#234 Haverford (3-7)

avg: 807.5  •  sd: 73.61  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
177 Virginia Commonwealth Win 11-10 1147.23 Feb 3rd Mid Atlantic Warmup 2018
51 Ohio State Loss 7-13 980.16 Feb 3rd Mid Atlantic Warmup 2018
161 Boston University Loss 6-13 487.79 Feb 3rd Mid Atlantic Warmup 2018
102 Richmond Loss 5-13 726.88 Feb 3rd Mid Atlantic Warmup 2018
145 Drexel Loss 9-11 900.1 Feb 4th Mid Atlantic Warmup 2018
86 Duke Loss 9-12 1053.61 Feb 4th Mid Atlantic Warmup 2018
193 Liberty Win 11-10 1091.5 Feb 4th Mid Atlantic Warmup 2018
196 SUNY-Stony Brook Loss 8-13 464.73 Mar 31st Garden State 8
393 Shenandoah** Win 13-3 728.97 Ignored Mar 31st Garden State 8
204 SUNY-Geneseo Loss 10-12 687.39 Mar 31st Garden State 8
**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)