#237 Loyola-Chicago (5-8)

avg: 899.86  •  sd: 93.54  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
332 Milwaukee School of Engineering Win 13-5 1159.2 Mar 23rd Meltdown 2019
424 Coe** Win 13-0 635.35 Ignored Mar 23rd Meltdown 2019
86 Marquette Loss 5-11 826.08 Mar 23rd Meltdown 2019
177 Winona State Loss 8-11 696.43 Mar 23rd Meltdown 2019
203 Wheaton (Illinois) Loss 9-10 847.12 Mar 24th Meltdown 2019
346 Marquette-B Win 9-4 1100.18 Mar 24th Meltdown 2019
309 Illinois State-B Loss 8-10 370.55 Mar 24th Meltdown 2019
276 North Park Loss 9-12 424.27 Mar 24th Meltdown 2019
148 Michigan-B Loss 11-12 1056.95 Mar 30th Illinois Invite 8
235 Northern Iowa Win 13-4 1505.68 Mar 30th Illinois Invite 8
224 DePaul Win 6-4 1282.17 Mar 31st Illinois Invite 8
97 Grand Valley State Loss 7-9 1084.46 Mar 31st Illinois Invite 8
236 Wisconsin-Platteville Loss 6-9 483.44 Mar 31st Illinois Invite 8
**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)