A chart showing a striking football trend is a claim. Without the data and the exact steps used to produce it, it’s a claim that has to be taken on trust — trust in the author’s care, trust that no step was skipped or misremembered in translating from data to conclusion. Reproducible research is the practice of removing that trust requirement: publishing enough of the data, method, and code that someone else can independently regenerate the same result and check it against what was reported.
Reproducibility Is a Narrower, More Mechanical Standard Than Replication
It’s worth separating two ideas that get used loosely as synonyms. Full replication means an independent researcher repeats an entire study — new data collection, the same method, a fresh test of whether the finding holds up in a different sample. Reproducibility is a smaller, more mechanical claim: given the exact same data the original author used, does the exact same method actually produce the result that was reported. Statistician Roger Peng, in a widely cited 2011 perspective piece in Science, framed reproducibility as a practical minimum standard for evaluating computational findings specifically because full independent replication is often expensive, slow, or simply not feasible for every published result — but checking whether a specific analysis, as described, actually does what it claims to do is almost always achievable if the data and code are available.
That distinction matters for sports analytics because most published sports findings never get the resources for a full replication study — there’s no grant funding a second, independent data-collection effort to confirm a fourth-down tendency or a clustering result. Reproducibility is the more modest, more attainable bar, and it’s still enough to catch a large share of the errors that make it into published analysis: a data-cleaning step applied inconsistently, a filter that silently excludes more games than intended, a plotting script that doesn’t match the description of how the numbers were computed.
What Goes Wrong Without It
An unreproducible result isn’t necessarily wrong — it’s simply unverifiable, which in practice means readers have no way to distinguish a correct result from an incorrect one dressed up identically. A chart built from a coding mistake and a chart built correctly can look indistinguishable to anyone reading the write-up; the only way to tell them apart is to check the work, and checking the work requires having the data and the method available to check.
This is a particular risk in sports analytics because the field runs largely outside traditional peer review — a compelling chart and a plausible-sounding methodology paragraph is often the entire bar for publication on a blog or a social media thread. Nothing in that process forces an author to double-check a filter condition or confirm a join didn’t silently drop half the intended games, and nothing forces a reader to notice if it happened. Publishing the code alongside the conclusion turns that invisible risk into something checkable — anyone can run the script and see whether it actually produces what’s claimed, and anyone can spot the specific step where an error crept in if it doesn’t.
Open Data as an Enabling Condition
Reproducibility requires access to the data a result was built on, which is where a genuinely open sports dataset matters beyond simple convenience. nflverse’s nflverse-data releases publish play-by-play, schedule, and player-stat data under a CC-BY-4.0 license with a stable, versioned release history — meaning a specific published analysis can point to the exact dataset release it used, and anyone else can pull that same release and check the analysis against it directly, not trust a paraphrased description of data nobody else can obtain.
This is a meaningfully different situation from analysis built on scraped or proprietary data with no public release attached. A result built on data nobody else can access is not reproducible by construction — there’s no way for a reader to check it even if the code is published, since the code has nothing public to run against. Open, licensed, versioned datasets remove that barrier, and their existence is part of why the open football-analytics community has been able to build a shared, checkable base of models — expected points, win probability, completion probability — that multiple independent analysts have verified against the same public data, not each working from a private version nobody else can inspect.
What Good Practice Looks Like in the Open Football-Analytics Community
Open Source Football, a blog publishing football-analytics write-ups alongside runnable code and built on the same open nflverse data infrastructure, is a concrete example of this practice working at a community scale: articles on expected points, win probability, and related models are published with the code that produced them, against openly available data, not as conclusions alone. That combination — public data plus public code — is what allows other analysts to actually verify a published model’s methodology, propose corrections, or build directly on top of it instead of re-deriving it from scratch based on a written description alone.
Common Barriers That Quietly Prevent Reproducibility
A few habits are responsible for most sports analysis that turns out to be unreproducible in practice, and none of them require bad intent — they’re just easy steps to skip under time pressure. The most common is describing a data-cleaning step in prose (“games with fewer than 10 offensive snaps were excluded”) without publishing the actual filter condition used in code, which leaves room for a reader’s attempt to reproduce the result to apply a subtly different threshold and get a different answer, with no way to tell whether the discrepancy is a real disagreement or just an inconsistent filter. A second is working entirely inside a private spreadsheet or notebook that never gets published in any form — the conclusion is shared, the working file isn’t, and there’s structurally nothing for anyone else to check even if they wanted to. A third is depending on data that was scraped or pulled at one point in time from a source that has since changed or gone offline, so that even a reader with the published code has no way to fetch the same inputs the original analysis used.
None of these are exotic failures — they’re closer to the default state of most published sports analysis, which makes deliberately avoiding them (naming the exact dataset release, publishing the actual filter code, choosing a data source with a stable public archive) less about following a formal standard and more about a small number of specific, avoidable habits.
A Practical Standard for Publishing a Sports Analysis
The practical version of reproducible research doesn’t require a formal framework — it requires three things published alongside any result: which dataset, specifically (ideally a named, versioned release, not “the play-by-play data”), was used; what filtering or cleaning steps were applied to it before analysis; and the actual code, even in a rough form, that turns that filtered data into the reported number or chart. None of this guarantees a result is correct — a reproducible analysis can still rest on a flawed assumption or a genuinely mistaken method. What it guarantees is that the question “is this actually what the data shows” has an answer someone other than the original author can check, not a description that has to be taken on faith.
Frequently asked questions
What does 'reproducible research' mean?
Reproducible research means that someone other than the original author, given the same data and the same code or method, can arrive at the same result. It's a narrower, more mechanical standard than full independent replication (repeating the whole study with new data) — reproducibility just asks whether the specific analysis, as published, actually produces what it claims to produce.
Why does reproducibility matter more than whether a finding sounds credible?
A finding can sound entirely credible and still not survive an attempt to reproduce it — a coding error, a data-cleaning step done differently than described, or a subtle mismatch between what the write-up says the method was and what the code actually did. Reproducibility is a mechanical check that catches these problems regardless of how plausible the final result looks, which is exactly the kind of error that intuition alone can't detect.
How is reproducibility different from cross-validation?
Cross-validation checks whether a model generalizes to data it wasn't trained on — a question about the model's predictive performance. Reproducibility checks whether the reported result can be independently regenerated from the stated data and method at all — a more basic question about whether the analysis, as described, is actually correct and complete. A model can be perfectly reproducible and still generalize poorly, and a model can generalize well in theory while the specific published result behind it can't be regenerated because of an undocumented step.
What makes open sports datasets like nflverse useful for reproducible research?
Open, versioned, freely licensed datasets remove one of the most common barriers to reproducing a sports-analytics result: access to the underlying data. When play-by-play or player-stat data is published under an open license with a stable release history, anyone can pull the exact dataset a published analysis used and check the analysis against it, not trust a description of data that isn't actually available to inspect.
What's a practical first step toward making a sports analysis reproducible?
Publishing the code alongside the result, not just the conclusion — even a simple script that loads a specific, named dataset release and reproduces the key numbers or chart removes most of the ambiguity in a write-up. The code doesn't need to be polished; it needs to actually run, on data someone else can obtain, and produce what the write-up claims it produces.