#60 Temple (15-6)

avg: 1435.02  •  sd: 45.88  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
107 Princeton Win 12-8 1649.76 Feb 5th New Jersey Warmup
167 Columbia Win 11-7 1425.15 Feb 10th New Jersey Warmup
196 NYU Win 13-5 1441.19 Feb 10th New Jersey Warmup
126 Lehigh Win 13-7 1702.93 Feb 10th New Jersey Warmup
167 Columbia Win 14-9 1432.12 Feb 11th New Jersey Warmup
113 Syracuse Win 14-10 1587.54 Feb 11th New Jersey Warmup
126 Lehigh Win 15-12 1445.89 Feb 11th New Jersey Warmup
158 Kennesaw State Win 10-8 1273.38 Feb 24th Easterns Qualifier 2024
34 Ohio State Loss 9-11 1392.66 Feb 24th Easterns Qualifier 2024
111 SUNY-Binghamton Win 12-11 1316.72 Feb 24th Easterns Qualifier 2024
61 William & Mary Win 11-10 1557.01 Feb 24th Easterns Qualifier 2024
50 Alabama Loss 9-14 1027.7 Feb 25th Easterns Qualifier 2024
56 Emory Win 13-8 1943.74 Feb 25th Easterns Qualifier 2024
106 Notre Dame Win 12-11 1335.32 Feb 25th Easterns Qualifier 2024
29 South Carolina Loss 10-13 1355.77 Feb 25th Easterns Qualifier 2024
85 Carnegie Mellon Loss 12-13 1193.33 Mar 30th Atlantic Coast Open 2024
62 Massachusetts -B Loss 10-12 1193.66 Mar 30th Atlantic Coast Open 2024
90 SUNY-Buffalo Win 15-8 1839.82 Mar 30th Atlantic Coast Open 2024
52 Virginia Tech Loss 12-13 1350.52 Mar 30th Atlantic Coast Open 2024
165 RIT Win 15-10 1418.89 Mar 31st Atlantic Coast Open 2024
90 SUNY-Buffalo Win 13-12 1400.01 Mar 31st Atlantic Coast Open 2024
**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)