#157 NY Swipes (13-13)

avg: 812.66  •  sd: 47.8  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
202 Spice Win 12-5 1135.71 Aug 5th Philly Open 2023
142 PS Loss 10-11 731.51 Aug 5th Philly Open 2023
26 Loco** Loss 3-13 1096.53 Ignored Aug 5th Philly Open 2023
139 Goosebumps Win 9-7 1149.79 Aug 5th Philly Open 2023
180 District Cocktails Win 13-4 1265.86 Aug 6th Philly Open 2023
93 Brackish Loss 7-10 707.53 Aug 6th Philly Open 2023
142 PS Win 13-10 1184.65 Aug 19th Philly Invite 2023
70 League of Shadows Loss 5-14 647 Aug 19th Philly Invite 2023
57 Greater Baltimore Anthem Loss 5-15 718.14 Aug 19th Philly Invite 2023
163 Espionage Win 11-9 1028.71 Aug 20th Philly Invite 2023
178 Philly Twist Win 12-10 910.19 Aug 20th Philly Invite 2023
148 Heavy Flow Loss 11-12 710.15 Aug 20th Philly Invite 2023
189 Starfire Loss 7-11 141.21 Aug 26th The Incident 2023
149 FLI Loss 9-10 704.82 Aug 26th The Incident 2023
226 The Incidentals Win 13-4 927.31 Aug 26th The Incident 2023
243 NYWT Win 13-6 714.61 Aug 26th The Incident 2023
98 Farm Show Loss 7-13 516.3 Aug 27th The Incident 2023
149 FLI Win 11-9 1079.03 Aug 27th The Incident 2023
61 Funk Loss 6-13 709.97 Aug 27th The Incident 2023
74 Deadweight Loss 3-13 608.47 Sep 9th 2023 Mixed Metro New York Sectional Championship
177 Eat Lightning Win 10-9 797.5 Sep 9th 2023 Mixed Metro New York Sectional Championship
65 Grand Army Loss 7-13 716.61 Sep 9th 2023 Mixed Metro New York Sectional Championship
158 Room Temperature Win 11-9 1056.71 Sep 9th 2023 Mixed Metro New York Sectional Championship
189 Starfire Win 10-9 733.1 Sep 10th 2023 Mixed Metro New York Sectional Championship
158 Room Temperature Win 13-12 932.5 Sep 10th 2023 Mixed Metro New York Sectional Championship
65 Grand Army Loss 4-15 674.14 Sep 10th 2023 Mixed Metro New York Sectional 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)