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. 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.
If you know you topped a shot or stumbled on a swing, note it as an outlier when reviewing the session rather than letting it drag down the session average — one bad swing in ten is a noise event, not a fault pattern.
Use repeated outlier positions as diagnostic information rather than dismissing them: if the same anomalous landmark position appears reliably under specific conditions (fatigue, certain clubs), it may be a compensatory pattern worth investigating as a root cause.
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.
How it shows up on video
An outlier swing is often visible in video as an obviously different movement pattern. A swing where the backswing was cut short, a stumble occurred, or the setup was clearly different from the rest of the session. Identify outliers visually before relying on the automated outlier flag.
Common mistakes
- Including obvious outlier swings (stumbled, topped, setup error) in the session average without flagging them — one extreme outlier can shift the session average by several degrees.
- Assuming all automatically flagged outliers are tracking errors — some flagged swings are genuine unusual movements that represent a real compensatory pattern worth investigating.
- Not reviewing the outlier frame in Motion Lab before deciding to exclude or include. The automated flag is a suggestion, not a final decision. Always check the raw video frame.
- Dismissing repeated outliers at the same position in the swing as "just errors" — if the same anomalous position appears reliably under fatigue or specific clubs, it is a pattern, not noise.
In SwingVantage Motion Lab
Motion Lab flags outlier frames with a yellow border on the skeleton overlay timeline. Click the flagged frame to see the raw skeleton and decide whether the outlier was a genuine (if unusual) swing or a tracking artifact that should be 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. This is 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. This produces the skeleton that SwingVantage uses to measure angles and movement patterns.
Put this into your swing
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