#44 Illinois (15-12)

avg: 1589.03  •  sd: 60.88  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
145 Drexel Win 12-6 1728.62 Feb 3rd Mid Atlantic Warmup 2018
109 Williams Win 13-9 1714.78 Feb 3rd Mid Atlantic Warmup 2018
227 Syracuse** Win 13-2 1436.26 Ignored Feb 3rd Mid Atlantic Warmup 2018
34 William & Mary Win 11-8 2013.81 Feb 3rd Mid Atlantic Warmup 2018
78 Georgetown Win 14-8 1951.11 Feb 4th Mid Atlantic Warmup 2018
102 Richmond Win 15-14 1451.88 Feb 4th Mid Atlantic Warmup 2018
48 Dartmouth Win 13-11 1794.27 Feb 4th Mid Atlantic Warmup 2018
67 Utah Loss 8-13 961.81 Feb 17th Presidents Day Invitational Tournament 2018
3 Oregon Loss 8-13 1692.61 Feb 17th Presidents Day Invitational Tournament 2018
59 Santa Clara Win 13-7 2057.3 Feb 17th Presidents Day Invitational Tournament 2018
53 UCLA Win 12-11 1659.42 Feb 17th Presidents Day Invitational Tournament 2018
24 Western Washington Loss 10-13 1413.92 Feb 18th Presidents Day Invitational Tournament 2018
5 Washington Loss 6-13 1451.41 Feb 18th Presidents Day Invitational Tournament 2018
59 Santa Clara Win 11-9 1748.98 Feb 19th Presidents Day Invitational Tournament 2018
148 San Diego State Win 11-7 1613.97 Feb 19th Presidents Day Invitational Tournament 2018
60 Cornell Win 15-4 2073.23 Feb 19th Presidents Day Invitational Tournament 2018
82 Oklahoma State Loss 11-12 1282.19 Mar 10th Mens Centex 2018
58 Kansas Win 10-7 1890.53 Mar 10th Mens Centex 2018
68 Baylor Win 10-8 1717.49 Mar 10th Mens Centex 2018
14 Florida Loss 7-13 1329.28 Mar 10th Mens Centex 2018
4 Minnesota Loss 9-14 1596.05 Mar 11th Mens Centex 2018
27 Texas State Win 15-8 2285.97 Mar 11th Mens Centex 2018
17 Colorado State Loss 6-14 1269.76 Mar 11th Mens Centex 2018
51 Ohio State Loss 8-12 1096.54 Mar 31st Huck Finn 2018
39 Northwestern Loss 10-15 1175.09 Mar 31st Huck Finn 2018
29 Texas Loss 11-12 1586.1 Mar 31st Huck Finn 2018
62 Vermont Loss 10-11 1340.83 Mar 31st Huck Finn 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)