#259 Drexel (12-6)

avg: 578.78  •  sd: 64.85  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
223 Colby Loss 6-9 287.82 Feb 22nd Bring The Huckus 2025
333 Connecticut-B Win 8-6 518.36 Feb 22nd Bring The Huckus 2025
275 SUNY-Geneseo Win 8-6 781.52 Feb 22nd Bring The Huckus 2025
372 West Chester-B** Win 12-4 522.58 Ignored Feb 22nd Bring The Huckus 2025
349 Lehigh-B Win 11-2 714.06 Feb 23rd Bring The Huckus 2025
372 West Chester-B Win 10-5 496.48 Feb 23rd Bring The Huckus 2025
275 SUNY-Geneseo Win 7-4 977.19 Feb 23rd Bring The Huckus 2025
391 Denison** Win 13-0 -49.72 Ignored Mar 1st Huckin in the Hills XI
164 Ohio Loss 6-8 676.37 Mar 1st Huckin in the Hills XI
320 Penn State-Behrend Win 8-3 873.35 Mar 1st Huckin in the Hills XI
147 Butler Loss 8-13 558.79 Mar 2nd Huckin in the Hills XI
230 West Virginia Loss 8-9 560.38 Mar 2nd Huckin in the Hills XI
330 Cornell-B Win 11-9 481.79 Mar 15th Natalies Animal Rescue 2025
324 Villanova Win 11-9 509.27 Mar 15th Natalies Animal Rescue 2025
177 Towson Loss 2-13 325.57 Mar 15th Natalies Animal Rescue 2025
390 Siena** Win 13-4 -46.18 Ignored Mar 15th Natalies Animal Rescue 2025
324 Villanova Win 12-7 780.57 Mar 16th Natalies Animal Rescue 2025
177 Towson Loss 7-13 368.03 Mar 16th Natalies Animal Rescue 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)