#56 Temple (13-7)

avg: 1509.6  •  sd: 75.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
135 Brandeis Win 12-7 1695.37 Feb 24th Oak Creek Challenge 2018
194 George Washington Win 13-6 1564.43 Feb 24th Oak Creek Challenge 2018
115 Villanova Loss 10-12 1038.55 Feb 24th Oak Creek Challenge 2018
136 Ohio Win 15-8 1739.58 Feb 25th Oak Creek Challenge 2018
106 RIT Loss 12-15 1015.26 Feb 25th Oak Creek Challenge 2018
115 Villanova Loss 12-15 976.18 Feb 25th Oak Creek Challenge 2018
70 Arkansas Win 13-5 2039.53 Mar 10th Mens Centex 2018
4 Minnesota Loss 6-13 1469.92 Mar 10th Mens Centex 2018
160 Oklahoma Win 10-7 1482.26 Mar 10th Mens Centex 2018
41 Northeastern Win 11-7 2070.25 Mar 10th Mens Centex 2018
29 Texas Loss 5-13 1111.1 Mar 10th Mens Centex 2018
68 Baylor Loss 10-11 1329.82 Mar 11th Mens Centex 2018
82 Oklahoma State Win 13-11 1636.03 Mar 11th Mens Centex 2018
184 Texas-San Antonio Win 15-6 1584.1 Mar 11th Mens Centex 2018
139 Luther Win 13-9 1586.61 Mar 24th Atlantic Coast Open 2018
368 Edinboro** Win 13-0 911.46 Ignored Mar 24th Atlantic Coast Open 2018
194 George Washington Win 13-7 1521.96 Mar 24th Atlantic Coast Open 2018
78 Georgetown Loss 11-13 1186.23 Mar 24th Atlantic Coast Open 2018
167 North Carolina-B Win 11-6 1612.94 Mar 25th Atlantic Coast Open 2018
84 Virginia Win 13-8 1898.3 Mar 25th Atlantic Coast Open 2018
**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)