#277 Texas-San Antonio (3-8)

avg: 466.7  •  sd: 68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
140 Arkansas Loss 1-9 443.15 Feb 1st Big D in lil d 2020 Open
124 Sul Ross State University** Loss 5-13 527.33 Ignored Feb 1st Big D in lil d 2020 Open
- Dallas Win 13-5 600 Ignored Feb 1st Big D in lil d 2020 Open
139 Texas Tech Loss 10-13 715.41 Feb 1st Big D in lil d 2020 Open
288 St John's Win 13-9 841.2 Feb 29th Mardi Gras XXXIII
144 Mississippi State Loss 6-13 420.85 Feb 29th Mardi Gras XXXIII
119 Emory** Loss 5-13 534.06 Ignored Feb 29th Mardi Gras XXXIII
69 Texas A&M** Loss 4-13 784.58 Ignored Feb 29th Mardi Gras XXXIII
237 Stephen F. Austin Loss 7-13 148.86 Mar 1st Mardi Gras XXXIII
341 LSU-B Win 13-2 600.52 Mar 1st Mardi Gras XXXIII
230 Sam Houston State Loss 4-13 136.85 Mar 1st Mardi Gras XXXIII
**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)