#98 Dartmouth (9-10)

avg: 1245.64  •  sd: 51.54  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
298 Mary Washington Win 11-6 910.48 Jan 27th Mid Atlantic Warm Up
123 Pennsylvania Win 10-7 1537.15 Jan 27th Mid Atlantic Warm Up
61 William & Mary Win 10-9 1557.01 Jan 27th Mid Atlantic Warm Up
111 SUNY-Binghamton Win 9-7 1471.06 Jan 27th Mid Atlantic Warm Up
73 Richmond Loss 11-12 1239.26 Jan 28th Mid Atlantic Warm Up
68 James Madison Loss 11-13 1148.05 Jan 28th Mid Atlantic Warm Up
67 Chicago Loss 8-8 1387.02 Mar 16th College Mens Centex Tier 1
14 Texas Loss 7-13 1379.1 Mar 16th College Mens Centex Tier 1
31 Middlebury Loss 8-13 1160.96 Mar 16th College Mens Centex Tier 1
121 Iowa State Loss 6-8 854.57 Mar 16th College Mens Centex Tier 1
139 LSU Loss 8-9 959.6 Mar 17th College Mens Centex Tier 1
70 Case Western Reserve Loss 12-13 1241.71 Mar 30th East Coast Invite 2024
169 Rutgers Win 9-8 1076.64 Mar 30th East Coast Invite 2024
107 Princeton Win 10-9 1333.6 Mar 30th East Coast Invite 2024
184 George Mason Win 10-8 1138.71 Mar 30th East Coast Invite 2024
154 Harvard Win 10-7 1412.85 Mar 31st East Coast Invite 2024
25 McGill Loss 12-13 1649.26 Mar 31st East Coast Invite 2024
107 Princeton Loss 9-11 959.4 Mar 31st East Coast Invite 2024
146 Yale Win 11-9 1309.26 Mar 31st East Coast Invite 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)