#131 Pittsburgh-B (12-7)

avg: 1121.3  •  sd: 50.48  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
149 Davidson Win 8-7 1179.75 Feb 15th 2025 Commonwealth Cup Weekend 1
241 Michigan-B Win 13-8 1138.46 Feb 15th 2025 Commonwealth Cup Weekend 1
255 Wake Forest Win 12-7 1106.99 Feb 15th 2025 Commonwealth Cup Weekend 1
149 Davidson Loss 8-11 689.14 Feb 16th 2025 Commonwealth Cup Weekend 1
124 Denver Loss 11-12 1021.36 Feb 16th 2025 Commonwealth Cup Weekend 1
303 Ball State** Win 15-4 964.79 Ignored Mar 15th Grand Rapids Invite 2025
212 Eastern Michigan Win 15-9 1265.81 Mar 15th Grand Rapids Invite 2025
292 Western Michigan** Win 15-4 1006.21 Ignored Mar 15th Grand Rapids Invite 2025
190 Toronto Win 14-5 1459.63 Mar 15th Grand Rapids Invite 2025
60 Michigan State Loss 9-13 1067.78 Mar 16th Grand Rapids Invite 2025
143 Michigan Tech Win 10-9 1187.91 Mar 16th Grand Rapids Invite 2025
221 Wisconsin-B Win 15-6 1310.71 Mar 16th Grand Rapids Invite 2025
71 Case Western Reserve Loss 4-15 805.06 Mar 29th East Coast Invite 2025
108 Columbia Loss 11-12 1094.19 Mar 29th East Coast Invite 2025
192 Princeton Win 13-7 1411.94 Mar 29th East Coast Invite 2025
177 Towson Win 11-9 1174.77 Mar 29th East Coast Invite 2025
174 Delaware Win 9-5 1467.07 Mar 30th East Coast Invite 2025
88 Georgetown Loss 7-15 708.67 Mar 30th East Coast Invite 2025
116 West Chester Loss 11-13 963.33 Mar 30th East Coast Invite 2025
**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)