#65 Princeton (11-1)

avg: 1374.33  •  sd: 77.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
235 Drexel Win 13-6 1170.94 Feb 18th Blue Hen Open
143 Virginia Commonwealth Win 13-4 1586.71 Feb 18th Blue Hen Open
215 Villanova Win 12-8 1099.18 Feb 18th Blue Hen Open
158 NYU Win 10-7 1321.02 Feb 18th Blue Hen Open
151 Johns Hopkins Win 15-10 1411.2 Feb 19th Blue Hen Open
68 Lehigh Loss 6-13 767.26 Feb 19th Blue Hen Open
183 Maine Win 13-4 1414.14 Mar 11th Oak Creek Invite 2023
101 Liberty Win 11-7 1656.46 Mar 11th Oak Creek Invite 2023
173 SUNY-Geneseo Win 13-10 1202.46 Mar 11th Oak Creek Invite 2023
167 SUNY-Cortland Win 13-9 1315.2 Mar 11th Oak Creek Invite 2023
67 Maryland Win 12-9 1715.4 Mar 12th Oak Creek Invite 2023
72 RIT Win 12-9 1695.52 Mar 12th Oak Creek Invite 2023
**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)