#43 Harvard (8-14)

avg: 1672.28  •  sd: 59.96  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
7 Carleton College-CUT Loss 12-14 1897.68 Feb 8th Florida Warm Up 2019
27 LSU Win 11-10 1902.74 Feb 8th Florida Warm Up 2019
80 Oklahoma Win 13-9 1870.53 Feb 8th Florida Warm Up 2019
17 Minnesota Loss 8-10 1688.38 Feb 9th Florida Warm Up 2019
72 Alabama-Huntsville Loss 8-9 1358.99 Feb 9th Florida Warm Up 2019
48 Kennesaw State Win 12-11 1771.49 Feb 9th Florida Warm Up 2019
73 Temple Win 12-3 2080.87 Feb 9th Florida Warm Up 2019
17 Minnesota Loss 7-11 1484.16 Feb 10th Florida Warm Up 2019
27 LSU Loss 10-15 1324.13 Feb 10th Florida Warm Up 2019
119 Clemson Win 12-6 1862.86 Mar 16th Tally Classic XIV
24 Auburn Loss 11-15 1415.61 Mar 16th Tally Classic XIV
79 Tulane Loss 11-12 1331.42 Mar 16th Tally Classic XIV
103 Georgia State Win 13-4 1948.38 Mar 16th Tally Classic XIV
36 Alabama Win 14-9 2197 Mar 17th Tally Classic XIV
61 Tennessee Loss 13-14 1429.19 Mar 17th Tally Classic XIV
7 Carleton College-CUT Loss 8-13 1622.48 Mar 30th Easterns 2019 Men
54 Virginia Tech Loss 9-13 1200.88 Mar 30th Easterns 2019 Men
22 Georgia Loss 10-13 1506.35 Mar 30th Easterns 2019 Men
1 North Carolina Loss 9-13 1813.36 Mar 30th Easterns 2019 Men
26 North Carolina-Wilmington Win 12-9 2126.34 Mar 31st Easterns 2019 Men
44 Virginia Loss 10-12 1433.29 Mar 31st Easterns 2019 Men
28 Northeastern Loss 10-11 1650.83 Mar 31st Easterns 2019 Men
**Blowout Eligible

FAQ

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
  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
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)