#59 Penn State (9-10)

avg: 1301.24  •  sd: 62.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
32 SUNY-Binghamton Loss 8-13 1155.43 Feb 25th Commonwealth Cup Weekend2 2023
89 Columbia Win 10-8 1341.32 Feb 25th Commonwealth Cup Weekend2 2023
10 Northeastern** Loss 3-13 1534.33 Ignored Feb 25th Commonwealth Cup Weekend2 2023
14 Virginia Loss 8-12 1489.51 Feb 25th Commonwealth Cup Weekend2 2023
32 SUNY-Binghamton Loss 3-12 1051.59 Feb 26th Commonwealth Cup Weekend2 2023
35 Michigan Win 11-10 1744.57 Feb 26th Commonwealth Cup Weekend2 2023
19 Yale Loss 5-13 1186.06 Feb 26th Commonwealth Cup Weekend2 2023
130 Liberty Win 13-6 1373.74 Mar 25th Rodeo 2023
21 North Carolina State Loss 4-13 1156.31 Mar 25th Rodeo 2023
58 Williams Win 12-7 1845.28 Mar 25th Rodeo 2023
30 South Carolina Loss 9-11 1411.59 Mar 26th Rodeo 2023
28 Duke Loss 9-13 1263.47 Mar 26th Rodeo 2023
144 North Carolina-B Win 13-8 1156.93 Mar 26th Rodeo 2023
151 Rutgers Win 8-4 1170.32 Apr 1st Shady Encounters
111 Lehigh Loss 7-8 793.2 Apr 1st Shady Encounters
61 Vermont-B Loss 4-7 790.38 Apr 1st Shady Encounters
97 NYU Win 4-2 1506.37 Apr 2nd Shady Encounters
67 Mount Holyoke Win 8-7 1392.7 Apr 2nd Shady Encounters
61 Vermont-B Win 9-7 1565.88 Apr 2nd Shady Encounters
**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)