#77 Temple (16-13)

avg: 1480.32  •  sd: 70.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
11 Brown Loss 7-13 1517.19 Feb 3rd Florida Warm Up 2023
26 Georgia Tech Loss 5-11 1268.33 Feb 3rd Florida Warm Up 2023
112 Illinois Win 9-8 1440.98 Feb 4th Florida Warm Up 2023
201 South Florida Win 13-2 1537.69 Feb 4th Florida Warm Up 2023
23 Wisconsin Loss 9-13 1475.95 Feb 4th Florida Warm Up 2023
5 Vermont Loss 7-15 1610.06 Feb 4th Florida Warm Up 2023
88 Central Florida Loss 11-12 1309.22 Feb 5th Florida Warm Up 2023
104 Florida State Loss 9-10 1220.01 Feb 5th Florida Warm Up 2023
37 McGill Loss 8-10 1510.71 Feb 25th Easterns Qualifier 2023
56 James Madison Loss 11-13 1370.8 Feb 25th Easterns Qualifier 2023
27 South Carolina Loss 7-13 1290.65 Feb 25th Easterns Qualifier 2023
131 Georgia State Loss 7-11 775.69 Feb 25th Easterns Qualifier 2023
59 Cincinnati Loss 6-15 978.71 Feb 26th Easterns Qualifier 2023
104 Florida State Win 13-7 1902.54 Feb 26th Easterns Qualifier 2023
150 George Washington Win 14-8 1684.28 Feb 26th Easterns Qualifier 2023
173 Pittsburgh-B Win 13-5 1661.3 Mar 18th Spring Fling adelphia
248 Drexel Win 13-6 1344.55 Mar 18th Spring Fling adelphia
244 Pennsylvania Win 12-8 1192.46 Mar 18th Spring Fling adelphia
350 SUNY-Fredonia** Win 13-1 638.15 Ignored Mar 18th Spring Fling adelphia
362 Delaware-B** Win 15-1 281.72 Ignored Mar 19th Spring Fling adelphia
248 Drexel Win 11-5 1344.55 Mar 19th Spring Fling adelphia
173 Pittsburgh-B Win 12-8 1502.46 Mar 19th Spring Fling adelphia
162 American Win 12-3 1704.41 Apr 1st Atlantic Coast Open 2023
190 MIT Win 13-9 1410.13 Apr 1st Atlantic Coast Open 2023
33 Duke Loss 10-13 1462.52 Apr 1st Atlantic Coast Open 2023
67 Virginia Tech Loss 8-9 1428.29 Apr 1st Atlantic Coast Open 2023
71 Cornell Win 15-7 2103.6 Apr 2nd Atlantic Coast Open 2023
167 Virginia Commonwealth Win 15-4 1690.1 Apr 2nd Atlantic Coast Open 2023
101 Navy Win 7-4 1872.65 Apr 2nd Atlantic Coast Open 2023
**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)