#234 Penn State-B (13-7)

avg: 656.34  •  sd: 36.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
112 Bowdoin Loss 8-12 763.42 Mar 1st Garden State 2025
279 Brown-B Win 7-5 794.31 Mar 1st Garden State 2025
217 Haverford Loss 7-8 616.57 Mar 1st Garden State 2025
151 Rhode Island Loss 2-5 451.19 Mar 1st Garden State 2025
279 Brown-B Win 9-6 884.74 Mar 2nd Garden State 2025
151 Rhode Island Loss 7-10 661.53 Mar 2nd Garden State 2025
231 Salisbury Loss 2-3 559.63 Mar 2nd Garden State 2025
305 Northeastern-C Win 9-8 483.84 Mar 22nd Jersey Devil 2025
372 West Chester-B Win 13-7 480.11 Mar 22nd Jersey Devil 2025
324 Villanova Win 9-6 678.63 Mar 22nd Jersey Devil 2025
366 Dartmouth-B** Win 13-3 532.46 Ignored Mar 23rd Jersey Devil 2025
349 Lehigh-B Win 13-3 714.06 Mar 23rd Jersey Devil 2025
305 Northeastern-C Win 15-12 659.33 Mar 23rd Jersey Devil 2025
347 Army Win 13-3 718.29 Mar 29th Northeast Classic 2025
235 College of New Jersey Win 11-10 780.99 Mar 29th Northeast Classic 2025
217 Haverford Win 10-8 1004.24 Mar 29th Northeast Classic 2025
336 SUNY-Cortland Win 13-4 791.65 Mar 29th Northeast Classic 2025
176 Ithaca Loss 7-13 374.53 Mar 30th Northeast Classic 2025
275 SUNY-Geneseo Win 13-12 606.03 Mar 30th Northeast Classic 2025
246 Skidmore Loss 11-13 401.62 Mar 30th Northeast Classic 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)