#163 Xavier (15-6)

avg: 973.48  •  sd: 65.42  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
284 Pacific Lutheran Win 13-4 1030.87 Feb 10th DIII Grand Prix
80 Lewis & Clark Win 10-8 1602.35 Feb 10th DIII Grand Prix
59 Whitman Loss 4-13 836.78 Feb 10th DIII Grand Prix
279 Whitworth Win 13-1 1079.7 Feb 10th DIII Grand Prix
234 Claremont Loss 7-9 423.8 Feb 11th DIII Grand Prix
190 Portland Win 10-8 1124.42 Feb 11th DIII Grand Prix
286 Reed Win 12-8 863.3 Feb 11th DIII Grand Prix
242 Butler Win 13-6 1268.37 Mar 2nd FCS D III Tune Up 2024
231 Christopher Newport Win 13-8 1208.77 Mar 2nd FCS D III Tune Up 2024
125 Davidson Loss 6-11 599.87 Mar 2nd FCS D III Tune Up 2024
137 Union (Tennessee) Loss 5-13 490.5 Mar 2nd FCS D III Tune Up 2024
99 Elon Loss 10-13 914.37 Mar 3rd FCS D III Tune Up 2024
118 Michigan Tech Loss 8-13 677.46 Mar 3rd FCS D III Tune Up 2024
179 Missouri S&T Win 13-7 1461.63 Mar 3rd FCS D III Tune Up 2024
242 Butler Win 12-10 906.5 Mar 23rd Butler Spring Fling
364 Michigan State-B** Win 13-5 544.62 Ignored Mar 23rd Butler Spring Fling
319 Purdue-B** Win 13-5 862.3 Ignored Mar 23rd Butler Spring Fling
275 Western Michigan Win 13-6 1102.94 Mar 23rd Butler Spring Fling
249 Hillsdale Win 9-7 908.57 Mar 24th Butler Spring Fling
318 Rose-Hulman Win 11-6 813.83 Mar 24th Butler Spring Fling
262 Loyola-Chicago Win 13-2 1145.98 Mar 24th Butler Spring Fling
**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)