#123 Pennsylvania (11-11)

avg: 1147.48  •  sd: 66.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
70 Case Western Reserve Loss 8-9 1241.71 Jan 27th Mid Atlantic Warm Up
98 Dartmouth Loss 7-10 855.97 Jan 27th Mid Atlantic Warm Up
61 William & Mary Loss 7-13 874.48 Jan 27th Mid Atlantic Warm Up
298 Mary Washington** Win 13-4 963.78 Ignored Jan 27th Mid Atlantic Warm Up
165 RIT Win 15-10 1418.89 Jan 28th Mid Atlantic Warm Up
116 Liberty Loss 8-11 817.35 Jan 28th Mid Atlantic Warm Up
208 Virginia Commonwealth Win 15-0 1385.38 Jan 28th Mid Atlantic Warm Up
167 Columbia Win 11-6 1504.95 Feb 5th New Jersey Warmup
107 Princeton Loss 11-12 1083.6 Feb 10th New Jersey Warmup
169 Rutgers Loss 7-12 431.13 Feb 10th New Jersey Warmup
113 Syracuse Loss 9-13 770.27 Feb 10th New Jersey Warmup
167 Columbia Win 15-8 1523.06 Feb 11th New Jersey Warmup
196 NYU Win 13-9 1259.76 Feb 11th New Jersey Warmup
107 Princeton Loss 13-14 1083.6 Feb 11th New Jersey Warmup
167 Columbia Win 11-5 1558.25 Mar 30th East Coast Invite 2024
101 Cornell Win 11-10 1349.57 Mar 30th East Coast Invite 2024
154 Harvard Win 10-8 1285.85 Mar 30th East Coast Invite 2024
146 Yale Loss 7-10 670.39 Mar 30th East Coast Invite 2024
70 Case Western Reserve Loss 7-13 809.18 Mar 31st East Coast Invite 2024
96 Connecticut Win 11-10 1374.4 Mar 31st East Coast Invite 2024
101 Cornell Loss 6-7 1099.57 Mar 31st East Coast Invite 2024
169 Rutgers Win 11-6 1498.33 Mar 31st East Coast Invite 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)