#175 Army (7-2)

avg: 1032.37  •  sd: 62.28  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
218 Rensselaer Polytech Win 15-14 1003.7 Feb 24th Bring the Huckus 9
266 Swarthmore Win 12-11 856.25 Feb 24th Bring the Huckus 9
161 Boston University Loss 10-11 962.79 Feb 24th Bring the Huckus 9
198 Wesleyan Win 13-12 1073.52 Feb 24th Bring the Huckus 9
357 Skidmore Win 13-7 904.58 Mar 4th 4th Annual West Point Classic
- Central Connecticut State Win 12-5 1230.31 Mar 4th 4th Annual West Point Classic
218 Rensselaer Polytech Win 10-5 1452.59 Mar 4th 4th Annual West Point Classic
319 Marist Win 15-9 1025.76 Mar 4th 4th Annual West Point Classic
198 Wesleyan Loss 10-11 823.52 Mar 4th 4th Annual West Point Classic
**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)