#99 SOUF (10-15)

avg: 1255.01  •  sd: 59.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 Blackfish Loss 6-11 1119.89 Jun 24th Summer Solstice 2023
145 Green River Swordfish Loss 8-13 468.1 Jun 24th Summer Solstice 2023
112 Heartbreak Loss 9-10 1032.29 Jun 24th Summer Solstice 2023
182 Anchor Win 11-7 1252.8 Jun 25th Summer Solstice 2023
193 ONI Win 13-8 1220.86 Jun 25th Summer Solstice 2023
141 Make it Rain Win 10-9 1096.54 Jun 25th Summer Solstice 2023
78 Drought Win 13-12 1499.11 Jul 15th TCT Select Flight West 2023
54 ISO Atmo Loss 5-10 945.45 Jul 15th TCT Select Flight West 2023
69 Clutch Loss 11-12 1285.39 Jul 15th TCT Select Flight West 2023
69 Clutch Loss 7-13 852.86 Jul 16th TCT Select Flight West 2023
70 OAT Loss 9-15 883.98 Jul 16th TCT Select Flight West 2023
58 Skipjack Loss 7-15 890.71 Jul 16th TCT Select Flight West 2023
81 Surf Loss 11-13 1121.13 Sep 9th 2023 Mens Washington Sectional Championship
87 Ghost Train Loss 10-13 981.26 Sep 9th 2023 Mens Washington Sectional Championship
59 Jen City Executives Win 14-13 1612.77 Sep 9th 2023 Mens Washington Sectional Championship
81 Surf Win 12-10 1588.09 Sep 10th 2023 Mens Washington Sectional Championship
112 Heartbreak Win 13-12 1282.29 Sep 10th 2023 Mens Washington Sectional Championship
136 Kalakala Wiffleball Club Win 15-10 1466.12 Sep 10th 2023 Mens Washington Sectional Championship
67 Mystery Gang Loss 9-15 910.43 Sep 23rd 2023 Northwest Mens Regional Championship
49 Shrimp Win 13-12 1680.23 Sep 23rd 2023 Northwest Mens Regional Championship
14 Sockeye Loss 7-15 1399.88 Sep 23rd 2023 Northwest Mens Regional Championship
10 Rhino Slam! Loss 7-15 1485.8 Sep 24th 2023 Northwest Mens Regional Championship
65 Sawtooth Loss 14-15 1318.15 Sep 24th 2023 Northwest Mens Regional Championship
60 Switchback Win 14-13 1600.77 Sep 24th 2023 Northwest Mens Regional Championship
11 Furious George** Loss 5-15 1457.44 Ignored Sep 24th 2023 Northwest Mens Regional Championship
**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)