Olympic Coaching Impact Analysis with Decay Model

Empirical studies apply a Change-Point Driven Difference-in-Differences with Decay Model to quantify the effect of great coaches on Olympic performance. The model reveals effect half-lives, such as over 20 years for systematic coaching systems and 5.3 years for technology-driven interventions, and predicts medal breakthroughs for specific teams.

Use Cases

• Apply the Change-Point Driven Difference-in-Differences with Decay Model to quantify the net effect of heterogeneous coach turnover events.
• Analyze the sustainability difference of coaching effects using the half-life model on performance data.
• Use the improved CUSUM algorithm with a dual test mechanism to detect abrupt performance points while filtering pseudo-abrupt signals like the host effect.

Strengths

• Methodology integrates CUSUM, dynamic DID, and exponential decay functions for systematic analysis.
• Model provides specific quantitative predictions, such as a forecast of 5 medals for the Brazilian swimming team in 2028.

Limitations

• Dataset is extremely small at 5.5 KB, indicating limited raw data or results.
• Input lacks column definitions and sample data, limiting direct analytical application.
• Empirical scope is focused on Olympic case studies, not a broad panel dataset.

Provenance

Source

figshare, author You Liu.

Collection Method

Proposed modeling framework applied to Olympic performance case studies.

Time Range

null

Freshness

Last updated March 2026.

Geography

Includes case studies on Chinese diving and Japanese judo teams.