#270 American (8-11)

avg: 707.3  •  sd: 48.22  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
169 Johns Hopkins Loss 2-13 460.68 Feb 24th Oak Creek Challenge 2018
182 NYU Loss 8-12 557.6 Feb 24th Oak Creek Challenge 2018
179 SUNY-Binghamton Loss 8-13 521.91 Feb 24th Oak Creek Challenge 2018
173 Oberlin Loss 9-13 615.18 Feb 25th Oak Creek Challenge 2018
318 Towson Win 15-7 1110.45 Feb 25th Oak Creek Challenge 2018
243 Rowan Loss 9-15 268.53 Feb 25th Oak Creek Challenge 2018
147 Akron Loss 7-9 868.81 Mar 3rd Huckin in the Hills 2018
424 SUNY-Buffalo-B** Win 13-2 384.04 Ignored Mar 3rd Huckin in the Hills 2018
362 Carnegie Mellon University-B Win 13-4 935.5 Mar 3rd Huckin in the Hills 2018
245 West Virginia Win 10-9 905.79 Mar 3rd Huckin in the Hills 2018
386 Indiana (Pennsylvania) Win 13-3 797.47 Mar 4th Huckin in the Hills 2018
147 Akron Loss 8-15 583.34 Mar 4th Huckin in the Hills 2018
368 Edinboro Win 10-7 701.13 Mar 4th Huckin in the Hills 2018
113 Lehigh Loss 7-13 726.55 Mar 24th Atlantic Coast Open 2018
151 George Mason Loss 9-12 771.47 Mar 24th Atlantic Coast Open 2018
86 Duke** Loss 5-13 798.98 Ignored Mar 24th Atlantic Coast Open 2018
174 East Carolina Loss 6-11 485.73 Mar 25th Atlantic Coast Open 2018
368 Edinboro Win 13-8 807.62 Mar 25th Atlantic Coast Open 2018
243 Rowan Win 5-4 909.01 Mar 25th Atlantic Coast Open 2018
**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)