#145 Dartmouth (8-12)

avg: 906.1  •  sd: 70.86  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
130 Boston University Loss 6-7 895.93 Feb 24th Bring The Huckus 2024
94 Lehigh Loss 5-8 822.09 Feb 24th Bring The Huckus 2024
193 SUNY-Geneseo Win 7-6 663.01 Feb 24th Bring The Huckus 2024
52 Haverford/Bryn Mawr Loss 5-11 969.87 Feb 24th Bring The Huckus 2024
193 SUNY-Geneseo Win 8-6 838.5 Feb 25th Bring The Huckus 2024
52 Haverford/Bryn Mawr Loss 8-11 1204.26 Feb 25th Bring The Huckus 2024
108 West Chester Win 9-6 1598.04 Feb 25th Bring The Huckus 2024
138 Liberty Loss 4-9 346.76 Mar 23rd Rodeo 2024
86 Williams Loss 4-11 733.84 Mar 23rd Rodeo 2024
62 Duke Loss 7-9 1204.72 Mar 23rd Rodeo 2024
151 North Carolina-B Win 9-7 1147.29 Mar 23rd Rodeo 2024
151 North Carolina-B Win 11-6 1414.65 Mar 24th Rodeo 2024
138 Liberty Loss 7-8 821.76 Mar 24th Rodeo 2024
87 Bates Win 7-6 1457.33 Apr 13th North New England D III Womens Conferences 2024
185 Bowdoin Win 5-4 710.58 Apr 13th North New England D III Womens Conferences 2024
92 Middlebury Loss 3-6 758.26 Apr 13th North New England D III Womens Conferences 2024
175 Amherst Loss 8-9 570.49 May 4th New England D III College Womens Regionals 2024
185 Bowdoin Win 11-8 951.19 May 4th New England D III College Womens Regionals 2024
92 Middlebury Loss 5-13 704.95 May 4th New England D III College Womens Regionals 2024
173 Bentley Loss 7-8 604.06 May 5th New England 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)