#103 Berry (19-3)

avg: 1444.9  •  sd: 57.39  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
102 Alabama Win 13-12 1572.3 Jan 25th T Town Throwdown XX
236 Illinois State Win 13-7 1465.58 Jan 25th T Town Throwdown XX
394 Tennessee-Chattanooga -B** Win 13-1 723.18 Ignored Jan 25th T Town Throwdown XX
130 LSU Win 13-9 1741.72 Jan 25th T Town Throwdown XX
102 Alabama Win 15-12 1747.8 Jan 26th T Town Throwdown XX
288 Harding** Win 13-2 1345.34 Ignored Jan 26th T Town Throwdown XX
186 Union (Tennessee) Win 13-11 1341.43 Jan 26th T Town Throwdown XX
130 LSU Win 15-8 1887.96 Jan 26th T Town Throwdown XX
306 Tennessee Tech Win 13-7 1211.71 Feb 8th Bulldog Brawl
325 Mississippi State-B** Win 13-2 1186.73 Ignored Feb 8th Bulldog Brawl
87 Missouri S&T Loss 11-13 1262.24 Feb 8th Bulldog Brawl
190 Vanderbilt Win 13-9 1507.78 Feb 8th Bulldog Brawl
118 Mississippi State Win 15-13 1571.42 Feb 9th Bulldog Brawl
95 Southern Illinois-Edwardsville Loss 14-15 1345.96 Feb 9th Bulldog Brawl
152 Lipscomb Win 13-10 1581.5 Feb 9th Bulldog Brawl
184 Georgia State Loss 14-15 991.96 Mar 15th Tally Classic XIX
229 Harvard Win 15-9 1454.51 Mar 15th Tally Classic XIX
177 Minnesota-Duluth Win 11-10 1280.35 Mar 15th Tally Classic XIX
364 Georgia College** Win 15-6 976.99 Ignored Apr 12th Southeast D III Mens Conferences 2025
228 Embry-Riddle Win 15-8 1510.77 Apr 12th Southeast D III Mens Conferences 2025
261 Florida Tech Win 15-7 1430.03 Apr 13th Southeast D III Mens Conferences 2025
186 Union (Tennessee) Win 15-12 1413.08 Apr 13th Southeast D III Mens Conferences 2025
**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)