#63 Temple (12-3)

avg: 1275.4  •  sd: 54.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
108 Princeton Win 12-8 1448.78 Feb 5th New Jersey Warmup
167 Columbia Win 11-7 1078.26 Feb 10th New Jersey Warmup
178 NYU** Win 13-5 1126.64 Ignored Feb 10th New Jersey Warmup
122 Lehigh Win 13-7 1499.46 Feb 10th New Jersey Warmup
91 Syracuse Win 14-10 1488.46 Feb 11th New Jersey Warmup
167 Columbia Win 14-9 1085.23 Feb 11th New Jersey Warmup
122 Lehigh Win 15-12 1242.42 Feb 11th New Jersey Warmup
101 SUNY-Binghamton Win 12-11 1149.93 Feb 24th Easterns Qualifier 2024
137 Kennesaw State Win 10-8 1101.28 Feb 24th Easterns Qualifier 2024
77 William & Mary Win 11-10 1330.07 Feb 24th Easterns Qualifier 2024
29 Ohio State Loss 9-11 1345.85 Feb 24th Easterns Qualifier 2024
66 Emory Win 13-8 1751.45 Feb 25th Easterns Qualifier 2024
34 South Carolina Loss 10-13 1232.61 Feb 25th Easterns Qualifier 2024
54 Alabama Loss 9-14 884.24 Feb 25th Easterns Qualifier 2024
88 Notre Dame Win 12-11 1237.7 Feb 25th Easterns Qualifier 2024
**Blowout Eligible

FAQ

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
  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
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)