#397 SUNY-Albany-B (3-9)

avg: 92.79  •  sd: 85.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
343 Connecticut-B Loss 0-6 -50.67 Mar 2nd Philly Special 2024
401 Siena Win 9-3 639.98 Mar 2nd Philly Special 2024
359 Bentley Loss 3-15 -146.04 Mar 3rd Philly Special 2024
343 Connecticut-B Loss 4-15 -50.67 Mar 3rd Philly Special 2024
401 Siena Win 15-8 604.79 Mar 3rd Philly Special 2024
283 Hofstra** Loss 3-11 209.27 Ignored Mar 30th Northeast Classic 2024
218 Middlebury-B** Loss 3-13 454.71 Ignored Mar 30th Northeast Classic 2024
378 SUNY-Buffalo-B Loss 12-13 176.69 Mar 30th Northeast Classic 2024
411 RIT-B Win 12-6 313.31 Apr 13th Metro East Dev Mens Conferences 2024
331 Rutgers-B Loss 3-15 -3.72 Apr 13th Metro East Dev Mens Conferences 2024
345 Cornell-B Loss 3-11 -65.9 Apr 14th Metro East Dev Mens Conferences 2024
378 SUNY-Buffalo-B Loss 3-15 -298.31 Apr 14th Metro East Dev Mens Conferences 2024
**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)