#224 Haverford (8-6)

avg: 813.68  •  sd: 60.5  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
227 Temple-B Win 8-6 1100.1 Feb 25th Bring The Huckus1
305 SUNY-Binghamton-B Win 12-6 1011.27 Feb 25th Bring The Huckus1
239 Stevens Tech Win 7-6 886.75 Feb 25th Bring The Huckus1
357 SUNY-Albany-B** Win 13-2 486.36 Ignored Feb 25th Bring The Huckus1
263 Swarthmore Loss 8-9 556.29 Feb 26th Bring The Huckus1
245 SUNY-Albany Loss 8-9 624.92 Feb 26th Bring The Huckus1
171 Brandeis Loss 8-12 625.7 Mar 25th Layout Pigout 2023
114 Kenyon Loss 4-15 711.91 Mar 25th Layout Pigout 2023
122 Williams Loss 1-12 693.13 Mar 25th Layout Pigout 2023
298 Hofstra Win 13-3 1072.11 Apr 1st Fuego2
307 West Chester-B Win 7-2 1004.29 Apr 1st Fuego2
285 Villanova Win 12-5 1149.69 Apr 1st Fuego2
282 New Hampshire Win 6-5 692.47 Apr 2nd Fuego2
160 Wesleyan Loss 5-13 512.09 Apr 2nd Fuego2
**Blowout Eligible


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)