#10 DiG (13-6)

avg: 1967.04  •  sd: 48.45  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
144 Club M - Magma** Win 15-1 1398.65 Ignored Jun 22nd Boston Invite 2019
68 Deathsquad Win 15-8 1774.08 Jun 22nd Boston Invite 2019
57 Red Circus Win 15-7 1882.69 Jun 22nd Boston Invite 2019
42 Shade Win 15-6 2010.03 Jun 23rd Boston Invite 2019
17 Sprout Win 14-12 1993.64 Jun 23rd Boston Invite 2019
26 Blueprint Win 15-5 2150.07 Jun 23rd Boston Invite 2019
33 Freaks Win 15-7 2099.59 Aug 17th TCT Elite Select Challenge 2019
11 Johnny Bravo Loss 13-14 1773.7 Aug 17th TCT Elite Select Challenge 2019
30 Black Market I Win 15-9 2050.86 Aug 17th TCT Elite Select Challenge 2019
16 Chain Lightning Win 11-9 2086.18 Aug 18th TCT Elite Select Challenge 2019
9 SoCal Condors Loss 8-10 1707.72 Aug 18th TCT Elite Select Challenge 2019
21 Brickyard Win 12-11 1812.79 Aug 18th TCT Elite Select Challenge 2019
18 Patrol Win 12-5 2338.32 Aug 18th TCT Elite Select Challenge 2019
7 Chicago Machine Loss 9-11 1759.77 Aug 31st TCT Pro Championships 2019
2 Truck Stop Loss 10-12 1931.45 Aug 31st TCT Pro Championships 2019
3 PoNY Loss 11-15 1786.85 Aug 31st TCT Pro Championships 2019
4 Ring of Fire Loss 14-15 2040.85 Sep 1st TCT Pro Championships 2019
14 Doublewide Win 13-10 2200.43 Sep 1st TCT Pro Championships 2019
12 Pittsburgh Temper Win 15-14 2014.86 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)