#243 NYWT (1-11)

avg: 114.61  •  sd: 113.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
29 Storm** Loss 1-13 1075.01 Ignored Aug 5th Philly Open 2023
93 Brackish** Loss 3-13 497.19 Ignored Aug 5th Philly Open 2023
154 ColorBomb Loss 5-10 244.8 Aug 5th Philly Open 2023
232 Voltage Loss 7-13 -308.66 Aug 5th Philly Open 2023
202 Spice Loss 9-10 410.71 Aug 6th Philly Open 2023
207 Buffalo Brain Freeze Loss 8-11 110.78 Aug 6th Philly Open 2023
157 NY Swipes Loss 6-13 212.66 Aug 26th The Incident 2023
226 The Incidentals Loss 5-13 -272.69 Aug 26th The Incident 2023
189 Starfire Loss 4-13 8.1 Aug 27th The Incident 2023
216 Brooklyn Hive Loss 9-12 72.63 Aug 27th The Incident 2023
98 Farm Show** Loss 4-13 473.83 Ignored Aug 27th The Incident 2023
226 The Incidentals Win 12-10 565.43 Aug 27th The Incident 2023
**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)