{"resourceId":"msu-athletics-ai-stats-system-every-sport-2026-10-06","versions":[{"version":"external-ad9a68c4b9e5340683e336974f6ba428eb1085ff20e884c92672c7be32442699","resource":{"id":"msu-athletics-ai-stats-system-every-sport-2026-10-06","title":"Mississippi State Athletics Communications scaled an AI-assisted stats pipeline from soccer to multi-sport, cutting turnaround from hours to minutes","organization":"Mississippi State University Athletics / MSU AI Hub","sector":"NCAA Division I athletics communications and statistics operations (SEC)","geography":"United States (Starkville, Mississippi)","publishedAt":"October 6, 2026","publicationDate":"2026-10-06","eventDate":null,"sourceName":"MSU AI Hub","sourceLabel":"University AI Hub case study","sourceUrl":"https://aihub.msstate.edu/app/news/brian-ogden-athletics-stats","evidenceClass":"vendor-claim","outcomeClass":"mixed","topics":["knowledge-work","developers-agents","operating-model","accessibility-workforce"],"finding":"MSU Athletics Communications used ChatGPT to draft Excel and Power Query formulas that pull and clean NCAA game data, then hit rate limits that risked account lockouts and still left full-player refreshes near an hour. Working with doctoral candidate Josh King in the athletics AI & Data Science Lab, staff routed underlying data files straight into spreadsheets, cutting refreshes so a full slate of player statistics turns around in minutes instead of hours. Will Simmons removed a redundant career-totals refresh by deriving career numbers from game logs. The system began with soccer, moved to basketball, with softball next, and relies on sport-specific scoring quirks (for example baseball innings as thirds). Ogden stresses staff are not coders and credits collaboration; next focus is teaching student workers to build similar fixes. No controlled baseline study, error rates or cross-school replication is reported.","sledRelevance":"Why now: published Oct 6, 2026 and not in the archive before this edition; it is an operational, in-house AI automation story for athletics communications rather than another FanWord-style vendor announcement. Interpretation: departments can gain real turnaround wins by pairing commodity LLM help with campus data-science capacity and existing NCAA XML feeds, without buying a content-generation platform.","evidence":"University AI Hub article dated Oct 6, 2026 quoting Brian Ogden (director of communications) and describing collaboration with Josh King and Will Simmons. Self-reported efficiency (minutes vs hours) and process history; no independent measurement, sample sizes, accuracy audit or cost figures.","architectureImplications":"LLM-assisted formula authoring over Excel/Power Query, then direct ingest of athletics XML/data files into spreadsheets instead of scraping the NCAA site. Interpretation: the durable architecture is the data-file route and shared formulas across sports, not the chatbot used to bootstrap formulas.","governanceImplications":"In-house scripts touching NCAA competition data need version control, access controls and a named owner so student worker turnover does not orphan the pipeline.","securityPrivacyImplications":"Stats pipelines use public competition data; still require controlled accounts so automated refresh cannot lock credentials or pull beyond authorized scopes.","caveats":"Institutional case study; efficiency is self-reported; no accuracy metrics; ChatGPT role limited to formula drafting; not an independent evaluation.","streamIds":["college-athletics"],"roles":{"sales":"Sales framing: this is an in-house automation win for athletics communications stats work, not a packaged AI content product. Buyers comparing vendor recap tools should ask whether their bottleneck is narrative drafting or structured stats refresh from NCAA feeds. A bounded offer is a stats-pipeline readiness assessment: inventory XML/data sources, measure current refresh time and error rates, and identify which sports share file formats. Do not claim vendor AI content tools deliver the same minutes-vs-hours result; MSU's gain came from routing existing files and simplifying refreshes.","engineering":"Engineering framing: start from the data contract, not the chatbot. Engineers should map NCAA/sport XML feeds into a single refresh path, derive career totals from game logs, and encode sport-specific scoring rules (innings as thirds, golf lowest-wins). Proof means timed refresh of a full roster before and after, plus spot-checks that totals match official box scores. Avoid scraping public pages when underlying files exist; MSU nearly locked an account doing that.","delivery":"Delivery framing: treat the pipeline as an owned athletics systems artifact. Delivery should name a communications and data-science co-owner, put formulas under version control, document sport-by-sport quirks, and train student workers to extend rather than only consume the tool. Interpretation: expand sport coverage only after a sample-data test passes. Acceptance should include a measured refresh-time target and a box-score reconciliation checklist each game night."},"roleDetails":{"sales":{"customerProblem":"Athletics communications staff spend hours each game night refreshing player and career statistics across sports.","stakeholders":["Director of athletics communications","Athletics data/AI fellow or campus data science","Sports information directors","Student communications workers","Athletics IT"],"discoveryQuestions":["How long does a full player-stats refresh take today?","Do you have direct access to NCAA or provider data files?","Which sports share the same underlying file format?","Who maintains formulas when student workers turn over?"],"valueHypothesis":"Routing existing competition data files into a shared spreadsheet pipeline may cut refresh time from hours to minutes when formulas encode sport rules correctly.","potentialEngagement":"A two-sport stats-pipeline readiness assessment with timed baseline refresh and box-score reconciliation checks.","unsupportedClaims":["Vendor content-AI tools produce the same efficiency","Measured accuracy across all sports","Cost savings figures","Replication outside MSU"]},"engineering":{"fit":"Departments with access to NCAA/provider XML or data files and staff who can maintain Excel/Power Query or equivalent transforms.","architectureIntegration":"LLM-assisted authoring of spreadsheet transforms; direct file ingest into athletics stats workbooks; shared formulas adapted per sport.","prerequisites":["Access to underlying data files","Named technical co-owner","Sample historical files for testing"],"constraints":["Rate limits if scraping websites","Sport-specific scoring quirks","Staff are not professional software engineers"],"security":"Use least-privilege data accounts; avoid high-frequency public scraping; keep credentials out of shared student workbooks where possible.","proofOfValue":["Timed full-roster refresh before/after","Box-score totals match official stats on a sample","Career totals reconcile to game logs"]},"delivery":{"work":"Operate and extend a multi-sport stats refresh pipeline with documentation and student-worker training.","dependencies":["Athletics communications","Campus AI/data science capacity","Stable data-file access"],"ownership":"Director of athletics communications with the athletics data/AI fellow.","skillsAdoption":"Teach student workers to adapt formulas for new sports rather than only run refreshes.","governanceCheckpoints":["Sample-data test before each new sport","Version-controlled formula change log","Game-night reconciliation checklist"],"acceptanceCriteria":["Proposed: full-slate refresh completes in under 15 minutes for live sports","Proposed: 100% of sampled player totals match official box scores before publish"],"risks":["Silent formula errors across sports","Knowledge loss when student workers leave","Account lockouts if scraping returns"]}},"retrievedAt":"2026-10-08T22:27:58-05:00","enrichedAt":"2026-10-08T22:31:33-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Shifts student-worker learning toward building and adapting automation, not only manual stats entry.","procurementImplications":"May reduce need for paid stats-refresh tooling if file access and campus data-science help are available; still budget for maintenance time.","operatingModelImplications":"Communications and an athletics AI lab co-own an operational pipeline, blurring SID and data-engineering roles.","sourceVerification":{"responseId":"curl-msu-aihub-athletics-stats-20261008T2227","openedUrl":"https://aihub.msstate.edu/app/news/brian-ogden-athletics-stats","referenceExcerpt":"turns a full slate of player statistics around in minutes instead of hours","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}