#54 Georgia Tech (13-6)

avg: 1352.86  •  sd: 116.86  •  top 16/20: 0.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
100 Alabama-Huntsville Win 8-7 1107.72 Jan 28th T Town Throwdown1
220 Emory-B** Win 13-0 -94.27 Ignored Jan 28th T Town Throwdown1
132 Emory Win 8-3 1362.73 Jan 28th T Town Throwdown1
100 Alabama-Huntsville Win 13-2 1582.72 Jan 29th T Town Throwdown1
132 Emory Win 13-2 1362.73 Jan 29th T Town Throwdown1
136 Alabama** Win 11-0 1322.36 Ignored Feb 11th 2023 TOTS The Only Tenn I See
88 Kentucky Win 7-2 1680.73 Feb 11th 2023 TOTS The Only Tenn I See
114 Union (Tennessee) Loss 2-7 299.39 Feb 11th 2023 TOTS The Only Tenn I See
179 LSU** Win 11-0 947.6 Ignored Feb 11th 2023 TOTS The Only Tenn I See
136 Alabama** Win 13-2 1322.36 Ignored Feb 12th 2023 TOTS The Only Tenn I See
56 Tennessee Loss 3-10 740.42 Feb 12th 2023 TOTS The Only Tenn I See
88 Kentucky Loss 6-8 780.24 Feb 12th 2023 TOTS The Only Tenn I See
82 Central Florida Win 10-6 1626.28 Mar 18th Womens Centex1
70 Northwestern Win 10-9 1363.77 Mar 18th Womens Centex1
23 Texas-Dallas Loss 2-13 1139.5 Mar 18th Womens Centex1
45 Washington University Win 12-10 1717.38 Mar 18th Womens Centex1
47 Florida Loss 10-11 1342.9 Mar 19th Womens Centex1
48 Texas Win 13-8 1956.11 Mar 19th Womens Centex1
23 Texas-Dallas Loss 9-11 1490.29 Mar 19th Womens Centex1
**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)