#245 West Virginia (8-6)

avg: 780.79  •  sd: 63.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
270 American Loss 9-10 582.3 Mar 3rd Huckin in the Hills 2018
362 Carnegie Mellon University-B Win 13-4 935.5 Mar 3rd Huckin in the Hills 2018
368 Edinboro Win 13-3 911.46 Mar 3rd Huckin in the Hills 2018
424 SUNY-Buffalo-B** Win 13-1 384.04 Ignored 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 8-13 651.99 Mar 4th Huckin in the Hills 2018
368 Edinboro Win 12-8 752.61 Mar 4th Huckin in the Hills 2018
430 Pennsylvania-Johnstown-B** Win 13-3 310.47 Ignored Mar 24th CWRUL Memorial 2018
288 Ohio Northern Win 8-6 931.86 Mar 24th CWRUL Memorial 2018
192 Cedarville Loss 10-13 639.58 Mar 24th CWRUL Memorial 2018
94 Kentucky Loss 8-13 866.5 Mar 25th CWRUL Memorial 2018
190 Northern Iowa Loss 6-12 396.47 Mar 25th CWRUL Memorial 2018
192 Cedarville Loss 4-5 842.72 Mar 25th CWRUL Memorial 2018
293 Trine Win 9-5 1132.79 Mar 25th CWRUL Memorial 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)