#124 Towson (9-4)

avg: 1269.13  •  sd: 98.69  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
162 American Win 8-6 1404.9 Mar 4th Oak Creek Challenge 2023
185 West Chester Win 13-0 1604.65 Mar 4th Oak Creek Challenge 2023
345 Salisbury** Win 13-1 713.45 Ignored Mar 4th Oak Creek Challenge 2023
70 Lehigh Loss 8-13 1030.57 Mar 5th Oak Creek Challenge 2023
157 Yale Loss 6-7 1000.16 Mar 5th Oak Creek Challenge 2023
67 Virginia Tech Loss 2-13 953.29 Mar 5th Oak Creek Challenge 2023
330 Edinboro** Win 13-3 869.37 Ignored Apr 1st Fuego2
220 Dickinson Win 13-7 1415.16 Apr 1st Fuego2
239 Stevens Tech Win 13-2 1361.75 Apr 1st Fuego2
345 Salisbury** Win 13-5 713.45 Ignored Apr 1st Fuego2
119 College of New Jersey Loss 6-12 717.94 Apr 2nd Fuego2
175 Rowan Win 13-2 1648.16 Apr 2nd Fuego2
185 West Chester Win 12-8 1445.8 Apr 2nd Fuego2
**Blowout Eligible


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)