#173 Pittsburgh-B (8-4)

avg: 1061.3  •  sd: 82.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
77 Temple Loss 5-13 880.32 Mar 18th Spring Fling adelphia
244 Pennsylvania Win 12-10 989.43 Mar 18th Spring Fling adelphia
350 SUNY-Fredonia** Win 13-1 638.15 Ignored Mar 18th Spring Fling adelphia
248 Drexel Win 12-11 869.55 Mar 18th Spring Fling adelphia
332 Messiah** Win 15-1 846.26 Ignored Mar 19th Spring Fling adelphia
77 Temple Loss 8-12 1039.16 Mar 19th Spring Fling adelphia
244 Pennsylvania Win 13-4 1351.3 Mar 19th Spring Fling adelphia
165 Penn State-B Loss 13-14 973.45 Apr 1st 2023 B team Brodown
350 SUNY-Fredonia** Win 13-2 638.15 Ignored Apr 1st 2023 B team Brodown
177 Rochester Win 12-8 1481.34 Apr 1st 2023 B team Brodown
132 Ave Maria Loss 5-15 642.39 Apr 2nd 2023 B team Brodown
250 Shippensburg Win 12-6 1312.25 Apr 2nd 2023 B team Brodown
**Blowout Eligible

FAQ

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
  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
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)