#29 Texas-Dallas (10-12)

avg: 1771.91  •  sd: 58.24  •  top 16/20: 3.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
5 Cal Poly-SLO Loss 6-13 1544.46 Jan 26th Santa Barbara Invite 2019
100 California-Santa Cruz Win 13-9 1777.33 Jan 26th Santa Barbara Invite 2019
40 Dartmouth Win 13-6 2286.47 Jan 26th Santa Barbara Invite 2019
51 Western Washington Win 13-10 1957.91 Jan 26th Santa Barbara Invite 2019
50 Stanford Loss 11-13 1403.9 Jan 27th Santa Barbara Invite 2019
19 Colorado State Loss 9-10 1774.55 Jan 27th Santa Barbara Invite 2019
51 Western Washington Win 11-10 1754.76 Jan 27th Santa Barbara Invite 2019
2 Brown Loss 10-12 1991.03 Feb 8th Florida Warm Up 2019
72 Alabama-Huntsville Win 13-7 2041.52 Feb 8th Florida Warm Up 2019
48 Kennesaw State Win 13-9 2065.05 Feb 8th Florida Warm Up 2019
136 South Florida Win 12-3 1837.03 Feb 9th Florida Warm Up 2019
15 Central Florida Loss 11-12 1865.32 Feb 9th Florida Warm Up 2019
106 Illinois State Win 9-8 1452.34 Feb 9th Florida Warm Up 2019
28 Northeastern Win 10-7 2165.5 Feb 9th Florida Warm Up 2019
4 Pittsburgh Loss 10-12 1946.8 Feb 10th Florida Warm Up 2019
18 Michigan Loss 9-10 1783.77 Feb 10th Florida Warm Up 2019
46 Iowa State Loss 11-14 1345.9 Mar 16th Centex 2019 Men
27 LSU Loss 10-11 1652.74 Mar 16th Centex 2019 Men
8 Colorado Loss 9-13 1676.88 Mar 16th Centex 2019 Men
19 Colorado State Win 11-9 2148.76 Mar 16th Centex 2019 Men
37 Illinois Loss 12-13 1595.39 Mar 17th Centex 2019 Men
76 Utah Loss 12-14 1252.77 Mar 17th Centex 2019 Men
**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)