#101 Berry (19-3)

avg: 1462.45  •  sd: 57.49  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
100 Alabama Win 13-12 1590.6 Jan 25th T Town Throwdown XX
238 Illinois State Win 13-7 1482.06 Jan 25th T Town Throwdown XX
393 Tennessee-Chattanooga -B** Win 13-1 738.31 Ignored Jan 25th T Town Throwdown XX
128 LSU Win 13-9 1759.58 Jan 25th T Town Throwdown XX
100 Alabama Win 15-12 1766.09 Jan 26th T Town Throwdown XX
284 Harding** Win 13-2 1361.43 Ignored Jan 26th T Town Throwdown XX
185 Union (Tennessee) Win 13-11 1358.04 Jan 26th T Town Throwdown XX
128 LSU Win 15-8 1905.82 Jan 26th T Town Throwdown XX
303 Tennessee Tech Win 13-7 1229.32 Feb 8th Bulldog Brawl
320 Mississippi State-B** Win 13-2 1203.09 Ignored Feb 8th Bulldog Brawl
87 Missouri S&T Loss 11-13 1278.74 Feb 8th Bulldog Brawl
190 Vanderbilt Win 13-9 1524.88 Feb 8th Bulldog Brawl
117 Mississippi State Win 15-13 1589.43 Feb 9th Bulldog Brawl
93 Southern Illinois-Edwardsville Loss 14-15 1362.63 Feb 9th Bulldog Brawl
151 Lipscomb Win 13-10 1600.22 Feb 9th Bulldog Brawl
184 Georgia State Loss 14-15 1009.51 Mar 15th Tally Classic XIX
230 Harvard Win 15-9 1472.79 Mar 15th Tally Classic XIX
174 Minnesota-Duluth Win 11-10 1305.64 Mar 15th Tally Classic XIX
359 Georgia College** Win 15-6 992.73 Ignored Apr 12th Southeast D III Mens Conferences 2025
228 Embry-Riddle Win 15-8 1526.61 Apr 12th Southeast D III Mens Conferences 2025
259 Florida Tech Win 15-7 1443.78 Apr 13th Southeast D III Mens Conferences 2025
185 Union (Tennessee) Win 15-12 1429.7 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)