#190 Vanderbilt (9-10)

avg: 1089.21  •  sd: 63.89  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
103 Berry Loss 9-13 1026.33 Feb 8th Bulldog Brawl
124 Clemson Loss 7-11 877.09 Feb 8th Bulldog Brawl
197 Kennesaw State Loss 7-12 543.66 Feb 8th Bulldog Brawl
95 Southern Illinois-Edwardsville Loss 9-13 1052.39 Feb 8th Bulldog Brawl
325 Mississippi State-B Win 9-3 1186.73 Feb 9th Bulldog Brawl
306 Tennessee Tech Win 15-7 1254.18 Feb 9th Bulldog Brawl
186 Union (Tennessee) Win 15-11 1493.75 Feb 9th Bulldog Brawl
127 Butler Win 13-10 1663.33 Mar 29th Corny Classic College 2025
353 Purdue-B Win 13-6 1020.2 Mar 29th Corny Classic College 2025
297 Loyola-Chicago Win 10-6 1197.34 Mar 29th Corny Classic College 2025
167 Dayton Win 10-9 1316.95 Mar 30th Corny Classic College 2025
59 Illinois Loss 7-9 1370.86 Mar 30th Corny Classic College 2025
169 Michigan Tech Win 6-5 1313.6 Mar 30th Corny Classic College 2025
67 Auburn Loss 5-13 1021.8 Apr 12th Gulf Coast D I Mens Conferences 2025
50 Tulane** Loss 4-13 1108.09 Ignored Apr 12th Gulf Coast D I Mens Conferences 2025
130 LSU Loss 7-12 802.64 Apr 12th Gulf Coast D I Mens Conferences 2025
172 Alabama-Birmingham Loss 7-12 654.36 Apr 13th Gulf Coast D I Mens Conferences 2025
321 Mississippi Win 13-5 1195.55 Apr 13th Gulf Coast D I Mens Conferences 2025
118 Mississippi State Loss 4-13 757.25 Apr 13th Gulf Coast D I 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)