Jan 27th Joint Summit XXXIII College Open
98 Clemson Loss 5-15 738.04 Jan 27th Joint Summit XXXIII College Open
193 Liberty Loss 5-7 638.35 Feb 17th Chucktown Throwdown XV
282 Wingate Win 7-6 787.96 Feb 17th Chucktown Throwdown XV
273 Wake Forest Loss 3-7 101.68 Feb 17th Chucktown Throwdown XV
252 Western Carolina Win 8-7 887.3 Feb 17th Chucktown Throwdown XV
116 Appalachian State Loss 4-10 674.46 Feb 17th Chucktown Throwdown XV
122 Tennessee Loss 4-15 655.55 Feb 18th Chucktown Throwdown XV
303 Charleston Win 11-7 1038.37 Feb 18th Chucktown Throwdown XV
273 Wake Forest Win 11-10 826.68 Feb 18th Chucktown Throwdown XV
278 James Madison-B Win 13-7 1230.78 Mar 17th Oak Creek Invite 2018
395 North Carolina State -B** Win 13-1 720.68 Ignored Mar 17th Oak Creek Invite 2018
365 William & Mary-B Win 11-9 573.22 Mar 17th Oak Creek Invite 2018
285 Maryland-B Win 9-7 923.02 Mar 17th Oak Creek Invite 2018
238 Delaware-B Win 15-8 1359.08 Mar 18th Oak Creek Invite 2018
285 Maryland-B Win 14-10 1042.38 Mar 18th Oak Creek Invite 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)