#165 Penn State-B (12-10)

avg: 1098.45  •  sd: 61.51  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
144 Army Loss 6-13 575.5 Feb 25th Bring The Huckus1
95 Massachusetts-B Loss 6-10 926.06 Feb 25th Bring The Huckus1
263 Swarthmore Win 13-2 1281.29 Feb 25th Bring The Huckus1
100 Vermont-B Loss 10-11 1268.49 Feb 25th Bring The Huckus1
263 Swarthmore Win 13-4 1281.29 Feb 26th Bring The Huckus1
177 Rochester Loss 6-7 915.19 Feb 26th Bring The Huckus1
100 Vermont-B Loss 8-10 1130.83 Feb 26th Bring The Huckus1
206 Colby Win 10-7 1309.07 Mar 4th Philly Special1
247 Wisconsin-Whitewater Win 15-0 1348.21 Mar 4th Philly Special1
220 Dickinson Win 8-4 1422.44 Mar 4th Philly Special1
206 Colby Win 13-6 1519.4 Mar 5th Philly Special1
307 West Chester-B** Win 13-3 1004.29 Ignored Mar 5th Philly Special1
247 Wisconsin-Whitewater Win 15-11 1129.38 Mar 5th Philly Special1
119 College of New Jersey Loss 6-7 1172.25 Mar 26th Garden State1
181 West Virginia Loss 5-11 414.57 Mar 26th Garden State1
185 West Chester Loss 5-7 676.51 Mar 26th Garden State1
132 Ave Maria Loss 9-11 993.18 Apr 1st 2023 B team Brodown
273 Michigan-B Win 12-7 1136.07 Apr 1st 2023 B team Brodown
340 Lehigh-B** Win 11-4 771.65 Ignored Apr 1st 2023 B team Brodown
173 Pittsburgh-B Win 14-13 1186.3 Apr 1st 2023 B team Brodown
130 Messiah Win 13-11 1481.13 Apr 2nd 2023 B team Brodown
126 Franciscan Loss 8-12 826.44 Apr 2nd 2023 B team Brodown
**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)