#383 West Chester-B (7-17)

avg: 226.64  •  sd: 76.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
335 Connecticut-B Win 10-8 821.57 Feb 22nd Bring The Huckus 2025
265 Drexel Loss 4-12 216.74 Feb 22nd Bring The Huckus 2025
267 SUNY-Geneseo Loss 3-13 209.64 Feb 22nd Bring The Huckus 2025
265 Drexel Loss 5-10 242.84 Feb 23rd Bring The Huckus 2025
313 Rowan Win 4-2 1124.92 Feb 23rd Bring The Huckus 2025
406 Dartmouth-B Loss 2-8 -645.26 Mar 22nd Jersey Devil 2025
353 Lehigh-B Loss 9-10 288.51 Mar 22nd Jersey Devil 2025
388 New Jersey Tech Loss 7-9 -90.44 Mar 22nd Jersey Devil 2025
294 Northeastern-C Loss 8-10 465.47 Mar 22nd Jersey Devil 2025
210 Penn State-B Loss 7-13 470 Mar 22nd Jersey Devil 2025
329 Villanova Win 8-7 701.88 Mar 23rd Jersey Devil 2025
336 Pennsylvania Western Loss 6-12 -22.35 Mar 29th Northeast Classic 2025
313 Rowan Loss 8-9 503.76 Mar 29th Northeast Classic 2025
417 Siena** Win 10-4 187.6 Ignored Mar 29th Northeast Classic 2025
381 SUNY-Binghamton-B Loss 6-13 -355.35 Mar 29th Northeast Classic 2025
356 Cornell-B Loss 10-11 274.38 Mar 30th Northeast Classic 2025
389 Stevens Tech Loss 9-12 -157.91 Mar 30th Northeast Classic 2025
419 SUNY-Albany-B** Win 13-0 98.62 Ignored Mar 30th Northeast Classic 2025
412 Carnegie Mellon-B Win 12-8 146.98 Apr 12th Ohio Valley Dev Mens Conferences 2025
326 Case Western Reserve-B Loss 7-13 27.51 Apr 12th Ohio Valley Dev Mens Conferences 2025
321 Cincinnati -B Loss 7-13 44.35 Apr 12th Ohio Valley Dev Mens Conferences 2025
210 Penn State-B** Loss 0-13 427.54 Ignored Apr 12th Ohio Valley Dev Mens Conferences 2025
412 Carnegie Mellon-B Win 15-6 305.82 Apr 13th Ohio Valley Dev Mens Conferences 2025
141 Pittsburgh-B Loss 6-13 698.32 Apr 13th Ohio Valley Dev Mens Conferences 2025
**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)