#250 Maryland-Baltimore County (7-13)

avg: 768.7  •  sd: 67.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
193 Liberty Loss 7-12 445.98 Feb 3rd Mid Atlantic Warmup 2018
86 Duke** Loss 4-10 798.98 Ignored Feb 3rd Mid Atlantic Warmup 2018
194 George Washington Win 11-9 1213.63 Feb 3rd Mid Atlantic Warmup 2018
115 Villanova Loss 9-10 1151.67 Feb 3rd Mid Atlantic Warmup 2018
84 Virginia** Loss 4-15 802.14 Ignored Feb 4th Mid Atlantic Warmup 2018
177 Virginia Commonwealth Loss 7-13 464.7 Feb 4th Mid Atlantic Warmup 2018
117 Pennsylvania Loss 3-13 671.31 Feb 24th Oak Creek Challenge 2018
54 Mary Washington** Loss 4-13 924.23 Ignored Feb 24th Oak Creek Challenge 2018
243 Rowan Win 14-12 1004.97 Feb 24th Oak Creek Challenge 2018
109 Williams Loss 7-13 738.68 Feb 24th Oak Creek Challenge 2018
169 Johns Hopkins Loss 11-13 831.84 Feb 25th Oak Creek Challenge 2018
182 NYU Loss 9-15 483.27 Feb 25th Oak Creek Challenge 2018
109 Williams Loss 5-13 696.21 Feb 25th Oak Creek Challenge 2018
306 Carleton College-Hot Karls Win 13-8 1056.52 Mar 17th Rip Tide 2018
413 Wisconsin-Milwaukee-B** Win 13-2 499.67 Ignored Mar 17th Rip Tide 2018
289 SUNY-Fredonia Win 13-4 1230.25 Mar 17th Rip Tide 2018
311 Messiah Win 13-7 1108.72 Mar 17th Rip Tide 2018
311 Messiah Loss 7-12 30.68 Mar 18th Rip Tide 2018
- St Lawrence Win 13-8 694.82 Mar 18th Rip Tide 2018
289 SUNY-Fredonia Loss 9-10 505.25 Mar 18th Rip Tide 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)