#146 SUNY-Binghamton (8-18)

avg: 1283.61  •  sd: 57.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 Auburn Loss 3-10 1040.44 Feb 22nd Easterns Qualifier 2025
59 James Madison Loss 6-13 1069.73 Feb 22nd Easterns Qualifier 2025
79 Notre Dame Loss 10-13 1243.56 Feb 22nd Easterns Qualifier 2025
35 South Carolina Loss 9-12 1488.84 Feb 22nd Easterns Qualifier 2025
106 Appalachian State Loss 6-10 953.33 Feb 23rd Easterns Qualifier 2025
196 Kennesaw State Win 14-9 1556.52 Feb 23rd Easterns Qualifier 2025
99 Syracuse Loss 12-13 1343.46 Feb 23rd Easterns Qualifier 2025
160 Ithaca Win 13-4 1830.7 Mar 22nd Salt City Classic
347 Rensselaer Polytech** Win 13-4 1061.28 Ignored Mar 22nd Salt City Classic
81 Rochester Loss 7-8 1430.12 Mar 22nd Salt City Classic
76 Williams Loss 6-11 1045.83 Mar 22nd Salt City Classic
182 Carleton University Loss 13-14 1028.19 Mar 23rd Salt City Classic
99 Syracuse Loss 8-13 972.3 Mar 23rd Salt City Classic
88 Carnegie Mellon Loss 9-10 1381.57 Mar 29th East Coast Invite 2025
149 Rutgers Win 8-7 1403.67 Mar 29th East Coast Invite 2025
92 Yale Loss 8-13 994.06 Mar 29th East Coast Invite 2025
207 Towson Win 15-4 1639.2 Mar 29th East Coast Invite 2025
161 Delaware Win 9-7 1509.69 Mar 30th East Coast Invite 2025
92 Yale Win 13-8 1986.38 Mar 30th East Coast Invite 2025
113 West Chester Loss 5-9 863.58 Mar 30th East Coast Invite 2025
182 Carleton University Loss 11-13 924.35 Apr 12th Western NY D I Mens Conferences 2025
51 Cornell Loss 10-13 1395.75 Apr 12th Western NY D I Mens Conferences 2025
30 Ottawa Loss 5-15 1272.88 Apr 12th Western NY D I Mens Conferences 2025
99 Syracuse Loss 7-13 910.92 Apr 12th Western NY D I Mens Conferences 2025
182 Carleton University Win 12-9 1498.55 Apr 13th Western NY D I Mens Conferences 2025
99 Syracuse Loss 9-11 1219.25 Apr 13th Western NY D I Mens Conferences 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)