#99 Temple (11-11)

avg: 1197.41  •  sd: 108.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
18 Brown Loss 7-13 1188.62 Feb 3rd Florida Warm Up 2023
28 Georgia Tech Loss 5-11 1072.79 Feb 3rd Florida Warm Up 2023
112 Illinois Win 9-8 1249.34 Feb 4th Florida Warm Up 2023
204 South Florida Win 13-2 1323.38 Feb 4th Florida Warm Up 2023
29 Wisconsin Loss 9-13 1248.1 Feb 4th Florida Warm Up 2023
5 Vermont Loss 7-15 1410.71 Feb 4th Florida Warm Up 2023
89 Central Florida Loss 11-12 1121.55 Feb 5th Florida Warm Up 2023
106 Florida State Loss 9-10 1035.45 Feb 5th Florida Warm Up 2023
34 McGill Loss 8-10 1360.03 Feb 25th Easterns Qualifier 2023
51 James Madison Loss 11-13 1208.37 Feb 25th Easterns Qualifier 2023
17 South Carolina Loss 7-13 1216.34 Feb 25th Easterns Qualifier 2023
117 Georgia State Loss 7-11 643.68 Feb 25th Easterns Qualifier 2023
63 Cincinnati Loss 6-15 780.22 Feb 26th Easterns Qualifier 2023
106 Florida State Win 13-7 1717.98 Feb 26th Easterns Qualifier 2023
154 George Washington Win 14-8 1482.68 Feb 26th Easterns Qualifier 2023
190 Pittsburgh-B Win 13-5 1381.66 Mar 18th Spring Fling adelphia
235 Drexel Win 13-6 1170.94 Mar 18th Spring Fling adelphia
246 Pennsylvania Win 12-8 951.44 Mar 18th Spring Fling adelphia
342 SUNY-Fredonia** Win 13-1 -441.93 Ignored Mar 18th Spring Fling adelphia
341 Delaware-B** Win 15-1 -203.63 Ignored Mar 19th Spring Fling adelphia
235 Drexel Win 11-5 1170.94 Mar 19th Spring Fling adelphia
190 Pittsburgh-B Win 12-8 1222.81 Mar 19th Spring Fling adelphia
**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)