#160 Syracuse (10-15)

avg: 663.44  •  sd: 53.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
223 Dickinson Win 10-1 805.15 Feb 22nd Bring The Huckus 2025
138 George Washington Loss 4-5 666.3 Feb 22nd Bring The Huckus 2025
118 Swarthmore Loss 6-8 638.73 Feb 22nd Bring The Huckus 2025
184 Skidmore Win 10-0 1121.4 Feb 22nd Bring The Huckus 2025
223 Dickinson Win 10-2 805.15 Feb 23rd Bring The Huckus 2025
118 Swarthmore Loss 7-8 814.23 Feb 23rd Bring The Huckus 2025
39 Wesleyan** Loss 1-13 987.33 Ignored Feb 23rd Bring The Huckus 2025
224 SUNY-Cortland Win 8-7 328.25 Mar 29th Northeast Classic 2025
162 SUNY-Geneseo Loss 4-7 150.15 Mar 29th Northeast Classic 2025
184 Skidmore Loss 7-8 396.4 Mar 29th Northeast Classic 2025
204 Vassar Win 10-5 961.86 Mar 29th Northeast Classic 2025
224 SUNY-Cortland Win 13-3 803.25 Mar 30th Northeast Classic 2025
204 Vassar Loss 7-8 262.96 Mar 30th Northeast Classic 2025
34 Cornell** Loss 2-15 1041.42 Ignored Apr 12th Western NY D I Womens Conferences 2025
234 Cornell-B Win 15-3 720.39 Apr 12th Western NY D I Womens Conferences 2025
175 RIT Win 12-6 1161.11 Apr 12th Western NY D I Womens Conferences 2025
112 SUNY-Binghamton Loss 9-13 551.61 Apr 13th Western NY D I Womens Conferences 2025
90 SUNY-Buffalo Loss 6-11 569.64 Apr 13th Western NY D I Womens Conferences 2025
76 Columbia Loss 1-13 619.45 Apr 26th Metro East D I College Womens Regionals 2025
34 Cornell** Loss 4-13 1041.42 Ignored Apr 26th Metro East D I College Womens Regionals 2025
91 NYU Loss 1-13 511.74 Apr 26th Metro East D I College Womens Regionals 2025
137 Rutgers Win 8-7 917.75 Apr 26th Metro East D I College Womens Regionals 2025
64 Connecticut Loss 5-11 719.82 Apr 27th Metro East D I College Womens Regionals 2025
185 SUNY-Stony Brook Win 5-4 643.93 Apr 27th Metro East D I College Womens Regionals 2025
137 Rutgers Loss 8-10 530.08 Apr 27th Metro East D I College Womens Regionals 2025
**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)