#94 Ithaca (14-10)

avg: 1094.08  •  sd: 45.89  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
36 Haverford/Bryn Mawr Loss 5-8 1165.45 Feb 22nd Bring The Huckus 2025
97 Lehigh Win 8-7 1215.28 Feb 22nd Bring The Huckus 2025
39 Wesleyan Loss 3-13 987.33 Feb 22nd Bring The Huckus 2025
162 SUNY-Geneseo Win 8-1 1246.31 Feb 22nd Bring The Huckus 2025
138 George Washington Win 10-5 1365.2 Feb 23rd Bring The Huckus 2025
162 SUNY-Geneseo Loss 6-7 521.31 Feb 23rd Bring The Huckus 2025
104 Amherst Loss 7-8 915.3 Mar 29th Northeast Classic 2025
86 Vermont-B Win 10-9 1264.79 Mar 29th Northeast Classic 2025
56 Rochester Loss 4-11 821.74 Mar 29th Northeast Classic 2025
112 SUNY-Binghamton Win 8-5 1423.78 Mar 29th Northeast Classic 2025
182 Vermont-C Win 13-4 1125.76 Mar 30th Northeast Classic 2025
85 Wellesley Win 10-9 1268.84 Mar 30th Northeast Classic 2025
56 Rochester Loss 7-9 1142.41 Mar 30th Northeast Classic 2025
121 Colgate Win 9-5 1456.49 Apr 12th Western NY D III Womens Conferences 2025
153 Hamilton Win 13-1 1281.19 Apr 12th Western NY D III Womens Conferences 2025
162 SUNY-Geneseo Win 8-5 1099.91 Apr 12th Western NY D III Womens Conferences 2025
56 Rochester Loss 3-9 821.74 Apr 12th Western NY D III Womens Conferences 2025
217 Rensselaer Polytech** Win 15-2 853 Ignored Apr 26th Metro East D III College Womens Regionals 2025
204 Vassar** Win 11-2 987.96 Ignored Apr 26th Metro East D III College Womens Regionals 2025
39 Wesleyan Loss 5-10 1013.43 Apr 26th Metro East D III College Womens Regionals 2025
56 Rochester Loss 1-15 821.74 Apr 26th Metro East D III College Womens Regionals 2025
162 SUNY-Geneseo Win 8-4 1211.12 Apr 27th Metro East D III College Womens Regionals 2025
184 Skidmore Win 8-2 1121.4 Apr 27th Metro East D III College Womens Regionals 2025
56 Rochester Loss 2-8 821.74 Apr 27th Metro East D III 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)