#8 Johnny Bravo (10-7)

avg: 2112.77  •  sd: 63.19  •  top 16/20: 99.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 Omen Win 15-12 2040.28 Aug 4th 2023 US Open Club Championships ICC
2 PoNY Loss 9-15 1818.45 Aug 4th 2023 US Open Club Championships ICC
10 Rhino Slam! Win 15-12 2386.3 Aug 5th 2023 US Open Club Championships ICC
1 Truck Stop Loss 11-15 2118.06 Aug 5th 2023 US Open Club Championships ICC
5 Chicago Machine Loss 13-15 1975.53 Aug 6th 2023 US Open Club Championships ICC
3 Revolver Loss 13-15 2031.12 Sep 2nd TCT Pro Championships 2023
4 Chain Lightning Loss 13-14 2086.89 Sep 2nd TCT Pro Championships 2023
13 Vault Win 15-13 2218.54 Sep 2nd TCT Pro Championships 2023
2 PoNY Loss 11-14 2020.6 Sep 2nd TCT Pro Championships 2023
10 Rhino Slam! Win 15-9 2601.28 Sep 3rd TCT Pro Championships 2023
9 Doublewide Win 15-11 2487.98 Sep 3rd TCT Pro Championships 2023
6 Ring of Fire Win 15-14 2305.66 Sep 4th TCT Pro Championships 2023
77 BARNSTORM** Win 15-4 1974.75 Ignored Sep 23rd 2023 South Central Mens Regional Championship
50 H.I.P Win 15-7 2153.51 Sep 23rd 2023 South Central Mens Regional Championship
153 Sprawl** Win 15-3 1498.87 Ignored Sep 23rd 2023 South Central Mens Regional Championship
9 Doublewide Loss 8-12 1665.66 Sep 24th 2023 South Central Mens Regional Championship
50 H.I.P Win 15-12 1854 Sep 24th 2023 South Central Mens Regional 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)