#212 SUNY-Albany (10-13)

avg: 1023.44  •  sd: 54.41  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
51 Cornell** Loss 1-10 1123.89 Ignored Mar 1st Oak Creek Challenge 2025
112 Liberty Loss 7-10 1015.92 Mar 1st Oak Creek Challenge 2025
113 West Chester Loss 3-12 792.64 Mar 1st Oak Creek Challenge 2025
112 Liberty Loss 4-11 805.59 Mar 2nd Oak Creek Challenge 2025
97 SUNY-Buffalo Loss 7-10 1084.97 Mar 2nd Oak Creek Challenge 2025
328 SUNY-Cortland Win 13-11 810.65 Mar 2nd Oak Creek Challenge 2025
160 Ithaca Win 9-7 1510.04 Mar 29th Northeast Classic 2025
235 Skidmore Win 9-8 1052 Mar 29th Northeast Classic 2025
267 SUNY-Geneseo Win 11-8 1175.25 Mar 29th Northeast Classic 2025
192 Vassar Win 8-6 1393.56 Mar 29th Northeast Classic 2025
244 College of New Jersey Loss 7-13 345.07 Mar 30th Northeast Classic 2025
235 Skidmore Loss 8-12 485.85 Mar 30th Northeast Classic 2025
267 SUNY-Geneseo Win 11-8 1175.25 Mar 30th Northeast Classic 2025
325 Central Connecticut State Win 10-5 1171.61 Apr 12th Hudson Valley D I Mens Conferences 2025
123 Connecticut Loss 8-10 1097.77 Apr 12th Hudson Valley D I Mens Conferences 2025
380 Southern Connecticut State Win 13-7 802.89 Apr 12th Hudson Valley D I Mens Conferences 2025
92 Yale Loss 8-13 994.06 Apr 12th Hudson Valley D I Mens Conferences 2025
325 Central Connecticut State Win 14-8 1133.75 Apr 13th Hudson Valley D I Mens Conferences 2025
92 Yale Loss 8-12 1049.07 Apr 13th Hudson Valley D I Mens Conferences 2025
108 Columbia Loss 7-15 843.45 Apr 26th Metro East D I College Mens Regionals 2025
123 Connecticut Loss 13-14 1235.44 Apr 26th Metro East D I College Mens Regionals 2025
331 Hofstra Win 15-8 1139.26 Apr 26th Metro East D I College Mens Regionals 2025
115 RIT Loss 14-15 1259.22 Apr 26th Metro East D I College Mens 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)