#281 Navy (4-6)

avg: 665.4  •  sd: 74.77  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
166 MIT Loss 6-9 647.94 Mar 3rd Atlantic City 7 2018
369 Brown-B Win 10-8 572.82 Mar 3rd Atlantic City 7 2018
389 Hofstra Win 13-3 760.95 Mar 4th Atlantic City 7 2018
361 Saint Joseph's University Win 13-8 836.89 Mar 4th Atlantic City 7 2018
302 Salisbury Win 13-8 1070.69 Mar 4th Atlantic City 7 2018
178 Shippensburg Loss 7-13 462.32 Mar 4th Atlantic City 7 2018
172 Colby Loss 6-12 456.46 Mar 4th Atlantic City 7 2018
102 Richmond Loss 8-15 762.07 Mar 31st DIII EastUR Powered by SAVAGE
83 Middlebury Loss 9-15 891.13 Mar 31st DIII EastUR Powered by SAVAGE
266 Swarthmore Loss 9-15 215.77 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)