#82 Rochester (16-6)

avg: 1344.48  •  sd: 53.86  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
144 Skidmore Win 9-3 1512.28 Feb 24th Bring The Huckus 2024
220 Dickinson** Win 13-0 873.22 Ignored Feb 24th Bring The Huckus 2024
108 West Chester Loss 5-9 650.41 Feb 24th Bring The Huckus 2024
168 Swarthmore Win 8-3 1377.22 Feb 24th Bring The Huckus 2024
52 Haverford/Bryn Mawr Loss 5-15 969.87 Feb 25th Bring The Huckus 2024
94 Lehigh Win 8-7 1400.7 Feb 25th Bring The Huckus 2024
191 Syracuse** Win 13-2 1152.54 Ignored Feb 25th Bring The Huckus 2024
108 West Chester Win 10-8 1442.14 Feb 25th Bring The Huckus 2024
68 Vermont-B Loss 8-9 1312.43 Mar 30th Northeast Classic 2024
81 Wesleyan Win 10-9 1478.32 Mar 30th Northeast Classic 2024
73 Wellesley Loss 6-7 1266.76 Mar 30th Northeast Classic 2024
52 Haverford/Bryn Mawr Loss 3-10 969.87 Mar 31st Northeast Classic 2024
105 Mount Holyoke Win 8-5 1642.21 Mar 31st Northeast Classic 2024
101 Rhode Island Win 8-7 1338.69 Mar 31st Northeast Classic 2024
73 Wellesley Win 8-6 1692.25 Mar 31st Northeast Classic 2024
157 Hamilton Win 12-2 1437.7 Apr 20th Western NY D III Womens Conferences 2024
142 Ithaca Win 8-5 1373.8 Apr 20th Western NY D III Womens Conferences 2024
193 SUNY-Geneseo** Win 11-2 1138.01 Ignored Apr 20th Western NY D III Womens Conferences 2024
157 Hamilton Win 15-5 1437.7 Apr 27th Metro East D III College Womens Regionals 2024
142 Ithaca Win 11-8 1285.8 Apr 27th Metro East D III College Womens Regionals 2024
144 Skidmore Win 14-5 1512.28 Apr 28th Metro East D III College Womens Regionals 2024
81 Wesleyan Loss 10-11 1228.32 Apr 28th Metro East D III College Womens Regionals 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)