#316 Rensselaer Polytech (5-5)

avg: 340.76  •  sd: 78.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
- Central Connecticut State Loss 1-10 84.52 Mar 26th Late Blooming Bids
- Southern Connecticut State Win 9-4 207.26 Mar 26th Late Blooming Bids
- New Haven Win 7-5 332.45 Mar 26th Late Blooming Bids
157 Yale** Loss 1-12 525.16 Ignored Mar 26th Late Blooming Bids
322 Vassar Win 7-0 903.68 Mar 26th Late Blooming Bids
187 SUNY-Geneseo** Loss 3-11 395.97 Ignored Apr 2nd Northeast Salvage
290 SUNY-Oneonta Loss 6-9 110.26 Apr 2nd Northeast Salvage
352 Siena Win 8-4 588.78 Apr 2nd Northeast Salvage
357 SUNY-Albany-B Win 10-1 486.36 Apr 2nd Northeast Salvage
245 SUNY-Albany Loss 5-12 149.92 Apr 2nd Northeast Salvage
**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)