#118 Franciscan (7-0)

avg: 959.89  •  sd: 168.89  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
217 Ohio** Win 13-3 797.26 Feb 3rd Huckin in the Hills X
242 Kent State** Win 13-4 371.45 Ignored Feb 3rd Huckin in the Hills X
215 West Virginia** Win 13-5 846.83 Feb 3rd Huckin in the Hills X
183 Towson Win 12-2 1090.11 Feb 3rd Huckin in the Hills X
249 Ohio-B** Win 15-1 -28.99 Ignored Feb 4th Huckin in the Hills X
183 Towson Win 15-7 1090.11 Feb 4th Huckin in the Hills X
156 Dayton Win 15-12 980.39 Feb 4th Huckin in the Hills X
**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)