#92 Middlebury (14-8)

avg: 1304.95  •  sd: 79.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 Brown Loss 4-13 1269.14 Mar 16th Womens Centex 2024
99 Chicago Loss 9-10 1094.77 Mar 16th Womens Centex 2024
53 Texas Loss 7-13 1003.76 Mar 16th Womens Centex 2024
153 Texas State Win 11-8 1226.84 Mar 16th Womens Centex 2024
32 Central Florida Loss 2-15 1217.19 Mar 17th Womens Centex 2024
48 Colorado College Win 8-7 1738.36 Mar 17th Womens Centex 2024
18 Colorado State** Loss 5-13 1471.59 Ignored Mar 17th Womens Centex 2024
173 Bentley Win 9-0 1329.06 Mar 23rd New England Open 2024
109 Brandeis Win 7-6 1290.91 Mar 23rd New England Open 2024
57 Connecticut Loss 1-12 929.14 Mar 23rd New England Open 2024
216 Northeastern-B** Win 13-3 905.25 Ignored Mar 23rd New England Open 2024
173 Bentley Win 10-3 1329.06 Mar 24th New England Open 2024
126 Massachusetts Loss 6-9 624.38 Mar 24th New England Open 2024
87 Bates Win 6-5 1457.33 Apr 13th North New England D III Womens Conferences 2024
185 Bowdoin** Win 13-3 1185.58 Ignored Apr 13th North New England D III Womens Conferences 2024
- Colby** Win 14-1 600 Ignored Apr 13th North New England D III Womens Conferences 2024
145 Dartmouth Win 6-3 1452.8 Apr 13th North New England D III Womens Conferences 2024
185 Bowdoin** Win 13-4 1185.58 Ignored May 4th New England D III College Womens Regionals 2024
145 Dartmouth Win 13-5 1506.1 May 4th New England D III College Womens Regionals 2024
86 Williams Win 10-9 1458.84 May 4th New England D III College Womens Regionals 2024
73 Wellesley Loss 9-10 1266.76 May 4th New England D III College Womens Regionals 2024
109 Brandeis Win 9-3 1765.91 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)