#136 Mississippi State (10-10)

avg: 927.02  •  sd: 55.5  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
64 LSU Loss 3-11 741.78 Jan 18th TTown Throwdown 2020 Open
61 Florida Loss 9-11 1103.35 Jan 18th TTown Throwdown 2020 Open
47 Illinois State Loss 5-11 848.24 Jan 18th TTown Throwdown 2020 Open
103 Vanderbilt Loss 7-8 968.1 Jan 18th TTown Throwdown 2020 Open
157 South Florida Win 9-8 957.67 Jan 18th TTown Throwdown 2020 Open
53 Alabama-Huntsville Loss 7-11 936.65 Jan 19th TTown Throwdown 2020 Open
103 Vanderbilt Win 15-8 1657.9 Jan 19th TTown Throwdown 2020 Open
116 Georgia State Win 10-8 1291.87 Feb 8th Chattanooga Classic 2020
188 Saint Louis Win 10-7 1012.89 Feb 8th Chattanooga Classic 2020
72 Kentucky Loss 3-10 707.31 Feb 8th Chattanooga Classic 2020
111 Missouri Loss 8-10 784.1 Feb 8th Chattanooga Classic 2020
188 Saint Louis Win 11-9 872.43 Feb 9th Chattanooga Classic 2020
66 Tennessee-Chattanooga Loss 8-12 888.49 Feb 9th Chattanooga Classic 2020
140 Chicago Loss 6-9 496.39 Feb 22nd Music City Tune Up 2020
141 Michigan State Loss 11-13 680.96 Feb 22nd Music City Tune Up 2020
183 Samford Win 9-7 922.45 Feb 22nd Music City Tune Up 2020
250 Belmont University** Win 12-3 760.38 Ignored Feb 22nd Music City Tune Up 2020
183 Samford Win 12-8 1084.26 Feb 23rd Music City Tune Up 2020
208 Toledo Win 10-9 645.5 Feb 23rd Music City Tune Up 2020
179 Union (Tennessee) Win 12-8 1106.56 Feb 23rd Music City Tune Up 2020
**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)