01
Does greater strength accompany greater speed?
- Variables
- Estimated squat 1RM × flying 10m, 40-yard, and 100m time
- Method
- Scatterplots, correlation, and simple regression
- Limitation
- Strength, technique, maturation, and recovery change together.
What can statistics solve in training?
Statistics does not turn every observation into a conclusion. It helps me frame a question, define variables, quantify uncertainty, and decide what evidence should come next.
02
Does training have a delayed effect?
- Variables
- Weekly lifting volume × performance 7, 14, and 28 days later
- Method
- Lagged comparisons and rolling averages
- Limitation
- A small personal dataset can suggest timing, not prove a mechanism.
03
Is a personal best a breakthrough or an outlier?
- Variables
- Repeated trials under similar conditions
- Method
- Mean, standard deviation, z-scores, and confidence intervals
- Limitation
- One exceptional result needs replication.
04
When does improvement begin to plateau?
- Variables
- Estimated 1RM, sprint time, and jump height across weeks
- Method
- Moving averages and change-point comparisons
- Limitation
- A plateau may reflect fatigue, measurement noise, or a real limit.
05
What changed around injury and return?
- Variables
- Load, RPE, sleep, pain notes, and performance before and after injury
- Method
- Segmented timelines and before–after summaries
- Limitation
- This documents association and is not medical or causal evidence.
06
Is the relationship between strength and speed linear?
- Variables
- Relative strength × sprint outcomes across the training history
- Method
- Linear and curved model comparison
- Limitation
- More complex models can easily overfit a small dataset.
THE ROLE OF MATHEMATICS
Sport gives me questions that numbers alone cannot answer. Statistics gives me a disciplined way to investigate them without pretending every change has a simple cause.