Outlier in Swing Data
Also known as: outlier, anomalous shot
An outlier is a data point that falls far outside the normal range for that metric and session — it may represent a genuine extreme swing or a measurement artifact, and it requires honest handling before being used in averages.
Outliers affect averages disproportionately. A single 130 mph club speed reading in a session of 95 mph swings is almost certainly a tracking error, not a real swing. SwingVantage identifies statistical outliers using interquartile range methods, flags them for review, and excludes them from session averages unless there is evidence they are real. Flagged outliers are shown in the session detail so you can inspect the relevant frame rather than having them silently distort your numbers.
Example
A session of ten swings shows nine paths clustered between +2 and +5 degrees, and one at -14 degrees — flagged as a likely tracking artifact and excluded from the session average.
Related terms
- Signal vs NoiseSignal is the real, repeatable pattern in your swing data; noise is the random variation that looks like a pattern but isn't. Distinguishing the two is what separates useful analysis from false precision.
- Sample SizeSample size is how many swings or shots a metric is based on — small samples produce more noise and lower confidence; larger samples produce more reliable estimates.
- Consistency ScoreA consistency score measures how tightly grouped your metrics are across multiple swings — low variance produces a high score, because consistency is often more valuable than peak performance.
- Pose EstimationPose estimation is the computer-vision process that detects the positions of major body joints (keypoints) in each video frame, producing the skeleton that SwingVantage uses to measure angles and movement patterns.
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