#176 Syracuse (7-10)

avg: 1047.79  •  sd: 62.05  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
348 Cornell-B** Win 13-1 676.83 Ignored Mar 4th No Sleep Till Brooklyn 2023
298 Hofstra Win 13-2 1072.11 Mar 4th No Sleep Till Brooklyn 2023
169 NYU Loss 8-9 958.8 Mar 4th No Sleep Till Brooklyn 2023
190 MIT Loss 9-11 742.36 Mar 5th No Sleep Till Brooklyn 2023
158 Tufts-B Win 11-9 1368.02 Mar 5th No Sleep Till Brooklyn 2023
153 Columbia Loss 9-12 795.26 Mar 5th No Sleep Till Brooklyn 2023
31 Ottawa** Loss 4-15 1229.69 Ignored Mar 25th Carousel City Classic
63 Rutgers Loss 5-15 968.79 Mar 25th Carousel City Classic
82 Binghamton Loss 4-15 861.54 Mar 25th Carousel City Classic
134 Carnegie Mellon Loss 7-10 846.65 Mar 26th Carousel City Classic
153 Columbia Loss 10-11 1015.62 Mar 26th Carousel City Classic
177 Rochester Loss 7-11 573.29 Mar 26th Carousel City Classic
290 SUNY-Oneonta Win 13-2 1128.83 Apr 2nd Northeast Salvage
205 SUNY-Cortland Win 13-5 1528.38 Apr 2nd Northeast Salvage
83 RIT Loss 9-10 1325.36 Apr 2nd Northeast Salvage
245 SUNY-Albany Win 10-6 1246.08 Apr 2nd Northeast Salvage
283 Skidmore Win 13-3 1159.1 Apr 2nd Northeast Salvage
**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)