{"resourceId":"rice-sports-science-dashboard-forecasting-2026","versions":[{"version":"external-c01a586f3b8742ba177ae92fe407d3e33a6837b2b3f85208acac28c91b7d6856","resource":{"id":"rice-sports-science-dashboard-forecasting-2026","title":"Rice distinguishes an operating dashboard from a forecasting pilot","organization":"Rice University","sector":"Collegiate athletics","geography":"United States; Rice University","publishedAt":"March 18, 2026","publicationDate":"2026-03-18","eventDate":null,"sourceName":"Rice News","sourceLabel":"University operator account","sourceUrl":"https://news.rice.edu/news/2026/working-smarter-not-harder-rice-blends-academics-athletics-produce-cutting-edge-sports","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["knowledge-work","developers-agents","data-security","accessibility-workforce","operating-model"],"finding":"Rice describes a consolidated coaching dashboard and a separate machine-learning performance forecast under development.","sledRelevance":"Direct collegiate implementation evidence, newly filling the archive's sports-science workflow detail; no cross-stream tagging.","evidence":"The dashboard combines GPS, hydration and force-plate inputs. A coach reports easier data use. The forecasting pilot concerns football and future countermovement jumps. No sample size, accuracy, controlled baseline or measured injury reduction is reported.","architectureImplications":"Interpretation: separate ingestion, dashboard reporting and experimental prediction services; approve hosting and identity boundaries before integration.","governanceImplications":"Interpretation: set an explicit review boundary between descriptive outputs and consequential recommendations.","securityPrivacyImplications":"Interpretation: restrict identifiable performance data, audit access and prohibit unapproved uploads to general-purpose assistants.","caveats":"Institutional publicity and attributed experience, not independent evaluation. Operational reporting should not be counted as validated predictive AI.","streamIds":["college-athletics"],"roles":{"sales":"Interpretation: Address the problem of fragmented performance information before proposing prediction. Engage sports performance staff, coaches, athletics IT and athlete representatives. Ask which decisions are delayed, who reconciles conflicting inputs, and what evidence would justify adding a forecast. A bounded engagement could map one reporting workflow and compare a consolidated view with current practice. The credible value hypothesis is less reconciliation and clearer evidence at the point of use. Do not promise injury prevention, competitive advantage or quantified savings. Determine whether protected support time exists and whether the institution can retain responsibility when a student developer graduates.","engineering":"Interpretation: Fit is a governed analytical service with an experimental model kept separate from production reporting. Prerequisites include an approved data dictionary, consistent athlete identifiers, timestamps and documented sensor changes. Use role-based access and a restricted development environment; choose cloud, local or hybrid hosting according to institutional controls rather than this article. For a proof of value, benchmark the report workflow independently of forecast accuracy. Use temporal holdouts and athlete-level separation when testing prediction, compare with a simple baseline, and inspect missing data and subgroup errors. Do not permit a copilot or agent to change training plans automatically.","delivery":"Interpretation: A sports-performance service owner should coordinate coaches, IT and qualified data analysts. Start with data mapping and reconciliation, then conduct a limited usability pilot with a documented support handoff. Dependencies include approved access, historical data quality and staff availability. Review consent, downstream uses and model scope before any predictive trial. Train users to distinguish observations from estimates. Proposed acceptance criteria include reconciled values on an approved test set, successful access-revocation checks, measured report-preparation time and an assigned backup maintainer. Predictive deployment requires separately approved validation criteria. Risks include data drift, unsupported extrapolation and loss of project knowledge."},"retrievedAt":"2026-09-10T03:01:34Z","enrichedAt":"2026-09-10T03:03:18Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Interpretation: make charts understandable without color alone and arrange durable support beyond student project participation.","procurementImplications":"Interpretation: require export rights, retention controls and a funded maintenance plan before acquiring a predictive layer.","operatingModelImplications":"Interpretation: assign athletics ownership of the service and IT ownership of production support.","updateExplanation":"URL absent from all 151 archive resources scanned at offsets 0 and 100. Historical evidence adds a concrete distinction between descriptive workflow adoption and predictive development; no new-since-last-run release is claimed.","sourceVerification":{"openedUrl":"https://news.rice.edu/news/2026/working-smarter-not-harder-rice-blends-academics-athletics-produce-cutting-edge-sports","referenceExcerpt":"She’s piloting it with football, which has the largest dataset, with plans to expand to other sports.","promptVersion":"sled-research-v3.1","model":null,"basis":"agent-reported inspection"}}}]}