#90 MIT (10-13)

avg: 1306.4  •  sd: 73.5  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
55 Georgia Tech Loss 6-13 948.96 Feb 24th Commonwealth Cup Weekend 2 2024
57 Connecticut Loss 4-10 929.14 Feb 24th Commonwealth Cup Weekend 2 2024
65 James Madison Loss 5-7 1134.35 Feb 24th Commonwealth Cup Weekend 2 2024
16 Pennsylvania** Loss 0-13 1499.13 Ignored Feb 24th Commonwealth Cup Weekend 2 2024
89 Virginia Tech Loss 6-8 1006.42 Feb 25th Commonwealth Cup Weekend 2 2024
121 Temple Win 8-6 1378.99 Feb 25th Commonwealth Cup Weekend 2 2024
221 LSU** Win 13-0 856.64 Ignored Mar 16th Womens Centex 2024
93 Rice Loss 9-13 862.7 Mar 16th Womens Centex 2024
66 Trinity Loss 9-12 1110.75 Mar 16th Womens Centex 2024
206 Texas-B** Win 13-4 986.61 Ignored Mar 16th Womens Centex 2024
153 Texas State Loss 8-11 495.62 Mar 17th Womens Centex 2024
206 Texas-B** Win 8-0 986.61 Ignored Mar 17th Womens Centex 2024
149 Texas A&M Win 11-5 1477.51 Mar 17th Womens Centex 2024
122 Boston College Win 15-5 1675.76 Apr 20th Metro Boston D I Womens Conferences 2024
78 Harvard Win 9-7 1644.24 Apr 20th Metro Boston D I Womens Conferences 2024
8 Tufts Loss 6-13 1755.7 Apr 20th Metro Boston D I Womens Conferences 2024
21 Northeastern** Loss 5-13 1359.88 Ignored Apr 20th Metro Boston D I Womens Conferences 2024
122 Boston College Win 13-3 1675.76 May 4th New England D I College Womens Regionals 2024
78 Harvard Win 11-8 1730.51 May 4th New England D I College Womens Regionals 2024
8 Tufts** Loss 3-15 1755.7 Ignored May 4th New England D I College Womens Regionals 2024
181 Vermont-C** Win 11-4 1210.94 Ignored May 4th New England D I College Womens Regionals 2024
27 Brown Loss 7-15 1269.14 May 5th New England D I College Womens Regionals 2024
68 Vermont-B Loss 7-11 970.54 May 5th New England D I 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)