#63 Tulane (11-9)

avg: 1463.68  •  sd: 88.18  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
45 Illinois State Win 11-8 1951.76 Jan 20th T Town Throwdown XIV Open
154 Mississippi Win 13-3 1714.56 Jan 20th T Town Throwdown XIV Open
396 Kentucky-B Win 13-6 709.63 Jan 20th T Town Throwdown XIV Open
259 Northern Illinois Win 13-7 1309.93 Jan 20th T Town Throwdown XIV Open
120 Mississippi State Loss 8-11 895.68 Jan 21st T Town Throwdown XIV Open
94 Kentucky Loss 12-14 1141.71 Jan 21st T Town Throwdown XIV Open
153 Xavier Win 11-5 1715.49 Jan 21st T Town Throwdown XIV Open
47 Iowa State Win 10-8 1830.91 Mar 10th Mens Centex 2018
17 Colorado State Loss 5-13 1269.76 Mar 10th Mens Centex 2018
199 Stephen F Austin Win 12-6 1513.78 Mar 10th Mens Centex 2018
21 Texas A&M Loss 7-11 1355.17 Mar 10th Mens Centex 2018
26 Texas-Dallas Loss 3-12 1129.02 Mar 10th Mens Centex 2018
82 Oklahoma State Loss 10-12 1169.07 Mar 11th Mens Centex 2018
184 Texas-San Antonio Win 12-9 1329.47 Mar 11th Mens Centex 2018
38 Southern California Win 10-9 1758.89 Mar 24th NW Challenge 2018
32 California Win 11-7 2162.69 Mar 24th NW Challenge 2018
5 Washington Loss 5-10 1477.51 Mar 24th NW Challenge 2018
24 Western Washington Loss 6-13 1142.07 Mar 24th NW Challenge 2018
43 British Columbia Win 15-5 2194.64 Mar 25th NW Challenge 2018
25 Victoria Loss 5-15 1131.74 Mar 25th NW Challenge 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)