#319 Edinboro (10-16)

avg: 654.16  •  sd: 70.35  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
182 Dayton Loss 3-13 590.4 Feb 3rd Huckin in the Hills X
387 Ohio-B Win 13-8 738.66 Feb 3rd Huckin in the Hills X
126 Towson** Loss 3-13 789.09 Ignored Feb 3rd Huckin in the Hills X
182 Dayton Loss 4-15 590.4 Feb 4th Huckin in the Hills X
194 Ohio Loss 2-15 542.62 Feb 4th Huckin in the Hills X
387 Ohio-B Win 12-6 821.81 Feb 4th Huckin in the Hills X
256 Salisbury Loss 9-13 501.22 Feb 24th Bring The Huckus 2024
310 Stevens Tech Loss 7-11 211.94 Feb 24th Bring The Huckus 2024
382 Lehigh-B Win 9-4 887.63 Feb 24th Bring The Huckus 2024
245 Skidmore Loss 3-9 378.07 Feb 24th Bring The Huckus 2024
331 Rutgers-B Win 15-9 1111.77 Feb 25th Bring The Huckus 2024
318 Swarthmore Win 11-10 780.47 Feb 25th Bring The Huckus 2024
407 West Chester-B** Win 13-0 467.56 Ignored Mar 23rd Garden State 2024
310 Stevens Tech Win 11-4 1278.83 Mar 23rd Garden State 2024
234 Haverford Loss 4-7 503.36 Mar 23rd Garden State 2024
382 Lehigh-B Win 11-4 887.63 Mar 24th Garden State 2024
272 Rowan Loss 6-8 556.29 Mar 24th Garden State 2024
212 West Virginia Loss 4-10 479.5 Mar 24th Garden State 2024
170 Villanova Loss 5-11 651.96 Mar 24th Garden State 2024
77 Carnegie Mellon** Loss 4-13 1007.47 Ignored Apr 20th West Penn D I Mens Conferences 2024
16 Penn State** Loss 1-13 1548.22 Ignored Apr 20th West Penn D I Mens Conferences 2024
417 Slippery Rock** Win 13-5 600 Ignored Apr 20th West Penn D I Mens Conferences 2024
212 West Virginia Loss 4-10 479.5 Apr 20th West Penn D I Mens Conferences 2024
6 Pittsburgh** Loss 5-13 1732.82 Ignored Apr 21st West Penn D I Mens Conferences 2024
417 Slippery Rock** Win 15-5 600 Ignored Apr 21st West Penn D I Mens Conferences 2024
212 West Virginia Loss 3-15 479.5 Apr 21st West Penn D I Mens Conferences 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)