#201 American (10-10)

avg: 830.17  •  sd: 60.66  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
110 Villanova Loss 5-13 576.86 Jan 25th Mid Atlantic Warmup 2020
195 George Mason Loss 11-12 716.41 Jan 25th Mid Atlantic Warmup 2020
177 Mary Washington Win 10-9 1051.51 Jan 25th Mid Atlantic Warmup 2020
78 Boston University Loss 9-13 906.92 Jan 25th Mid Atlantic Warmup 2020
118 Navy Loss 9-14 660.47 Jan 26th Mid Atlantic Warmup 2020
244 Christopher Newport Win 13-10 995.22 Jan 26th Mid Atlantic Warmup 2020
125 SUNY-Binghamton Loss 2-11 523.97 Jan 26th Mid Atlantic Warmup 2020
207 Drexel Win 9-8 941.19 Feb 22nd Oak Creek Challenge 2020
263 NYU Win 10-9 673.42 Feb 22nd Oak Creek Challenge 2020
278 Salisbury Win 13-7 1018.15 Feb 22nd Oak Creek Challenge 2020
177 Mary Washington Win 13-10 1254.65 Feb 22nd Oak Creek Challenge 2020
66 Georgetown Loss 6-15 792.84 Feb 23rd Oak Creek Challenge 2020
127 Brandeis Win 11-9 1371.06 Feb 23rd Oak Creek Challenge 2020
103 Princeton Loss 6-14 605.44 Feb 23rd Oak Creek Challenge 2020
213 Catholic Win 7-4 1285.51 Feb 29th Lorton Hears a Huck
195 George Mason Loss 5-9 312.35 Feb 29th Lorton Hears a Huck
248 New Hampshire Loss 10-11 520.58 Feb 29th Lorton Hears a Huck
309 Maryland-Baltimore County Win 8-2 868.83 Feb 29th Lorton Hears a Huck
123 Virginia Commonwealth University Loss 8-12 687.24 Mar 1st Lorton Hears a Huck
309 Maryland-Baltimore County Win 13-7 826.36 Mar 1st Lorton Hears a Huck
**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)