#277 Loyola-Chicago (6-4)

avg: 592.28  •  sd: 82.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
- Milwaukee Engineering Win 11-4 726.38 Mar 19th Meltdown College
183 Minnesota-B Loss 5-10 436.96 Mar 19th Meltdown College
294 Winona State Win 8-5 959.33 Mar 19th Meltdown College
247 Wisconsin-Whitewater Loss 8-11 382.52 Mar 19th Meltdown College
296 Wisconsin-B Win 7-6 626.68 Mar 25th Old Capitol Open
338 St John's (Minnesota) Win 8-5 638.35 Mar 25th Old Capitol Open
199 Nebraska Loss 5-12 344.95 Mar 25th Old Capitol Open
255 Toledo Loss 6-12 136.1 Mar 26th Old Capitol Open
338 St John's (Minnesota) Win 11-5 784.75 Mar 26th Old Capitol Open
309 Wisconsin-Stevens Point Win 11-5 988.74 Mar 26th Old Capitol Open
**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)