#113 Ithaca (6-6)

avg: 905.27  •  sd: 61.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
200 Princeton** Win 13-2 664.53 Ignored Feb 25th Bring The Huckus1
187 Dickinson** Win 7-2 851.91 Ignored Feb 25th Bring The Huckus1
154 Syracuse Win 10-3 1182.2 Feb 25th Bring The Huckus1
94 Boston College Loss 6-7 913.5 Feb 26th Bring The Huckus1
102 Dartmouth Win 7-6 1098.04 Feb 26th Bring The Huckus1
111 Lehigh Loss 7-8 793.2 Feb 26th Bring The Huckus1
63 Haverford/Bryn Mawr Loss 2-13 676.27 Mar 25th Garden State1
89 Columbia Loss 5-7 750.51 Mar 25th Garden State1
111 Lehigh Loss 5-7 590.06 Mar 25th Garden State1
161 Skidmore Win 7-1 1105.55 Mar 26th Garden State1
61 Vermont-B Loss 5-8 832.94 Mar 26th Garden State1
140 Rochester Win 7-2 1280.32 Mar 26th Garden State1
**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)