#3 Tufts (18-0)

avg: 2840.06  •  sd: 56.27  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
55 Appalachian State** Win 13-0 2299.38 Ignored Feb 15th Queen City Tune Up 2025
31 Pittsburgh** Win 13-2 2614.51 Ignored Feb 15th Queen City Tune Up 2025
20 Virginia Win 13-4 2868.93 Feb 15th Queen City Tune Up 2025
2 Carleton College Win 9-7 3277.91 Feb 16th Queen City Tune Up 2025
9 North Carolina Win 11-6 3069.33 Feb 16th Queen City Tune Up 2025
18 Brigham Young Win 13-7 2850.85 Mar 1st Stanford Invite 2025 Womens
17 California-Santa Barbara Win 12-8 2745.01 Mar 1st Stanford Invite 2025 Womens
46 Texas-Dallas** Win 13-2 2422.28 Ignored Mar 1st Stanford Invite 2025 Womens
14 Cal Poly-SLO Win 9-7 2722.43 Mar 2nd Stanford Invite 2025 Womens
12 California-Santa Cruz Win 12-9 2822.47 Mar 2nd Stanford Invite 2025 Womens
13 Stanford Win 11-8 2840.69 Mar 2nd Stanford Invite 2025 Womens
32 Georgetown** Win 15-5 2607.01 Ignored Mar 29th East Coast Invite 2025
21 Ohio State Win 15-7 2866.62 Mar 29th East Coast Invite 2025
23 Pennsylvania Win 13-6 2845.85 Mar 29th East Coast Invite 2025
20 Virginia Win 15-7 2868.93 Mar 29th East Coast Invite 2025
36 MIT Win 15-9 2414.7 Mar 30th East Coast Invite 2025
19 Notre Dame Win 13-4 2886.09 Mar 30th East Coast Invite 2025
21 Ohio State Win 15-1 2866.62 Mar 30th East Coast Invite 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)