#93 Charleston Heat Stroke (17-10)

avg: 1291.36  •  sd: 33.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
134 Dyno Win 13-11 1255 Jul 8th Club Terminus 2023
61 Lost Boys Loss 8-10 1203.27 Jul 8th Club Terminus 2023
56 Little Red Wagon Loss 9-12 1152.55 Jul 8th Club Terminus 2023
120 El Niño Win 11-10 1216.7 Jul 9th Club Terminus 2023
150 Nashville Mudcats Win 13-10 1254.34 Jul 9th Club Terminus 2023
85 Space Cowboys Win 11-9 1561.51 Jul 9th Club Terminus 2023
114 Bloom Win 11-10 1274.31 Jul 22nd Filling the Void 2023
40 Cash Crop 2 Loss 8-13 1124.21 Jul 22nd Filling the Void 2023
138 Queen City Kings Win 12-11 1120.15 Jul 22nd Filling the Void 2023
210 Oakay Win 13-6 1183.18 Jul 22nd Filling the Void 2023
35 baNC Loss 7-15 1044.34 Jul 23rd Filling the Void 2023
121 John Doe Win 12-7 1609.86 Jul 23rd Filling the Void 2023
101 Triumph Win 8-7 1337.73 Jul 23rd Filling the Void 2023
37 Alliance Loss 9-11 1378.17 Aug 5th Trestlemania V
134 Dyno Win 10-9 1151.16 Aug 5th Trestlemania V
144 Music City Mafia Win 12-5 1565.1 Aug 5th Trestlemania V
150 Nashville Mudcats Win 10-7 1315.86 Aug 5th Trestlemania V
134 Dyno Win 9-7 1305.49 Aug 6th Trestlemania V
150 Nashville Mudcats Win 7-2 1526.2 Aug 6th Trestlemania V
64 Hooch Loss 9-11 1199.36 Aug 6th Trestlemania V
144 Music City Mafia Win 13-11 1193.94 Sep 9th 2023 Mens East Coast Sectional Championship
61 Lost Boys Loss 11-13 1237.09 Sep 9th 2023 Mens East Coast Sectional Championship
56 Little Red Wagon Loss 11-12 1372.91 Sep 9th 2023 Mens East Coast Sectional Championship
85 Space Cowboys Loss 10-12 1074.18 Sep 9th 2023 Mens East Coast Sectional Championship
116 Atlanta Arson Win 15-7 1743.89 Sep 10th 2023 Mens East Coast Sectional Championship
134 Dyno Win 15-14 1151.16 Sep 10th 2023 Mens East Coast Sectional Championship
64 Hooch Loss 12-13 1323.57 Sep 10th 2023 Mens East Coast 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)