#58 Garbage Plates (20-4)

avg: 1317.32  •  sd: 53.2  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
207 Buffalo Brain Freeze Win 13-8 972.55 Jun 24th LVU’s Disc Days of Summer 2023
149 FLI Win 10-8 1092.49 Jun 24th LVU’s Disc Days of Summer 2023
68 Heat Wave Win 10-8 1520.71 Jun 24th LVU’s Disc Days of Summer 2023
148 Heavy Flow Win 10-9 960.15 Jun 24th LVU’s Disc Days of Summer 2023
83 Buffalo Lake Effect Loss 5-8 707.04 Jun 25th LVU’s Disc Days of Summer 2023
186 Crucible** Win 12-2 1215.92 Ignored Jun 25th LVU’s Disc Days of Summer 2023
57 Greater Baltimore Anthem Win 8-7 1443.14 Jun 25th LVU’s Disc Days of Summer 2023
142 PS Win 12-9 1201.87 Aug 5th Philly Open 2023
207 Buffalo Brain Freeze** Win 12-3 1076.39 Ignored Aug 5th Philly Open 2023
232 Voltage** Win 13-2 848.87 Ignored Aug 5th Philly Open 2023
163 Espionage Win 13-4 1379.5 Aug 6th Philly Open 2023
29 Storm Loss 6-11 1128.31 Aug 6th Philly Open 2023
83 Buffalo Lake Effect Win 12-5 1760.64 Aug 6th Philly Open 2023
163 Espionage Win 12-9 1124.87 Aug 19th Philly Invite 2023
148 Heavy Flow Win 15-9 1350.63 Aug 19th Philly Invite 2023
49 Jughandle Win 13-11 1635.61 Aug 19th Philly Invite 2023
57 Greater Baltimore Anthem Win 15-13 1532.32 Aug 20th Philly Invite 2023
68 Heat Wave Loss 9-11 1008.84 Aug 20th Philly Invite 2023
49 Jughandle Win 11-10 1531.77 Aug 20th Philly Invite 2023
207 Buffalo Brain Freeze** Win 13-4 1076.39 Ignored Sep 9th 2023 Mixed Upstate New York Sectional Championship
- Crash Win 12-7 1288.86 Sep 9th 2023 Mixed Upstate New York Sectional Championship
72 Townies Win 12-11 1361.04 Sep 9th 2023 Mixed Upstate New York Sectional Championship
38 UNION Loss 10-13 1218.55 Sep 10th 2023 Mixed Upstate New York Sectional Championship
72 Townies Win 14-10 1634.74 Sep 10th 2023 Mixed Upstate 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)