#17 Tufts (13-6)

avg: 1897.01  •  sd: 68.31  •  top 16/20: 73.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
35 Chicago Loss 12-13 1529.33 Feb 15th Queen City Tune Up 2025
52 William & Mary Win 13-12 1676.49 Feb 15th Queen City Tune Up 2025
48 Maryland Win 13-8 2061.82 Feb 15th Queen City Tune Up 2025
35 Chicago Win 11-0 2254.33 Feb 16th Queen City Tune Up 2025
49 North Carolina State Win 10-4 2164.8 Feb 16th Queen City Tune Up 2025
66 Dartmouth Win 13-5 2052.25 Mar 15th Mens Centex 2025
46 Middlebury Win 9-7 1877.13 Mar 15th Mens Centex 2025
13 Texas Loss 5-10 1396.96 Mar 15th Mens Centex 2025
40 Wisconsin Win 11-8 1993.62 Mar 15th Mens Centex 2025
35 Chicago Win 13-8 2150.49 Mar 16th Mens Centex 2025
57 Illinois Win 14-11 1828.75 Mar 16th Mens Centex 2025
13 Texas Win 13-10 2299 Mar 16th Mens Centex 2025
36 Michigan Loss 9-13 1234.21 Mar 29th Easterns 2025
31 Minnesota Win 10-8 1976.14 Mar 29th Easterns 2025
3 North Carolina Loss 9-13 1787.55 Mar 29th Easterns 2025
20 Vermont Win 10-9 1983.08 Mar 29th Easterns 2025
14 California Win 13-9 2387.8 Mar 30th Easterns 2025
4 Carleton College Loss 9-13 1784.74 Mar 30th Easterns 2025
1 Massachusetts Loss 8-15 1694.29 Mar 30th Easterns 2025
**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)