#89 Tarleton State (9-1)

avg: 1287.1  •  sd: 89.66  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
243 North Texas** Win 10-4 1254.35 Ignored Mar 9th Centex Tier 2 2024
237 Rice** Win 13-5 1276.68 Ignored Mar 9th Centex Tier 2 2024
136 Houston Loss 14-15 982.21 Mar 10th Centex Tier 2 2024
212 San Diego State Win 15-13 985.1 Mar 10th Centex Tier 2 2024
172 Texas-San Antonio Win 15-11 1321.46 Mar 10th Centex Tier 2 2024
332 Dallas** Win 15-6 757.98 Ignored Mar 23rd Huckfest 2024
300 Texas A&M-B** Win 15-3 951.81 Ignored Mar 23rd Huckfest 2024
218 Texas-Dallas Win 15-9 1273.33 Mar 23rd Huckfest 2024
75 Ave Maria Win 14-10 1758.28 Mar 24th Huckfest 2024
221 Baylor Win 14-5 1339.74 Mar 24th Huckfest 2024
**Blowout Eligible


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)