#111 SUNY-Binghamton (7-12)

avg: 1191.72  •  sd: 64.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
70 Case Western Reserve Win 10-6 1862.87 Jan 27th Mid Atlantic Warm Up
96 Connecticut Win 11-9 1498.61 Jan 27th Mid Atlantic Warm Up
98 Dartmouth Loss 7-9 966.3 Jan 27th Mid Atlantic Warm Up
208 Virginia Commonwealth Win 12-8 1226.53 Jan 27th Mid Atlantic Warm Up
142 Boston University Loss 8-11 703.1 Jan 28th Mid Atlantic Warm Up
85 Carnegie Mellon Loss 9-13 899.76 Jan 28th Mid Atlantic Warm Up
158 Kennesaw State Win 12-6 1590.03 Feb 24th Easterns Qualifier 2024
29 South Carolina Loss 9-13 1265.34 Feb 24th Easterns Qualifier 2024
61 William & Mary Loss 6-13 832.01 Feb 24th Easterns Qualifier 2024
60 Temple Loss 11-12 1310.02 Feb 24th Easterns Qualifier 2024
158 Kennesaw State Loss 6-7 885.72 Feb 25th Easterns Qualifier 2024
34 Ohio State Loss 5-12 1041.87 Feb 25th Easterns Qualifier 2024
169 Rutgers Win 8-5 1405.24 Feb 25th Easterns Qualifier 2024
58 Maryland Loss 12-15 1142.47 Feb 25th Easterns Qualifier 2024
236 MIT Win 11-8 1046.18 Mar 23rd Carousel City Classic 2024
32 Ottawa Loss 10-12 1415.54 Mar 23rd Carousel City Classic 2024
46 Williams Loss 8-13 1028.8 Mar 23rd Carousel City Classic 2024
32 Ottawa Loss 8-12 1212.51 Mar 24th Carousel City Classic 2024
113 Syracuse Win 11-10 1313.84 Mar 24th Carousel City Classic 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)