#225 SUNY-Oneonta (8-4)

avg: 916.54  •  sd: 145.78  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
438 St Mary's (Maryland)** Win 15-3 367.63 Ignored Mar 23rd Towson Cup 2019
410 Maryland-Baltimore County-B** Win 15-2 760.34 Ignored Mar 23rd Towson Cup 2019
437 Towson -B** Win 13-3 372.21 Ignored Mar 23rd Towson Cup 2019
428 American-B** Win 15-1 572.84 Ignored Mar 24th Towson Cup 2019
270 Delaware-B Win 13-4 1382.13 Mar 24th Towson Cup 2019
442 SUNY Oneonta-B** Win 15-1 199.16 Ignored Mar 24th Towson Cup 2019
217 Amherst College Loss 6-8 626.09 Mar 30th Tea Cup 2019
214 Hartford Loss 7-10 549.01 Mar 30th Tea Cup 2019
122 Yale Loss 8-10 1016.86 Mar 30th Tea Cup 2019
262 Tufts-B Loss 9-10 694.8 Mar 30th Tea Cup 2019
445 Amherst College-B** Win 13-0 -192.83 Ignored Mar 31st Tea Cup 2019
255 Boston College-B Win 11-8 1198.16 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)