#282 Wingate (3-11)

avg: 662.96  •  sd: 68.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 Kennesaw State Loss 6-13 858.01 Jan 27th Clutch Classic 2018
140 Florida Tech Loss 9-12 822.1 Jan 27th Clutch Classic 2018
272 Miami Loss 8-13 205.53 Jan 27th Clutch Classic 2018
381 Georgia Gwinnett Win 9-7 494.57 Jan 27th Clutch Classic 2018
418 Kennesaw State-B** Win 15-4 445.2 Ignored Jan 28th Clutch Classic 2018
224 Georgia Southern Loss 10-11 736.24 Jan 28th Clutch Classic 2018
75 Tennessee-Chattanooga** Loss 5-15 815.67 Ignored Jan 28th Clutch Classic 2018
223 High Point Loss 6-7 738.1 Feb 17th Chucktown Throwdown XV
193 Liberty Loss 5-6 841.5 Feb 17th Chucktown Throwdown XV
116 Appalachian State** Loss 2-11 674.46 Ignored Feb 17th Chucktown Throwdown XV
252 Western Carolina Loss 8-9 637.3 Feb 17th Chucktown Throwdown XV
273 Wake Forest Win 10-6 1197.84 Feb 17th Chucktown Throwdown XV
272 Miami Loss 10-11 576.69 Feb 18th Chucktown Throwdown XV
252 Western Carolina Loss 6-11 215.61 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)