#162 Rowan (9-2)

avg: 920.56  •  sd: 94.61  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
266 Maryland-Baltimore County Win 11-4 1037.69 Mar 4th Oak Creek Challenge 2023
151 Johns Hopkins Loss 6-13 357.59 Mar 4th Oak Creek Challenge 2023
68 Lehigh Loss 5-13 767.26 Mar 4th Oak Creek Challenge 2023
180 American Win 11-10 953.1 Mar 5th Oak Creek Challenge 2023
235 Drexel Win 10-8 833.61 Mar 5th Oak Creek Challenge 2023
296 Salisbury Win 11-7 657.92 Mar 5th Oak Creek Challenge 2023
159 Ithaca Win 9-7 1208.23 Mar 26th Garden State1
159 Ithaca Win 11-9 1178.1 Mar 26th Garden State1
- Skidmore Win 10-6 848.75 Mar 26th Garden State1
338 Siena** Win 13-3 299.22 Ignored Mar 26th Garden State1
175 West Virginia Win 10-7 1253.64 Mar 26th Garden State1
**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)