#249 North Greenville (3-4)

avg: 769.37  •  sd: 97.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
347 Radford Win 11-6 924.97 Feb 17th Chucktown Throwdown XV
303 Charleston Win 11-4 1171.48 Feb 17th Chucktown Throwdown XV
272 Miami Loss 9-10 576.69 Feb 17th Chucktown Throwdown XV
122 Tennessee Loss 2-13 655.55 Feb 17th Chucktown Throwdown XV
273 Wake Forest Win 14-11 1015.01 Feb 18th Chucktown Throwdown XV
116 Appalachian State Loss 7-13 716.93 Feb 18th Chucktown Throwdown XV
193 Liberty Loss 0-13 366.5 Feb 18th Chucktown Throwdown XV
**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)