#153 Xavier (9-9)

avg: 1115.49  •  sd: 57.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
120 Mississippi State Loss 7-13 703.76 Jan 20th T Town Throwdown XIV Open
66 Kennesaw State Loss 7-12 937.5 Jan 20th T Town Throwdown XIV Open
97 Alabama Win 13-12 1472.93 Jan 20th T Town Throwdown XIV Open
335 Southern Mississippi** Win 12-5 1028.37 Ignored Jan 20th T Town Throwdown XIV Open
45 Illinois State Loss 6-15 986.15 Jan 21st T Town Throwdown XIV Open
23 Georgia Tech** Loss 5-15 1143.96 Ignored Jan 21st T Town Throwdown XIV Open
63 Tulane Loss 5-11 863.68 Jan 21st T Town Throwdown XIV Open
181 Ball State Win 12-7 1531.27 Feb 24th Music City Tune Up 2018
88 Alabama-Huntsville Loss 9-10 1263.31 Feb 24th Music City Tune Up 2018
236 Middle Tennessee State Win 13-8 1296.41 Feb 24th Music City Tune Up 2018
155 Vanderbilt Win 9-7 1392.14 Feb 24th Music City Tune Up 2018
352 Belmont** Win 13-5 958.54 Ignored Mar 17th D III Midwestern Invite 2018
384 Grinnell** Win 13-2 807.76 Ignored Mar 17th D III Midwestern Invite 2018
257 Knox Win 10-6 1251.79 Mar 17th D III Midwestern Invite 2018
135 Brandeis Loss 9-13 756.3 Mar 17th D III Midwestern Invite 2018
35 Air Force Loss 7-15 1039.57 Mar 18th D III Midwestern Invite 2018
135 Brandeis Loss 12-15 874.37 Mar 18th D III Midwestern Invite 2018
232 St. Thomas Win 15-11 1200.51 Mar 18th D III Midwestern Invite 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)