#265 Drexel (13-11)

avg: 816.74  •  sd: 55.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
223 Colby Loss 6-9 564.16 Feb 22nd Bring The Huckus 2025
335 Connecticut-B Win 8-6 859.4 Feb 22nd Bring The Huckus 2025
267 SUNY-Geneseo Win 8-6 1110.13 Feb 22nd Bring The Huckus 2025
383 West Chester-B Win 12-4 826.64 Feb 22nd Bring The Huckus 2025
353 Lehigh-B Win 11-2 1013.51 Feb 23rd Bring The Huckus 2025
383 West Chester-B Win 10-5 800.53 Feb 23rd Bring The Huckus 2025
267 SUNY-Geneseo Win 7-4 1305.8 Feb 23rd Bring The Huckus 2025
416 Denison** Win 13-0 196.58 Ignored Mar 1st Huckin in the Hills XI
178 Ohio Loss 6-8 866.25 Mar 1st Huckin in the Hills XI
334 Penn State-Behrend Win 8-3 1162.13 Mar 1st Huckin in the Hills XI
125 Butler Loss 8-13 855.46 Mar 2nd Huckin in the Hills XI
226 West Virginia Loss 8-9 838.48 Mar 2nd Huckin in the Hills XI
356 Cornell-B Win 11-9 648.59 Mar 15th Natalies Animal Rescue 2025
329 Villanova Win 11-9 826.09 Mar 15th Natalies Animal Rescue 2025
207 Towson Loss 2-13 439.2 Mar 15th Natalies Animal Rescue 2025
417 Siena** Win 13-4 187.6 Ignored Mar 15th Natalies Animal Rescue 2025
329 Villanova Win 12-7 1097.39 Mar 16th Natalies Animal Rescue 2025
207 Towson Loss 7-13 481.67 Mar 16th Natalies Animal Rescue 2025
113 West Chester Loss 5-13 792.64 Apr 12th East Penn D I Mens Conferences
329 Villanova Win 9-7 856.22 Apr 12th East Penn D I Mens Conferences
94 Lehigh** Loss 3-13 880.82 Ignored Apr 12th East Penn D I Mens Conferences
167 Pennsylvania Loss 6-9 791.02 Apr 12th East Penn D I Mens Conferences
74 Temple** Loss 4-11 1000.01 Ignored Apr 13th East Penn D I Mens Conferences
167 Pennsylvania Loss 3-11 609.59 Apr 13th East Penn D I Mens Conferences
**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)