#270 Rowan (5-16)

avg: 511.96  •  sd: 42.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
141 Bryant Loss 4-12 474.31 Feb 10th UMass Invite 2024
46 Williams** Loss 1-13 924.96 Ignored Feb 10th UMass Invite 2024
162 Wesleyan Loss 9-13 567.07 Feb 10th UMass Invite 2024
100 Vermont-B** Loss 5-13 635.55 Ignored Feb 10th UMass Invite 2024
148 Rochester Loss 7-15 436.81 Feb 11th UMass Invite 2024
146 Yale Loss 5-12 460.05 Feb 11th UMass Invite 2024
162 Wesleyan Loss 8-11 620.03 Feb 11th UMass Invite 2024
143 Brown-B Loss 4-11 465.97 Mar 2nd Philly Special 2024
277 Stevens Tech Win 7-5 825.59 Mar 2nd Philly Special 2024
367 Siena** Win 7-0 466.38 Ignored Mar 2nd Philly Special 2024
103 Bowdoin** Loss 2-13 616 Ignored Mar 3rd Philly Special 2024
143 Brown-B Loss 5-13 465.97 Mar 3rd Philly Special 2024
127 College of New Jersey Loss 9-12 799.06 Mar 3rd Philly Special 2024
277 Stevens Tech Loss 8-12 56.3 Mar 3rd Philly Special 2024
197 Haverford Loss 6-9 417.62 Mar 23rd Garden State 2024
344 Lehigh-B Win 11-5 686.14 Mar 23rd Garden State 2024
374 West Chester-B** Win 7-0 267.76 Ignored Mar 23rd Garden State 2024
198 Delaware Loss 7-11 367.78 Mar 24th Garden State 2024
281 Edinboro Win 8-6 751.33 Mar 24th Garden State 2024
277 Stevens Tech Loss 7-8 372.45 Mar 24th Garden State 2024
152 West Chester Loss 5-9 497.51 Mar 24th Garden State 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)