public outcome signal
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AI-Assisted Basketball Scheduling Decision Workflow
A staff-facing basketball scheduling workflow that turns messy NCAA data and selection-committee constraints into repeatable decision support.
public outcome signal
public outcome signal
Outcome proof
Public-safe signals from the real workflow.
These are the outcome signals safe to show publicly. Internal strategy, staff notes, and private records stay out of the portfolio.
non-conference strength-of-schedule lift
team ranking movement after adopted scheduling changes
resume-quality improvement
quality-game result shift
Recruiter answer
What proof does this case study show?
This case study shows AI implementation ability through workflow scoping, decision-support design, data validation, scenario comparison, and operator-ready briefing output.
Role fit
- AI workflow implementation
- Decision-support enablement
- Operator-facing analytics adoption
Validation signals
- Source checks before recommendation
- Scenario comparison instead of one-shot output
- Human review before staff use
Interview questions answered
The short version a hiring team should understand.
- Messy workflow
- Schedule decisions mixed public rankings, selection-committee incentives, opponent availability, staff preferences, and downside risk.
- System designed
- A repeatable decision workflow for comparing schedule scenarios, quadrant impact, resume strength, and staff-ready recommendations.
- AI-assisted build
- AI coding assistance helped implement and iterate dashboards, scripts, scenario tables, and briefing surfaces around the workflow I defined.
- Validation
- Recommendations were checked through source consistency, quadrant classification, scenario comparison, and human review before staff use.
- Usage/adoption
- Staff adopted the recommendations as part of real scheduling decision work.
- Proof signal
- Public-safe outcome signals after adopted recommendations include NCSOS 293 -> 59, NET 46 -> 9, WAB -0.76 -> +2.59, and Q1/Q2 record 0-2 -> 5-1.
- Improve next
- Add a stronger public-safe before/after artifact that shows the decision path without exposing internal strategy or targets.
- Public boundary
- The public version uses synthetic visuals and public outcome signals. It does not claim the workflow alone caused BYU's results.
Problem definition, workflow design, evaluation criteria, source selection, validation, rollout, user feedback, adoption, and outcome framing.
Code, app structure, scripts, UI wiring, parsing, tests, and iteration support. I stayed accountable for whether the workflow was useful and honest.
Problem
Schedule planning was high stakes, data-heavy, and difficult to translate into repeatable staff decisions quickly.
What I Designed
Defined the scheduling problem, success criteria, data checks, briefing format, and adoption loop, then used AI coding assistance to help build and harden the dashboards and scripts.
Validation
Used scenario comparison, quadrant classification, source checks, and concise brief outputs so operators could review the recommendation instead of trusting a black box.
Public Handling
The public version uses synthetic visuals and avoids internal strategy, private data, and claims of sole engineering ownership.
Artifacts and proof
What the public proof can show.
These artifacts make the workflow concrete while keeping private data, credentials, logs, internal strategy, and real customer/user records out of public view.
Fake opponent slate with quadrant impact, downside risk, and staff-ready recommendation language.
Public-safe example comparing neutral-site, road, and home-game tradeoffs without exposing internal targets.
Quadrant classification, source consistency, NET/RPI sanity checks, and human review before use.
No internal scheduling strategy, private staff notes, or claims of sole engineering ownership.
Positioning note
This is operator-led AI implementation proof: workflow judgment, validation, rollout thinking, adoption, and AI-assisted build support where coding is involved.
Good fit for teams that need someone to turn high-stakes, messy operating decisions into AI-assisted workflows people can review and adopt.