#12 Pittsburgh Temper (11-7)

avg: 1889.86  •  sd: 63.79  •  top 16/20: 90.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
57 Red Circus Win 13-7 1840.22 Jul 27th TCT Select Flight Invite East 2019
30 Black Market I Win 13-7 2092.91 Jul 27th TCT Select Flight Invite East 2019
24 Brickhouse Win 13-8 2064.36 Jul 27th TCT Select Flight Invite East 2019
25 General Strike Loss 14-15 1439.89 Jul 28th TCT Select Flight Invite East 2019
57 Red Circus** Win 13-4 1882.69 Ignored Jul 28th TCT Select Flight Invite East 2019
36 Nitro Win 13-10 1791.55 Jul 28th TCT Select Flight Invite East 2019
39 Inception Win 15-6 2014.7 Aug 17th TCT Elite Select Challenge 2019
21 Brickyard Win 15-11 2068.96 Aug 17th TCT Elite Select Challenge 2019
19 Voodoo Win 15-6 2335.56 Aug 17th TCT Elite Select Challenge 2019
11 Johnny Bravo Loss 8-11 1533.1 Aug 18th TCT Elite Select Challenge 2019
15 Rhino Slam! Win 12-10 2091.67 Aug 18th TCT Elite Select Challenge 2019
18 Patrol Win 9-7 2017.66 Aug 18th TCT Elite Select Challenge 2019
6 Sub Zero Loss 12-14 1842.29 Aug 31st TCT Pro Championships 2019
14 Doublewide Loss 6-15 1272.29 Aug 31st TCT Pro Championships 2019
4 Ring of Fire Loss 8-13 1669.69 Aug 31st TCT Pro Championships 2019
10 DiG Loss 14-15 1842.04 Sep 1st TCT Pro Championships 2019
2 Truck Stop Win 13-12 2294.57 Sep 1st TCT Pro Championships 2019
3 PoNY Loss 12-14 1947.06 Sep 1st TCT Pro Championships 2019
**Blowout Eligible

FAQ

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
  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
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)