#61 Emory (11-11)

avg: 1576.98  •  sd: 52.9  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
43 Alabama-Huntsville Loss 8-9 1578.13 Jan 28th T Town Throwdown1
238 Spring Hill Win 13-8 1259.43 Jan 28th T Town Throwdown1
131 Georgia State Win 12-4 1842.58 Jan 28th T Town Throwdown1
259 Jacksonville State** Win 11-3 1296.22 Ignored Jan 28th T Town Throwdown1
85 Alabama Win 11-10 1571.99 Jan 29th T Town Throwdown1
89 Mississippi State Win 11-9 1683.26 Jan 29th T Town Throwdown1
108 Vanderbilt Win 13-11 1556.46 Jan 29th T Town Throwdown1
42 Grand Canyon Loss 8-9 1580.28 Feb 18th President’s Day Invite
10 California-Santa Cruz Win 12-11 2214.74 Feb 18th President’s Day Invite
9 Oregon Loss 9-13 1718.57 Feb 18th President’s Day Invite
6 Colorado Loss 7-13 1640.04 Feb 19th President’s Day Invite
46 Western Washington Win 9-7 1967.87 Feb 19th President’s Day Invite
17 Washington Loss 10-12 1752.02 Feb 19th President’s Day Invite
47 Colorado State Win 12-10 1885.34 Feb 20th President’s Day Invite
18 California Loss 9-13 1543 Feb 20th President’s Day Invite
59 Cincinnati Loss 5-6 1453.71 Apr 1st Huck Finn1
49 Notre Dame Loss 3-5 1224.69 Apr 1st Huck Finn1
108 Vanderbilt Win 6-5 1452.62 Apr 1st Huck Finn1
22 Washington University Loss 5-9 1376.26 Apr 1st Huck Finn1
118 Marquette Win 10-8 1563.44 Apr 2nd Huck Finn1
64 St. Olaf Loss 8-11 1202.39 Apr 2nd Huck Finn1
75 Grinnell Loss 8-9 1361.77 Apr 2nd Huck Finn1
**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)