#255 Boston College-B (9-6)

avg: 832.55  •  sd: 99.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
377 Stetson Win 13-6 953.38 Feb 8th Florida Warm Up 2019
300 High Point Loss 9-10 552.14 Feb 8th Florida Warm Up 2019
246 Florida-B Loss 6-13 275.42 Feb 8th Florida Warm Up 2019
418 South Florida-B** Win 12-4 700.43 Ignored Feb 9th Florida Warm Up 2019
294 Florida Gulf Coast Win 13-4 1297.54 Feb 9th Florida Warm Up 2019
207 North Florida Loss 8-12 524.36 Feb 9th Florida Warm Up 2019
295 Embry-Riddle (Florida) Loss 6-15 95.98 Feb 10th Florida Warm Up 2019
366 Central Florida-B Win 11-4 1000.1 Feb 10th Florida Warm Up 2019
436 Yale-B** Win 13-1 386.51 Ignored Mar 30th Tea Cup 2019
220 Northeastern-B Loss 5-7 593.83 Mar 30th Tea Cup 2019
297 Connecticut-B Win 10-4 1294.68 Mar 30th Tea Cup 2019
293 Wentworth Win 13-8 1197.56 Mar 30th Tea Cup 2019
372 Rutgers-B Win 13-6 962.59 Mar 31st Tea Cup 2019
297 Connecticut-B Win 7-0 1294.68 Mar 31st Tea Cup 2019
225 SUNY-Oneonta Loss 8-11 550.93 Mar 31st Tea Cup 2019
**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)