#202 Spice (7-18)

avg: 535.71  •  sd: 60.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
163 Espionage Win 8-4 1344.31 Jun 24th Seven Cities Show Down
197 Swampbenders Loss 7-9 278.85 Jun 24th Seven Cities Show Down
93 Brackish Loss 6-11 550.5 Jun 24th Seven Cities Show Down
106 Ant Madness Loss 3-11 441.14 Jun 24th Seven Cities Show Down
105 Legion Loss 7-10 654.31 Jun 24th Seven Cities Show Down
46 Revival** Loss 5-15 853.7 Ignored Jun 25th Seven Cities Show Down
180 District Cocktails Loss 9-10 540.86 Jun 25th Seven Cities Show Down
122 Magnanimouse Loss 3-15 381.28 Jun 25th Seven Cities Show Down
130 904 Shipwreck Loss 9-10 783.32 Jul 8th Summer Glazed Daze 2023
96 Bear Jordan Loss 9-11 826.79 Jul 8th Summer Glazed Daze 2023
152 Verdant Loss 5-13 224.5 Jul 8th Summer Glazed Daze 2023
85 Too Much Fun Loss 9-11 901.31 Jul 8th Summer Glazed Daze 2023
217 Flood Zone Win 15-4 1009.59 Jul 9th Summer Glazed Daze 2023
139 Goosebumps Loss 5-11 270.45 Aug 5th Philly Open 2023
26 Loco** Loss 2-13 1096.53 Ignored Aug 5th Philly Open 2023
157 NY Swipes Loss 5-12 212.66 Aug 5th Philly Open 2023
207 Buffalo Brain Freeze Win 12-9 821.75 Aug 5th Philly Open 2023
243 NYWT Win 10-9 239.61 Aug 6th Philly Open 2023
220 Compost Plates Win 9-8 512.94 Aug 6th Philly Open 2023
250 Vanguard and Friends** Win 12-5 516.33 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
93 Brackish Loss 7-12 576.68 Sep 9th 2023 Mixed Capital Sectional Championship
116 One More Year Loss 3-13 417.86 Sep 9th 2023 Mixed Capital Sectional Championship
67 HVAC** Loss 5-13 662.34 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
251 Pumphouse** Win 14-3 391.33 Ignored Sep 10th 2023 Mixed Capital Sectional Championship
148 Heavy Flow Loss 4-15 235.15 Sep 10th 2023 Mixed Capital 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)