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Intermediate

Interpreted Value

Also known as: AI interpretation, AI-inferred value

Definition

An interpreted value is an AI judgment or label about raw data, like 'likely timing issue' or 'probable hip stall'. It is not a direct measurement or a rule's output.

Beyond measured facts and rule-based (heuristic) estimates, some findings in SwingVantage come from AI pattern-matching. The AI looks at combinations of signals that the rules engine alone cannot fully resolve. These are interpreted values. They are more contextual and nuanced than a deterministic rule output. But they are also inherently less precise and more subject to AI error. They are labeled 'AI Interpreted' or 'AI-Inferred'. They always come with the evidence that led to them, so you can decide how much weight to give them.

Beginner tip

AI-interpreted findings require more skepticism than deterministic findings. Before changing something based on an interpreted value, verify the underlying evidence: look at the cited frames in the timeline and confirm that the cue the AI described is actually visible.

Advanced note

Treat AI interpretations as hypotheses to test, not verdicts to act on immediately. Drill the suggested fix and run a retest to see if the interpretation was directionally correct. A positive retest delta validates the interpretation. An inconclusive delta means the interpretation was speculative.

Example

'AI Interpreted: possible mental tempo disruption — the downswing initiated 40ms faster than your average, suggesting a rushing tendency under pressure (Moderate confidence).'

How it shows up on video

AI-interpreted findings in Motion Lab appear with a purple [AI] badge and include the specific observable cues that triggered the interpretation — for example, "downswing tempo 18% faster than your average — interpreted as rushing under pressure." Always verify the cited cue is visible in the timeline before acting on the interpretation.

Common mistakes

  • Treating an AI-interpreted finding as equally authoritative as a deterministic finding — interpretations are inferences from patterns; deterministic findings are direct rule-engine outputs from measured positions.
  • Acting immediately on an AI interpretation without running a drill block to test whether it is correct — interpretations are hypotheses. Treat them as such until a retest confirms the direction.
  • Ignoring the evidence panel that accompanies each interpretation. The AI always shows what triggered the interpretation. Reviewing this evidence is how you decide whether to accept or question the finding.
  • Not flagging interpretations that seem inconsistent with your feel or prior coaching — inconsistencies are important diagnostic signals that the AI may have interpreted a normal variation as a fault.

In SwingVantage Motion Lab

In Motion Lab, clicking an AI-interpreted finding expands the evidence panel showing the specific landmark data and timing patterns that drove the interpretation. This transparency differs from estimated values — interpretations explain their reasoning explicitly, which you can evaluate and agree or disagree with.

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