#68 Heat Wave (13-9)

avg: 1258.05  •  sd: 65.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
207 Buffalo Brain Freeze** Win 12-4 1076.39 Ignored Jun 24th LVU’s Disc Days of Summer 2023
149 FLI Win 8-7 954.82 Jun 24th LVU’s Disc Days of Summer 2023
186 Crucible** Win 13-3 1215.92 Ignored Jun 24th LVU’s Disc Days of Summer 2023
58 Garbage Plates Loss 8-10 1054.66 Jun 24th LVU’s Disc Days of Summer 2023
83 Buffalo Lake Effect Loss 8-9 1035.64 Jun 25th LVU’s Disc Days of Summer 2023
148 Heavy Flow Win 10-8 1097.82 Jun 25th LVU’s Disc Days of Summer 2023
14 Slow Loss 3-13 1224.04 Jul 15th Boston Invite 2023
57 Greater Baltimore Anthem Loss 9-10 1193.14 Jul 15th Boston Invite 2023
48 Wild Card Loss 8-9 1316.83 Jul 15th Boston Invite 2023
70 League of Shadows Loss 6-10 750.84 Jul 15th Boston Invite 2023
40 Pittsburgh Port Authority Loss 12-14 1307.95 Aug 19th Philly Invite 2023
105 Legion Win 15-5 1643.98 Aug 19th Philly Invite 2023
213 Milk Win 13-6 1041.23 Aug 19th Philly Invite 2023
74 Deadweight Win 12-7 1728.98 Aug 20th Philly Invite 2023
40 Pittsburgh Port Authority Loss 9-14 1055.04 Aug 20th Philly Invite 2023
58 Garbage Plates Win 11-9 1566.53 Aug 20th Philly Invite 2023
189 Starfire** Win 13-5 1208.1 Ignored Sep 9th 2023 Mixed Metro New York Sectional Championship
216 Brooklyn Hive Win 11-7 884.89 Sep 9th 2023 Mixed Metro New York Sectional Championship
149 FLI Win 13-8 1325.98 Sep 9th 2023 Mixed Metro New York Sectional Championship
61 Funk Loss 5-10 736.07 Sep 9th 2023 Mixed Metro New York Sectional Championship
74 Deadweight Win 12-6 1787.78 Sep 10th 2023 Mixed Metro New York Sectional Championship
65 Grand Army Win 12-9 1619.51 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)