#126 Elon (5-5)

avg: 1212.12  •  sd: 103.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
48 Dartmouth Loss 6-13 965.43 Feb 3rd Mid Atlantic Warmup 2018
107 Rutgers Win 11-9 1563.57 Feb 3rd Mid Atlantic Warmup 2018
84 Virginia Win 13-10 1730.28 Feb 3rd Mid Atlantic Warmup 2018
78 Georgetown Loss 10-12 1176.95 Feb 3rd Mid Atlantic Warmup 2018
145 Drexel Win 15-10 1602.91 Feb 4th Mid Atlantic Warmup 2018
86 Duke Win 13-12 1523.98 Feb 4th Mid Atlantic Warmup 2018
109 Williams Loss 8-10 1033.54 Feb 4th Mid Atlantic Warmup 2018
54 Mary Washington Loss 11-15 1143.07 Mar 31st DIII EastUR Powered by SAVAGE
178 Shippensburg Loss 11-15 638.69 Mar 31st DIII EastUR Powered by SAVAGE
311 Messiah Win 15-7 1151.19 Mar 31st DIII EastUR Powered by SAVAGE
**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)