#64 Appalachian State (6-10)

avg: 1274.41  •  sd: 74.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
30 South Carolina Loss 4-12 1060.8 Jan 21st Carolina Kickoff womens and nonbinary
144 North Carolina-B Win 9-5 1189.83 Jan 21st Carolina Kickoff womens and nonbinary
21 North Carolina State Loss 4-14 1156.31 Jan 21st Carolina Kickoff womens and nonbinary
95 Temple Win 8-3 1629.11 Jan 22nd Carolina Kickoff womens and nonbinary
28 Duke Loss 5-10 1108.14 Jan 22nd Carolina Kickoff womens and nonbinary
7 Carleton College** Loss 4-15 1678.35 Ignored Feb 11th Queen City Tune Up1
38 Chicago Loss 8-11 1201.61 Feb 11th Queen City Tune Up1
60 Ohio Loss 4-8 734.53 Feb 11th Queen City Tune Up1
14 Virginia Loss 6-13 1330.66 Feb 11th Queen City Tune Up1
47 Florida Win 7-6 1592.9 Feb 12th Queen City Tune Up1
46 Florida State Loss 7-8 1348.72 Feb 12th Queen City Tune Up1
116 Cedarville Win 12-5 1490.53 Mar 25th Needle in a Ho Stack2
- Berry Win 13-8 1441.5 Mar 25th Needle in a Ho Stack2
77 Tennessee-Chattanooga Win 9-3 1779.39 Mar 25th Needle in a Ho Stack2
16 Middlebury Loss 4-13 1235.84 Mar 25th Needle in a Ho Stack2
56 Tennessee Loss 4-12 740.42 Mar 26th Needle in a Ho Stack2
**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)