Flip it round. An analyst's week is a handful of distinct jobs. You watch players. You get data and clean it. You analyse it to answer a question. You visualise the answer so someone can read it in ten seconds. You write it up. You organise and share your work so people can find it. Pick the job first, then the lightest tool that does it well.
That reframe kills the whole listicle genre. There is no single best tool, only the best tool for a specific job at your stage. Most categories below have a free way in and an expensive professional version, and knowing which is which saves you money and months.
Watching and Video (Scouting the Player)
The core job is watching players with structure and pulling out the moments that make your case. At the professional end, that means Hudl Wyscout. Wyscout and InStat merged into one product in 2025, and it is what most clubs and agencies use: footage across hundreds of leagues, filtering, playlists, stats next to the video. It is the first thing I open most mornings.
Now the access reality no listicle mentions. Wyscout is expensive and usually club-tied. A personal licence starts around 299 euros a year for the entry tier, which limits your video minutes heavily. The full experience is a club or agency subscription costing far more, and you only get that once someone employs you. If a guide tells you to "just learn Wyscout" on day one, they have never paid for it themselves. Hudl Sportscode, the tactical analyst's video-coding tool, is the same story: powerful, club-priced, and not something you need to break in.
The free way in is better than people think. YouTube league and club channels, free streams and full-match replays give you plenty to watch, and a basic clipping tool or your phone's screen recorder lets you pull moments. What matters at this stage is not the platform, it is whether you watch with a checklist in your head rather than like a fan. The structure is the skill; the subscription just makes it faster once you are inside. I cover doing this without a club login in how to get into football analytics.
Data (Getting It and Handling It)
The second job is getting football data and handling it without making a mess. The professional providers are StatsBomb, Opta (now Stats Perform) and the data side of Wyscout, plus tracking providers like SkillCorner. These sit behind most of the metrics discussed online. You will not have access to the paid tiers until you are in a role, and that is fine; knowing what they do is enough for now.
To get started, more football data is freely available than ever. The barrier is not access, it is knowing what to do with it. The StatsBomb open data library is the single best free resource going: full event data, well documented, widely referenced. Understat gives you expected goals for the top five leagues. Transfermarkt is free and I check it several times a day for contracts, transfer histories and market values. One correction to older guides, including my own earlier version of this article: FBref lost its Opta licence in January 2026, so treat it as a historical archive now, not your live source. I keep a fuller, maintained list in where to find football data.
Now the handling. The most underrated tool in this field is Excel, or Google Sheets if you want it free. I still use spreadsheets daily for squad profiles, longlist filtering and cleaning data before it goes near code. If you cannot clean data in a spreadsheet, no amount of Python saves you. I have watched people build an elaborate model to answer a question a pivot table would have solved in five minutes. Learn Excel first. SQL comes later, when you are querying a real database at a club, and it is worth learning then rather than now.
Analysis and Code
The third job is analysing data to answer a specific question, and this is where people panic about code. So let me be blunt: you do not need to code on day one. Coding is powerful, but it is not the entry ticket. Visible output beats tool-collecting every time, and plenty of good analysts get hired on video literacy and clean reporting without writing a line of Python. I have said the same about certificates in stop collecting certificates, start building proof.
When you do want it, Python is the one I would point you to. The libraries that matter for football are pandas for handling and reshaping data, mplsoccer for pitch plots, radars and shot maps built for football, and a general plotting library underneath, either matplotlib, seaborn or plotly depending on the look you want. R does the same jobs and some analysts swear by it, but if you are choosing one, Python has the bigger football community and more code to copy from.
I could not code when I started. It took me months to make my first pizza chart, and it was ugly. Anyone who tells you Python is quick to pick up has forgotten what the first month feels like. The way through is to build one real thing rather than grind tutorials. McKay Johns on YouTube is a good free resource, walking through football-specific mplsoccer and pandas work you can follow along with. To see a finished project end to end, I wrote up building player ratings from scratch with Python.
Visualisation and Presentation
Analysis nobody can read is worthless. The fourth job is turning your answer into something a coach or sporting director understands in seconds. The point is communication, not decoration.
For dashboards and interactive work, Tableau Public is free and more than powerful enough for a portfolio. I learned it during lockdown with nothing but time and a laptop, and it changed my career. Power BI does a similar job and turns up more in Microsoft-run club environments, so it is worth knowing the name, but Tableau Public is the easier free start. For static, made-to-measure graphics, the Python route above (mplsoccer on top of matplotlib) gives you total control over pitch plots and player cards.
A clean, well-labelled Tableau viz or a sharp player-comparison graphic is portfolio gold, because it proves you can both analyse and communicate. The mistake is learning a viz tool too early, before you have anything worth showing. Get the analysis right, then make it readable. When you are ready to assemble the work into something clubs can judge, how to build a football analyst portfolio covers what to put in it.
Organising and Sharing Your Work
This is the job nobody puts on a tools list, and it is the one that gets people hired. You need a consistent way of organising your reports and publishing them where people can find them.
For organising, Notion or plain Google Docs is enough: somewhere to keep report templates, a running database of players you have looked at, and drafts before they go public. For publishing, you need a portfolio home, whether a personal site, a Substack or a well-kept LinkedIn feed. The tool matters less than the habit of putting finished work into the world regularly. A folder of PDFs on your laptop is not a portfolio; if nobody can find it, it does not exist, which is the whole argument in why your online portfolio matters more than your CV.
LinkedIn deserves a specific mention because it is where football recruitment people actually are. Posting your analysis there, tagging it well and engaging with the right people has opened more doors for the members I work with than any application form. I broke down how to do it without being cringe in how to use LinkedIn to get into football.
What to Learn First (If You're Starting Today)
Competitors do not give you an order, so here is mine. Do these one at a time, and do not move on until the current one produces something visible.
- Watch with structure. Free footage, a checklist, and written notes. Build the habit of forming and defending a view on a player. Cost: nothing.
- Excel or Google Sheets. Squad lists, filtering, cleaning. The unglamorous foundation everything else sits on. Cost: nothing.
- One free data source plus one viz tool. Pull from StatsBomb open data or Understat, then present it in Tableau Public. Now you can turn a question into a readable answer. Cost: nothing.
- Then code, if you want the data path. Python with
pandas and mplsoccer. Build one real project, not ten tutorials. Cost: nothing but time.
Everything else waits. You do not need Wyscout, SkillCorner, SQL, Power BI or R to start, and chasing them early just spreads you thin. Go deep on two or three tools and produce work. That order alone puts you ahead of most people circling the industry.
The Overrated and the Overhyped
Now the opinions. A few things people obsess over that do not move the needle for getting hired.
Paid courses that teach free tools. There are excellent free tutorials for Python, Tableau and Excel. Before you spend money learning software, exhaust YouTube. Most paid "analytics course" curricula are a playlist with a certificate stapled on.
Collecting tools as a substitute for output. Being average at ten tools loses every time to being genuinely good at two or three with visible work to prove it. The best analysts I know have a small, sharp stack, not a long one.
Buying Wyscout before you are ready. I rate Wyscout, but paying 299 euros to poke around footage you can largely find free, before you can even watch with structure, is money spent to feel like you are progressing. Get the free reps in first.
Obsessing over the fanciest metric or provider. Packing rate, tracking data, potential models: interesting, and worth understanding as concepts for interviews. But you will not have access until you are in a role, and no club hires you because you can recite what SkillCorner measures. They hire you because you thought clearly about a player with the data you could get.
Where to Go From Here
The tool does not make the analyst. Judgement plus visible output does. Every good analyst I have worked with got there the same way: they picked two or three tools, went deep, and produced work that showed they could think clearly about football. Tools just got them there faster.
If you want more of this, I write a free weekly newsletter on breaking into football, what I am learning in the job and what I am seeing across clubs. Sign up for the Football Progression Path newsletter if that is useful.
And if you want the structured version, with templates, live sessions, a community of people making the same career change and direct access to me, that is what the Recruitment Room is for. Either way, do not overthink the stack. Start watching, start building, start sharing.
Frequently Asked Questions
What tools do football analysts actually use?
Grouped by job: for watching players, Hudl Wyscout at clubs and free footage to start. For data, providers like StatsBomb, Opta and SkillCorner at the top end, with StatsBomb open data, Understat and Transfermarkt free. For handling data, Excel or Google Sheets, then SQL. For analysis, Python with pandas and mplsoccer. For visualisation, Tableau, Power BI or Python viz. For sharing, Notion, a portfolio site and LinkedIn. Excel and Wyscout are near-universal in the professional game.
What software should I learn first to get into football analytics?
Learn in this order: watch football with structure using free footage, then Excel or Google Sheets, then one free data source paired with one visualisation tool like Tableau Public, and only then code if you want the data path. Do each until it produces something visible before moving on. Trying to learn everything at once just leaves you mediocre at all of it.
What are the best free football analytics tools?
The StatsBomb open data library is the best free data source, with full event data. Understat gives you expected goals for the top five leagues, and Transfermarkt is free for squads, transfers and market values. Google Sheets covers spreadsheets, Tableau Public covers visualisation, and Python with pandas and mplsoccer is free for analysis and football graphics. Together they are more than enough to build real portfolio projects.
Do I need to learn Python to be a football analyst?
Not on day one, and not for every role. Video-led scouting and tactical roles reward watching with structure and clean report writing more than code. For data-led roles, Python is increasingly expected and gives you a real edge. If you learn it, focus on pandas for handling data and mplsoccer for football graphics, and build one real project rather than grinding tutorials.
Is Wyscout worth it and how do I get access?
Wyscout is useful and it is what clubs use, but be honest about access. A personal licence starts around 299 euros a year with heavily limited video minutes, and the full version is a club or agency subscription you only get once employed. If you are starting out, do not rush to pay for it. Free footage plus the habit of watching with structure builds the same underlying skill.
Which visualisation tool should I learn?
Start with Tableau Public. It is free, powerful enough for any portfolio, and quick to produce clean dashboards and player comparisons. Power BI does a similar job and shows up in Microsoft-based club setups, so it is worth knowing the name. For fully custom static graphics like pitch plots and player cards, learn Python's mplsoccer on top of matplotlib. The goal is communication, not decoration.