#234 Haverford (14-12)

avg: 999.52  •  sd: 62.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
139 Army Loss 6-13 748.35 Feb 24th Bring The Huckus 2024
225 Colby Loss 8-10 773.02 Feb 24th Bring The Huckus 2024
108 Vermont-B Loss 1-13 875.03 Feb 24th Bring The Huckus 2024
267 SUNY-Geneseo Loss 7-9 592.15 Feb 24th Bring The Huckus 2024
267 SUNY-Geneseo Win 13-12 996.49 Feb 25th Bring The Huckus 2024
108 Vermont-B Loss 7-13 917.5 Feb 25th Bring The Huckus 2024
319 Edinboro Win 7-4 1150.32 Mar 23rd Garden State 2024
310 Stevens Tech Win 7-6 803.83 Mar 23rd Garden State 2024
272 Rowan Win 9-6 1275.35 Mar 23rd Garden State 2024
407 West Chester-B** Win 11-1 467.56 Ignored Mar 24th Garden State 2024
382 Lehigh-B** Win 9-2 887.63 Ignored Mar 24th Garden State 2024
170 Villanova Win 10-9 1376.96 Mar 24th Garden State 2024
212 West Virginia Win 13-6 1679.5 Mar 24th Garden State 2024
150 West Chester Loss 8-13 818.37 Mar 24th Garden State 2024
407 West Chester-B** Win 13-2 467.56 Ignored Mar 30th Layout Pigout 2024
150 West Chester Win 11-8 1680.13 Mar 30th Layout Pigout 2024
376 SUNY-Fredonia** Win 13-5 908.89 Ignored Mar 30th Layout Pigout 2024
382 Lehigh-B** Win 11-3 887.63 Ignored Apr 13th East Penn D III Mens Conferences 2024
171 Scranton Loss 2-15 638.19 Apr 13th East Penn D III Mens Conferences 2024
318 Swarthmore Win 9-3 1255.47 Apr 13th East Penn D III Mens Conferences 2024
68 Franciscan** Loss 3-13 1060.48 Ignored Apr 27th Ohio Valley D III College Mens Regionals 2024
174 Grove City Loss 8-12 783.84 Apr 27th Ohio Valley D III College Mens Regionals 2024
198 Messiah Loss 4-13 528.99 Apr 27th Ohio Valley D III College Mens Regionals 2024
168 Kenyon Loss 9-13 833.79 Apr 27th Ohio Valley D III College Mens Regionals 2024
173 Xavier Loss 9-10 1108.43 Apr 28th Ohio Valley D III College Mens Regionals 2024
318 Swarthmore Win 9-5 1184.53 Apr 28th Ohio Valley D III College Mens Regionals 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)