Olympic Coaching Effect Analysis with Half-Life Estimates

A 5.5 KB XLS file contains power analysis results for the effect sizes of great coaches on Olympic performance. The study by You Liu applies a Change-Point Driven Difference-in-Differences with Decay Model to quantify coaching impacts. It includes specific predictions, such as a 5.3-year half-life for technology-driven interventions and a forecast of 5 medals for the Brazilian swimming team in 2028.

Use Cases

• Validate the half-life model estimates for systematic coaching systems versus technology-driven interventions using the provided effect size data.
• Apply the Change-Point Driven Difference-in-Differences with Decay Model methodology to replicate the analysis of dynamic confounding bias.
• Use the forecasted medal count for the Brazilian swimming team as a benchmark for evaluating other coaching resource allocation frameworks.

Strengths

• Provides concrete numerical estimates, including a 20+ year half-life for systematic coaching and a 5.3-year half-life for AI-driven interventions.
• Includes a specific prediction for the 2028 Olympics: the Brazilian swimming team achieving 5 medals (95% CI: 4.7–6.3).
• Dataset is openly licensed under CC BY 4.0.

Limitations

• Very small dataset at only 5.5 KB, indicating limited raw data or analysis outputs.
• Unknown row and column structure prevents assessment of sample size or feature breadth.
• Methodological results may be highly specific to the study's model and not easily generalizable without the full analytical context.

Provenance

Source

figshare, authored by You Liu.

Collection Method

Analysis output from a proposed Change-Point Driven Difference-in-Differences with Decay Model applied to Olympic coaching effects.

Time Range

null

Freshness

Last updated March 13, 2026.

Geography

Includes case studies on Chinese diving and Japanese judo, with a prediction for Brazilian swimming.