#25 Colorado Small Batch (12-10)

avg: 1459.47  •  sd: 63.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
9 Schwa Loss 7-14 1518.82 Jul 8th TCT Pro Elite Challenge West 2023
6 Flipside** Loss 4-15 1667.63 Ignored Jul 8th TCT Pro Elite Challenge West 2023
77 Portland Rain Check** Win 15-5 1010.41 Ignored Jul 8th TCT Pro Elite Challenge West 2023
27 Underground Win 10-8 1688.59 Jul 9th TCT Pro Elite Challenge West 2023
3 Fury** Loss 1-15 1854.65 Ignored Jul 9th TCT Pro Elite Challenge West 2023
11 Seattle Riot Loss 7-15 1384.62 Jul 9th TCT Pro Elite Challenge West 2023
9 Schwa** Loss 5-14 1501.71 Ignored Jul 9th TCT Pro Elite Challenge West 2023
12 Nemesis Loss 8-15 1366.66 Aug 19th TCT Elite Select Challenge 2023
11 Seattle Riot Loss 5-15 1384.62 Aug 19th TCT Elite Select Challenge 2023
29 Pop Loss 13-14 1277.13 Aug 19th TCT Elite Select Challenge 2023
27 Underground Loss 8-9 1300.92 Aug 20th TCT Elite Select Challenge 2023
54 Stellar Win 9-7 1156.28 Aug 20th TCT Elite Select Challenge 2023
34 Indy Rogue Win 13-8 1682.5 Aug 20th TCT Elite Select Challenge 2023
85 Colorado Cutthroat: Youth Club U-20 Girls** Win 13-2 853.81 Ignored Sep 9th 2023 Womens Rocky Mountain Sectional Championship
70 COSMOS** Win 13-2 1102.32 Ignored Sep 9th 2023 Womens Rocky Mountain Sectional Championship
71 Jackwagon** Win 13-4 1097.72 Ignored Sep 9th 2023 Womens Rocky Mountain Sectional Championship
49 Trainwreck Win 11-8 1299.67 Sep 9th 2023 Womens Rocky Mountain Sectional Championship
82 Venom** Win 15-4 878.67 Ignored Sep 23rd 2023 South Central Womens Regional
64 TWISTED** Win 12-4 1289.75 Ignored Sep 23rd 2023 South Central Womens Regional
4 Molly Brown** Loss 5-12 1734.03 Ignored Sep 23rd 2023 South Central Womens Regional
49 Trainwreck Win 14-3 1534.06 Sep 24th 2023 South Central Womens Regional
32 Crush City Win 11-6 1860.09 Sep 24th 2023 South Central Womens Regional
**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)