Collegiate tennis model shows why overall accuracy can conceal weak injury detection

Frame the customer problem as evaluating whether readiness analytics can support review without overwhelming staff. Engage the athletic trainer, tennis coach, athlete representatives and athletics IT. Ask how actual injuries are labeled, what triggers a review and how many alerts staff can handle. Offer a bounded retrospective data-and-evaluation assessment with an explicit stop/go decision. The value hypothesis is better evidence for purchasing and workflow design. Do not promise fewer injuries, competitive gains or savings from the reported accuracy. Applicability is strongest for programs able to support careful tennis-specific evaluation.

Build a restricted research workspace with versioned inputs and independently adjudicated outcome labels before any live integration. Establish permitted exports, identifier mapping and timestamp alignment. Fit preprocessing on training data only; use future-period and held-out-athlete tests, with a simple baseline and confidence intervals. Examine confusion matrices and threshold-specific alert volume rather than selecting on accuracy alone. Treat insufficient injury events as a reason to defer conclusions. Cloud, on-premises or hybrid placement should follow institutional data controls; the paper does not establish a preferred hosting model. Do not expose identifiable records through a general-purpose copilot.

Assign a sports-medicine owner and a data engineer, with athlete representation at design review. First document consent, access, retention and escalation; then pilot in shadow mode. Train reviewers to record disagreement and correction reasons. Proposed acceptance criteria are complete provenance for every evaluated prediction, zero unauthorized exports, a reproducible held-out evaluation and an agreed maximum alert workload. Set performance thresholds with the clinical owner before testing. These are proposed gates, not observed results. Key dependencies are reliable outcome labels and staff review time; key risks are false reassurance, unnecessary restrictions and coercive participation.