#165 Firefly TX (10-16)

avg: 858.17  •  sd: 55.28  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
149 Rawhide Loss 10-11 802.02 Jul 22nd Riverside Classic 2023
153 Sprawl Win 10-6 1395.03 Jul 22nd Riverside Classic 2023
122 Lil Heroes Loss 10-12 850.3 Jul 22nd Riverside Classic 2023
129 Foxtrot Loss 5-11 442.89 Jul 22nd Riverside Classic 2023
168 San Antonio Warhawks Win 15-14 956.23 Jul 23rd Riverside Classic 2023
153 Sprawl Win 15-10 1352.47 Jul 23rd Riverside Classic 2023
159 Choice City Hops Loss 12-13 759.65 Jul 23rd Riverside Classic 2023
102 Harvey Cats Loss 8-13 711.03 Aug 5th Dog Days of Summer 23
50 H.I.P** Loss 5-13 953.51 Ignored Aug 5th Dog Days of Summer 23
225 Forge Win 8-6 788.73 Aug 5th Dog Days of Summer 23
203 Shrimp Discs Win 11-10 777.03 Aug 5th Dog Days of Summer 23
254 Adventure Time** Win 13-5 566.76 Ignored Aug 6th Dog Days of Summer 23
69 Clutch Loss 7-8 1285.39 Aug 6th Dog Days of Summer 23
168 San Antonio Warhawks Loss 9-13 412.67 Aug 12th PBJ 2023
97 Texas Duffy Loss 8-13 785.99 Aug 12th PBJ 2023
129 Foxtrot Loss 7-10 653.23 Aug 12th PBJ 2023
125 Cowtown Cannons Loss 11-13 827.77 Aug 13th PBJ 2023
129 Foxtrot Win 12-11 1167.89 Aug 13th PBJ 2023
153 Sprawl Loss 7-9 619.53 Aug 13th PBJ 2023
222 RGV Tlacuaches Win 13-7 1064.3 Sep 9th 2023 Mens Texas Sectional Championship
48 Alamode Loss 6-13 956.94 Sep 9th 2023 Mens Texas Sectional Championship
125 Cowtown Cannons Loss 4-13 456.61 Sep 9th 2023 Mens Texas Sectional Championship
102 Harvey Cats Loss 8-13 711.03 Sep 9th 2023 Mens Texas Sectional Championship
203 Shrimp Discs Win 15-5 1252.03 Sep 10th 2023 Mens Texas Sectional Championship
129 Foxtrot Win 12-11 1167.89 Sep 10th 2023 Mens Texas Sectional Championship
153 Sprawl Loss 9-13 480.3 Sep 10th 2023 Mens Texas 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)