#250 Shippensburg (4-6)

avg: 732.92  •  sd: 51.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
- Bentley Loss 9-11 652.56 Mar 25th Layout Pigout 2023
66 Bowdoin** Loss 6-15 964.31 Ignored Mar 25th Layout Pigout 2023
99 Oberlin Loss 6-9 979.1 Mar 25th Layout Pigout 2023
273 Michigan-B Win 9-8 740.53 Apr 1st 2023 B team Brodown
340 Lehigh-B Win 12-3 771.51 Apr 1st 2023 B team Brodown
126 Franciscan Loss 5-12 667.64 Apr 1st 2023 B team Brodown
- Dayton-B** Win 11-2 600 Ignored Apr 1st 2023 B team Brodown
130 Messiah Loss 5-13 652.36 Apr 2nd 2023 B team Brodown
173 Pittsburgh-B Loss 6-12 481.98 Apr 2nd 2023 B team Brodown
261 Ohio State Win 10-8 951.39 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)