#176 Ithaca (11-8)

avg: 932.06  •  sd: 74  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
174 Delaware Win 11-9 1187.21 Feb 22nd Bring The Huckus 2025
217 Haverford Win 12-5 1341.57 Feb 22nd Bring The Huckus 2025
328 Hofstra** Win 13-1 840.55 Ignored Feb 22nd Bring The Huckus 2025
223 Colby Win 13-6 1306.39 Feb 23rd Bring The Huckus 2025
171 Dickinson Loss 10-13 625.06 Feb 23rd Bring The Huckus 2025
217 Haverford Loss 5-9 212.51 Feb 23rd Bring The Huckus 2025
128 SUNY-Binghamton Loss 4-13 528.07 Mar 22nd Salt City Classic
73 Williams Loss 7-11 918.2 Mar 22nd Salt City Classic
80 Rochester Loss 7-13 782.96 Mar 22nd Salt City Classic
301 Rensselaer Polytech Win 13-2 976.06 Mar 22nd Salt City Classic
153 Carleton University Loss 11-13 816.72 Mar 23rd Salt City Classic
301 Rensselaer Polytech Win 15-6 976.06 Mar 23rd Salt City Classic
246 Skidmore Win 9-6 1049.02 Mar 29th Northeast Classic 2025
202 Vassar Win 10-4 1393.45 Mar 29th Northeast Classic 2025
275 SUNY-Geneseo Win 9-5 1010.09 Mar 29th Northeast Classic 2025
226 SUNY-Albany Loss 7-9 417.74 Mar 29th Northeast Classic 2025
217 Haverford Win 13-7 1299.11 Mar 30th Northeast Classic 2025
234 Penn State-B Win 13-7 1213.87 Mar 30th Northeast Classic 2025
202 Vassar Loss 10-11 668.45 Mar 30th Northeast Classic 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)