#71 Penn State-B (12-1)

avg: 1366.46  •  sd: 97.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
315 Bentley Win 9-4 881.98 Mar 2nd Philly Special 2024
103 Bowdoin Win 9-8 1341 Mar 2nd Philly Special 2024
225 Brandeis** Win 13-2 1330.24 Ignored Mar 3rd Philly Special 2024
127 College of New Jersey Win 11-9 1393.63 Mar 3rd Philly Special 2024
277 Stevens Tech** Win 13-3 1097.45 Ignored Mar 3rd Philly Special 2024
213 SUNY-Albany Win 13-7 1323.67 Mar 3rd Philly Special 2024
120 Army Loss 9-11 911.37 Mar 16th Free Tournament
352 Rensselaer Polytech** Win 13-5 616.9 Ignored Mar 16th Free Tournament
130 Towson Win 13-7 1674.36 Mar 16th Free Tournament
214 Scranton Win 13-7 1320.42 Mar 16th Free Tournament
120 Army Win 15-6 1760.57 Mar 17th Free Tournament
313 Dartmouth-B** Win 15-5 884.25 Ignored Mar 17th Free Tournament
130 Towson Win 12-8 1557.98 Mar 17th Free Tournament
**Blowout Eligible

FAQ

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)