#154 Syracuse (5-4)

avg: 582.2  •  sd: 98.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
113 Ithaca Loss 3-10 305.27 Feb 25th Bring The Huckus1
187 Dickinson Win 13-0 851.91 Feb 25th Bring The Huckus1
200 Princeton Win 8-2 664.53 Feb 25th Bring The Huckus1
156 Connecticut College Win 8-5 1009.02 Feb 26th Bring The Huckus1
111 Lehigh Loss 2-9 318.2 Feb 26th Bring The Huckus1
133 New Hampshire Win 7-5 1076.69 Feb 26th Bring The Huckus1
76 Bates Loss 0-13 581.37 Apr 1st Northeast Classic2
- RIT Win 9-7 437.26 Apr 1st Northeast Classic2
161 Skidmore Loss 8-10 242.88 Apr 2nd Northeast Classic2
**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)