#178 Army (7-8)

avg: 1059.73  •  sd: 52.48  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
129 Marist Loss 12-15 971.31 Feb 2nd 5th Annual West Point Classic
129 Marist Loss 13-15 1057.62 Mar 3rd 5th Annual West Point Classic
304 Rhode Island Win 5-2 1244.91 Mar 23rd Spring Awakening 8
281 Skidmore Win 8-1 1349.6 Mar 23rd Spring Awakening 8
134 Boston University Loss 11-12 1119.39 Mar 23rd Spring Awakening 8
343 Dickinson Win 13-1 1112.3 Mar 23rd Spring Awakening 8
223 Rensselaer Polytech Win 11-9 1165.82 Mar 24th Spring Awakening 8
96 Bowdoin Loss 10-12 1129.69 Mar 24th Spring Awakening 8
193 Colgate Win 12-11 1136.84 Mar 24th Spring Awakening 8
315 Muhlenberg Win 13-6 1206.97 Mar 30th Layout Pigout 2019
228 Swarthmore Win 13-11 1143.7 Mar 30th Layout Pigout 2019
95 Bates College Loss 6-12 790.46 Mar 30th Layout Pigout 2019
107 Franciscan Loss 10-13 997.38 Mar 30th Layout Pigout 2019
95 Bates College Loss 11-13 1140.93 Mar 31st Layout Pigout 2019
176 Bentley Loss 5-13 465.27 Mar 31st Layout Pigout 2019
**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)