#70 Case Western Reserve (14-7)

avg: 1366.71  •  sd: 68.85  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
96 Connecticut Win 10-9 1374.4 Jan 27th Mid Atlantic Warm Up
68 James Madison Win 12-10 1615.02 Jan 27th Mid Atlantic Warm Up
208 Virginia Commonwealth Win 12-5 1385.38 Jan 27th Mid Atlantic Warm Up
111 SUNY-Binghamton Loss 6-10 695.56 Jan 27th Mid Atlantic Warm Up
123 Pennsylvania Win 9-8 1272.48 Jan 27th Mid Atlantic Warm Up
142 Boston University Win 13-5 1668.71 Jan 28th Mid Atlantic Warm Up
73 Richmond Win 14-12 1585.21 Jan 28th Mid Atlantic Warm Up
154 Harvard Loss 10-14 624.48 Feb 10th Queen City Tune Up 2024
36 North Carolina-Charlotte Loss 10-13 1290.12 Feb 10th Queen City Tune Up 2024
61 William & Mary Loss 10-12 1193.88 Feb 10th Queen City Tune Up 2024
16 Penn State Loss 9-15 1405.75 Feb 10th Queen City Tune Up 2024
106 Notre Dame Win 15-12 1510.81 Feb 11th Queen City Tune Up 2024
72 Georgetown Win 11-6 1912.4 Feb 11th Queen City Tune Up 2024
184 George Mason Win 12-5 1476.05 Mar 30th East Coast Invite 2024
107 Princeton Loss 9-12 863.24 Mar 30th East Coast Invite 2024
169 Rutgers Win 13-6 1551.64 Mar 30th East Coast Invite 2024
98 Dartmouth Win 13-12 1370.64 Mar 30th East Coast Invite 2024
113 Syracuse Win 12-8 1629.99 Mar 31st East Coast Invite 2024
146 Yale Win 11-9 1309.26 Mar 31st East Coast Invite 2024
123 Pennsylvania Win 13-7 1705.01 Mar 31st East Coast Invite 2024
25 McGill Loss 6-13 1174.26 Mar 31st East Coast Invite 2024
**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)