#21 Tufts (9-12)

avg: 1828.7  •  sd: 61.64  •  top 16/20: 39.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
12 Alabama-Huntsville Win 13-12 2118.67 Feb 2nd Florida Warm Up 2024
17 Brigham Young Loss 10-13 1547.3 Feb 2nd Florida Warm Up 2024
185 South Florida** Win 13-3 1466.3 Ignored Feb 2nd Florida Warm Up 2024
14 Texas Loss 8-13 1440.48 Feb 3rd Florida Warm Up 2024
52 Virginia Tech Win 13-4 2075.52 Feb 3rd Florida Warm Up 2024
8 Vermont Loss 11-15 1657.43 Feb 3rd Florida Warm Up 2024
56 Emory Win 15-4 2047.58 Feb 4th Florida Warm Up 2024
2 Georgia Loss 7-13 1715.28 Mar 2nd Smoky Mountain Invite 2024
4 Massachusetts Loss 13-15 2020.78 Mar 2nd Smoky Mountain Invite 2024
11 Minnesota Loss 10-13 1674.1 Mar 2nd Smoky Mountain Invite 2024
13 North Carolina State Loss 9-13 1528.03 Mar 2nd Smoky Mountain Invite 2024
15 California Win 15-11 2305.38 Mar 3rd Smoky Mountain Invite 2024
13 North Carolina State Loss 10-15 1493 Mar 3rd Smoky Mountain Invite 2024
14 Texas Loss 11-15 1555.47 Mar 3rd Smoky Mountain Invite 2024
2 Georgia Loss 7-13 1715.28 Mar 30th Easterns 2024
28 North Carolina-Wilmington Win 13-8 2230.66 Mar 30th Easterns 2024
7 Pittsburgh Loss 10-12 1855.02 Mar 30th Easterns 2024
33 Wisconsin Win 13-12 1770.5 Mar 30th Easterns 2024
42 Michigan Loss 14-15 1441.42 Mar 31st Easterns 2024
16 Penn State Win 12-11 2046.23 Mar 31st Easterns 2024
29 South Carolina Win 15-9 2199.39 Mar 31st Easterns 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)