WorldCupROI: a sponsorship-decision platform that keeps real, text and mock data visibly separate

From predicting results to choosing a sponsorship strategy

WorldCupROI is a sports-sponsorship intelligence platform that combines match performance, media attention, fan behavior, sponsor investment, scenario simulation and uncertainty risk into one decision tool. The README frames the goal as going past predicting football results to answering which sponsorship strategy a brand should choose, under what risk, and why.

The user is an analyst or business reviewer weighing sponsorship decisions who wants forecasts with defensible uncertainty, not point estimates presented as fact. The README describes a product flow of discover, explain, predict, simulate, recommend, aimed at making the analysis usable by people who are not model builders.

The project is a reproducible analytics codebase organized in layers: data intelligence, ML modeling, risk and explainability, decision intelligence, and a product dashboard. It is Python 3.11 with a Streamlit and Plotly dashboard, and it uses conformal prediction and Monte Carlo methods for uncertainty. What makes it worth a close look is less any single model and more how it treats the honesty of its inputs and the uncertainty of its outputs.

Labeling data provenance before any decision

The design choice that stands out is that WorldCupROI makes its data boundaries visible. The README's data intelligence layer separates real historical data, real text data, and proxy or mock commercial data, with the stated business value of making data boundaries visible before decisions.

That is unusually honest for a decision platform. Sponsorship ROI analysis inevitably mixes solid historical results with softer signals and, often, placeholder commercial figures where real spend data is unavailable. Most tools blur these together and present a confident number. WorldCupROI instead labels which parts are real and which are proxy, so a reviewer knows where the ground is firm and where a figure is a stand-in. A decision made on data you know is partly mock is a different, more defensible decision than one made on data you assumed was all real.

The README reports specific metrics for the current build, including a sponsor ROI R2 of 0.8838 and MAE of 0.1177, a match accuracy of 0.5566, and coverage figures such as an ROI interval coverage of 0.8814. Those are the model's reported numbers, and the presence of coverage metrics alongside point accuracy is consistent with the project's emphasis on calibrated uncertainty rather than headline accuracy alone.

Uncertainty and explainability as first-class outputs

The risk and explainability layer is where WorldCupROI tries to turn estimates into decisions. The README describes SHAP-style drivers, conformal intervals, Monte Carlo risk and scenario lift, with the stated aim of turning point estimates into defensible decisions. The decision intelligence layer adds optimization, causal evidence, counterfactuals, tail risk, temporal behavior and graph learning.

Conformal intervals matter here because they come with a coverage guarantee: an interval calibrated to cover the true value a stated fraction of the time is a far more honest input to a business decision than a bare prediction. The README's reported conformal coverage figures, a match conformal coverage of 0.9021 and ROI interval coverage of 0.8814, are the kind of number that tells a reviewer how much to trust the range.

The explainability side, SHAP-style attribution of which signals drove a forecast, addresses the other half of a defensible decision: not just what the model predicts but why. For a sponsorship decision that a brand has to justify internally, being able to point at the drivers and the risk interval is more useful than a single ROI number, and the platform is built around producing both.

Running the dashboard

WorldCupROI is run as a project rather than installed as a package, and the README points to a browser website, a static dashboard file, and a live Streamlit dashboard. The live dashboard is launched through the Makefile:

make dashboard

The repository ships a `Dockerfile` and a `Makefile` for reproducible runs, and a `requirements.txt` for the Python 3.11 environment, so a containerized run is available for anyone who prefers not to manage a local Python setup. The README also links a static `dashboard/panel_dashboard.html` for viewing without running anything, and reports it publishes to a GitHub Pages site.

Because the project is organized as layers with notebooks, a dashboard and reports, the practical entry point is the dashboard for exploration and the reports directory, model card, data card and business insights, for understanding what the models are and what the numbers mean. Anyone intending to trust the outputs should read the data card first, since that is where the real-versus-proxy boundaries the project emphasizes are documented.

The limitations: modest accuracy and an unlicensed repo

The honest limitations are visible in the project's own numbers and metadata. The reported match accuracy of 0.5566 is modest for a three-outcome prediction, which the README does not hide, and the platform's own framing, treating models as baselines to be improved and emphasizing intervals over point estimates, is the right response to that. Football outcomes are genuinely hard to predict, and a tool that surrounds a mediocre point estimate with honest uncertainty is more useful than one that overclaims. Still, a user should not mistake the polished dashboard for high predictive certainty.

The proxy commercial data is the deeper caveat. Because some commercial figures are mock by the project's own labeling, the ROI forecasts are demonstrations of a method on partly synthetic inputs, not validated business predictions. The value here is the pipeline and the honesty about provenance, not a guarantee that the ROI numbers reflect a real market.

The most consequential gap is legal: the repository ships no license, so under default copyright the author retains all rights. It can be read, but it cannot be safely reused, redistributed or built upon until a license is added, which is a blocking issue for any adoption beyond study.

Against a spreadsheet or a black-box BI model

The alternatives are the tools brands actually use: a sponsorship model in a spreadsheet, or a black-box BI or ML service that outputs an ROI score. A spreadsheet is transparent but has no calibrated uncertainty and no explainability; a black-box service may be more accurate but hides its data provenance and its confidence.

WorldCupROI's difference is the combination it insists on: reproducible layers, labeled data provenance, conformal intervals with reported coverage, and SHAP-style drivers. It refuses to present an ROI number without a risk interval and an explanation, and it refuses to mix real and proxy data silently. The cost is that it is a research-grade project, not a productized service, and its accuracy on the hard prediction task is modest. Choose a spreadsheet when the analysis is small and you want full manual control. Choose a managed BI model when accuracy on your own real data matters most and you accept opacity. Choose WorldCupROI as a reference for how to build a sponsorship decision pipeline that is honest about provenance and uncertainty, understanding its commercial figures are partly mock and its license question is unresolved.

No license, reproducibility, and what to check first

The absence of a license is the first thing to resolve. As shipped, WorldCupROI is public and reproducible but not reusable in any legal sense, so anyone wanting to adapt it should ask the author to add a license before investing. Its last push was on 2026-06-16, which is recent, so the project is not stale, and the CI badge and reproducibility tooling suggest ongoing maintenance.

The reproducibility surface, a Dockerfile, a Makefile, pinned `requirements.txt` and dated model and data cards, is a genuine strength for a research project, because it means the reported numbers can in principle be regenerated rather than taken on faith.

The concrete first step is to read the data card and separate what is real from what is proxy before drawing any business conclusion, then launch the dashboard with `make dashboard` and treat the ROI interval and the SHAP-style drivers, not the single ROI point, as the outputs that matter. Verify the conformal coverage numbers hold on any data you supply, since a calibrated interval is only trustworthy if its coverage claim survives your inputs.

Editorial conclusion

Study WorldCupROI as a reference for building an honest sponsorship-decision pipeline: it labels real versus proxy data before deciding, surrounds ROI forecasts with conformal intervals and Monte Carlo risk, and explains them with SHAP-style drivers, all reproducibly via a Dockerfile and Makefile. It is the wrong thing to treat as a validated predictor, since its match accuracy is a modest 0.5566 and some commercial figures are mock by its own labeling, and it cannot be reused legally because the repository ships no license. Before drawing any conclusion, read the data card to see which inputs are real, launch the dashboard with make dashboard, and trust the ROI interval and drivers rather than the single ROI point.

Frequently asked questions

Official sources

1. 2417467487-hub/WorldCupROI on GitHub
2. Issues
3. Project website
4. README