#120 Army (9-3)

avg: 1160.57  •  sd: 99.45  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
197 Haverford Win 13-6 1436.18 Feb 24th Bring The Huckus 2024
207 Colby Win 10-5 1369.12 Feb 24th Bring The Huckus 2024
100 Vermont-B Loss 11-12 1110.55 Feb 24th Bring The Huckus 2024
247 SUNY-Geneseo Win 9-4 1237.5 Feb 24th Bring The Huckus 2024
187 Salisbury Loss 11-15 482.51 Feb 25th Bring The Huckus 2024
214 Scranton Win 13-10 1091.03 Mar 16th Free Tournament
130 Towson Win 10-9 1241.83 Mar 16th Free Tournament
352 Rensselaer Polytech** Win 10-4 616.9 Ignored Mar 16th Free Tournament
71 Penn State-B Win 11-9 1615.67 Mar 16th Free Tournament
331 New Jersey Tech** Win 15-3 763.75 Ignored Mar 17th Free Tournament
214 Scranton Win 13-8 1259.05 Mar 17th Free Tournament
71 Penn State-B Loss 6-15 766.46 Mar 17th Free Tournament
**Blowout Eligible


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
  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
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)