How to Get a Job in Sports Analytics: Real Paths, Not Shortcuts

There is no published, standard hiring pipeline into football analytics the way there is for, say, actuarial work or public accounting. But there are several real, currently active paths that show up repeatedly in public reporting and in the field’s own documented history — and all of them share the same underlying requirement: work that can actually be checked, not credentials or connections alone.

Build a Public, Checkable Portfolio

The single most consistently cited entry point is also the most within an individual’s control: publish real analytical work on real data, in a form a stranger can read and verify. That’s a meaningfully different bar than a private project or a class assignment, because a public repository or write-up is evidence a hiring manager can evaluate directly, in a field where formal job postings are relatively uncommon and often filled through people who already have some visible track record. The nflverse project publishes NFL play-by-play data back to 1999 as free downloads on GitHub under a CC-BY-4.0 license, alongside weekly player stats and tracking-derived data — the same dataset most public football-analytics writing, and the Big Data Bowl itself, is built on. There’s no gatekeeping step between deciding to build something and having the data to build it with.

A portfolio built around a genuine, specific football question tends to read very differently from one built around demonstrating a technique for its own sake. “Here’s a k-means clustering implementation” shows a skill; “here’s whether a team’s fourth-down decisions actually followed the expected-value-maximizing call last season, and by how much they diverged when they didn’t” shows the same skill applied to a question a reader can immediately see the point of. The technique is usually the easy part to demonstrate — public tutorials cover most of them — and the football framing around it is what’s actually specific to the person building the portfolio, which is exactly what a hiring manager reading dozens of similar portfolios in a season is looking for a reason to remember.

Enter the Big Data Bowl

The NFL’s Big Data Bowl is the clearest documented case of a competition functioning as a direct pipeline into the industry. Run annually on Kaggle and powered by AWS since 2019, each edition releases a season of player-tracking data around a themed prediction problem — running back evaluation, pass coverage, special teams, tackling and pre-snap motion have all served as past themes, with the 2026 edition focused on predicting player movement while the ball is in the air. Finalists present their work directly to NFL team decision-makers at the Scouting Combine, and the league has stated that more than 75 Big Data Bowl participants have since been hired into data and analytics roles across sports. Participation doesn’t require an existing industry connection — it requires the ability to work with the released tracking dataset and produce something worth presenting.

MIT Sloan Sports Analytics Conference

Founded in 2006 by Daryl Morey and Jessica Gelman and first held in 2007, the MIT Sloan Sports Analytics Conference has grown from a one-day event with fewer than 200 attendees into the field’s largest annual gathering. Two of its components are directly relevant to someone trying to break in rather than just attend: the Research Paper Competition, which includes a dedicated football track alongside basketball, baseball, soccer and business-of-sports, and runs on an abstract-then-full-paper submission process months ahead of the conference; and a separate case competition open to student teams working from a provided dataset. Both are genuinely open entry points rather than invite-only showcases, and past participants and speakers include people who have gone on to work inside NFL front offices.

Publish and Engage With the Open Community

Football analytics has an unusually active open-source and open-publishing culture relative to most professional sports, and knowing where that community actually is is most of the work of engaging with it. Open Source Football, a community site built to centralize shared analytical work using the nflverse ecosystem, exists specifically because individual write-ups scattered across social platforms are hard to find later — publishing there, or in a similar public, indexable form, puts work in front of the same community that reviews Big Data Bowl submissions and reads MIT Sloan abstracts. The overlap between “people who write publicly about football analytics” and “people who end up interviewing candidates for football analytics roles” is real and documented anecdotally across the field, even where it isn’t formally tracked.

What Hiring Managers Have Actually Said They Look For

Public reporting on football analytics hiring is thin compared to more established fields, but what does surface consistently is a preference for demonstrated technical ability combined with the ability to communicate findings to non-technical decision-makers — not technical skill alone. ESPN’s recurring anonymous survey of current and former NFL analytics staffers, which has documented staffing growth across multiple teams over the past several years (Tennessee, for example, expanded from a single analytics employee to a four-person group after hiring a director of football research and development in 2023), consistently frames the value of analytics staff in terms of whether their work actually changes decisions coaches and front offices make — the translation skill that separates an analytics department that gets listened to from one that produces reports nobody acts on. A portfolio, a Big Data Bowl submission, or a conference paper that only demonstrates modeling skill and never explains why the result matters to a football decision is doing half the job publicly reported hiring criteria actually ask for.

Internships and Team-Facing Vendor Work as a Side Door

Not every path into an NFL building runs through a team’s own hiring page. A growing set of vendors — companies like Pro Football Focus, which builds player grades from its own charting, and SumerSports, which has publicly described building tracking-data models for team-facing scouting products — hire analysts and engineers who work adjacent to, and sometimes directly with, NFL front offices without being on a team’s own payroll. That work builds the same portfolio of checkable, football-specific analytical output the paths above are built around, and it’s a route into the field that doesn’t require an opening on a specific team’s internal analytics staff, which at many organizations is still small enough that turnover is rare.

No Shortcuts, Only Documented Paths

None of these paths guarantee a job, and none of them are secret — that’s the point. A public portfolio, a Big Data Bowl entry, a conference paper or case-competition submission, and consistent public engagement with the open football-analytics community are the parts of the process that are genuinely checkable from outside the industry, in a field where most of the actual hiring process still happens behind closed doors. Getting stuck while building any of that public work is normal, and asking for help effectively is a skill on its own, worth developing alongside the technical one.