#42 Houndd (12-1)

avg: 1648.86  •  sd: 120.46  •  top 16/20: 0.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
103 Black Lung Win 13-6 1528.57 Jun 21st SCINNY 2025
122 Hazard Win 13-6 1387.22 Jun 21st SCINNY 2025
181 Maumee Boys** Win 13-5 669.42 Ignored Jun 21st SCINNY 2025
79 Chimney Win 12-7 1655.19 Jun 22nd SCINNY 2025
67 Endgame Win 12-7 1769.4 Jun 22nd SCINNY 2025
172 Enigma** Win 13-1 840.84 Ignored Jun 22nd SCINNY 2025
91 Alibi Win 13-6 1667.35 Jul 12th Boston Invite 2025
92 Crypt Win 13-7 1615.41 Jul 12th Boston Invite 2025
156 MBTA** Win 13-4 1073.43 Ignored Jul 12th Boston Invite 2025
96 Skeleton Squad Win 13-9 1424.49 Jul 12th Boston Invite 2025
40 Colt Win 15-11 2042.99 Jul 13th Boston Invite 2025
54 Lantern Win 15-9 1901.25 Jul 13th Boston Invite 2025
26 Red Tide Loss 10-15 1459.08 Jul 13th Boston Invite 2025
**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)