Most guides on football analytics tools are written by people who don't use them. Course providers recommending their own syllabus. Career sites listing software they've never opened. You can tell.
"What tools do I need to learn?" is the question I get on almost every call. From career changers, graduates, and people who've been circling the industry for months without knowing where to start.
The answer is fewer than you think. But the right ones, in the right order.
Most people waste months learning tools they'll never use professionally because they followed an outdated guide. They learn Python before they understand what questions to ask. They spend weeks building Tableau dashboards before they've watched a game with any real purpose. They pay for courses teaching tools they could learn for free on YouTube.
I've been guilty of some of this myself.
I'm a data analyst and first team scout at a global football agency. I use these tools every day for recruitment decisions that affect real players and real transfers.
This is the guide I wish someone had handed me when I started.
The core football analytics tools in 2026 are Excel, Python, Tableau, Wyscout, and Transfermarkt for day-to-day work, with data providers like StatsBomb, Opta, and SkillCorner at the professional level. Tactical analysts also rely on Hudl Sportscode for video coding. The right combination depends on your career stage and whether you lean towards data analysis or tactical work.
Before you start: not every tool matters at every stage
The biggest mistake people make is trying to learn everything at once. You don't need ten tools. You need two or three that match where you are right now, and the discipline to get genuinely good at them.
This guide has three sections. First, the tools anyone can access right now without working at a club. Second, the free data websites where you can find football data to practise with. Third, the professional platforms you'll encounter once you're in a role. Your tool stack depends on whether you lean towards data or tactical analysis - I've written about the two types of football analyst separately if you want to figure out your direction first.
Start at the top. Work down as your career progresses.
Tools you can access right now (no club needed)
You don't need to work at a club to start with any of these. They're all free or affordable, and they're where you'll build the skills that actually matter early on.
Excel / Google Sheets
What it is: Spreadsheet software. What it costs: Free (Google Sheets) or included with most computers. What you use it for: Data cleaning, basic analysis, squad lists, scouting databases.
Not glamorous. But I still use Excel daily. Squad profiles, contract databases, longlist filtering, data cleaning before it goes anywhere near Python - it all happens in spreadsheets.
Most people skip it because it's not exciting. That's a mistake.
If you can't organise and clean data in a spreadsheet, no amount of Python knowledge will save you. I've seen people build beautiful models in Python only to realise they could've answered the same question with a pivot table in five minutes. Excel is the foundation everything else sits on. Start here.
Python
What it is: A programming language. What it costs: Free. What you use it for: Data analysis, building models, creating visualisations, automating repetitive workflows.
I couldn't code when I started in football. It took me months to make my first pizza chart. Months. And it was ugly. The learning curve is real, and anyone who tells you Python is easy to pick up is either lying or forgot what it felt like at the beginning.
But Python is what separates a data analyst from someone who can use a spreadsheet. If you want the data path, you need this. It's where you build player ratings, run statistical comparisons across leagues, and automate work that would take hours manually.
I wrote a practical walkthrough of building player ratings from scratch with Python if you want to see what this looks like day-to-day.
One important note: if you're on the tactical or video path, Python is not required. Don't let anyone tell you otherwise.
Tableau
What it is: Data visualisation software. What it costs: Free (Tableau Public) or paid for the full version. What you use it for: Building dashboards, player comparison visuals, presentation-ready graphics.
Tableau Public is free and powerful enough for everything you need as a portfolio builder. I learned it during COVID lockdown - nothing but time and a laptop - and it changed my career. Turning raw data into clear, professional visuals is what makes your analysis useful to the people making decisions.
The mistake people make is learning Tableau too early, before they have anything worth visualising. Get comfortable with data first. Then make it look good. When you're ready to start putting projects together, I've written a guide on how to build a football analyst portfolio that covers what to include.
Wyscout
What it is: Video and data platform. The industry standard. What it costs: From €299/year for a personal licence (Copper tier, 70 minutes of video per month). Club licences cost significantly more. What you use it for: Watching player footage, basic stats, creating playlists, player comparison.
If you can only afford one paid tool, make it Wyscout. It's what clubs use. It's what agencies use. It's genuinely the first thing I open most mornings.
Learning to navigate it efficiently is a skill in itself. Knowing how to filter players, build meaningful playlists, and cross-reference data with video takes time. I'm still finding quicker ways to do things in it now. Start early.
Transfermarkt
What it is: Free transfer, squad, and market value database. What it costs: Free. What you use it for: Squad research, transfer history, contract information, market values.
I check Transfermarkt multiple times a day. Contract expiry dates, transfer histories, squad compositions, market valuations - all there, all free.
The interface looks like it was designed in 2008 (because it probably was). But it's indispensable. When you're scouting a player, Transfermarkt is usually your first stop for context before you open Wyscout or pull any data.
Free football data websites
"Where do I get data?" is the second most common question I get. Good news: there's more freely available football data now than at any point in history. The barrier isn't access. It's knowing what to do with it.
- FBref - Important caveat. FBref was the go-to free data source for years. But it lost its Opta data licence in January 2026, so it no longer receives updated advanced statistics. The historical data is still useful for research and back-testing, but don't rely on it for current season analysis. If you're reading a guide that still calls FBref the best free resource, that guide is outdated.
- Understat - Expected goals data for the top five European leagues. Clean interface, easy to navigate. If you want to do any xG-based analysis for your portfolio, this is the simplest starting point. Free and regularly updated.
- FotMob - Match data, live scores, player stats, and heat maps. Excellent for quick reference and following games. Not as deep as StatsBomb or Opta, but for basic stats and getting a feel for player involvement across a match, it's very good. Free.
- SofaScore - Similar to FotMob. Match ratings, player stats, heat maps, shot maps. Useful for quick comparisons and getting a snapshot of a player's performance across a season. Free and well-designed.
- WhoScored - Basic match stats, player ratings, and league tables. The ratings are algorithm-generated and shouldn't be taken as gospel, but the underlying data is useful for quick reference. Free.
- Kaggle football datasets - Community-uploaded datasets covering everything from World Cup results to match event data. Quality varies, but there are excellent resources for practice projects. Good if you want data to get your hands dirty with Python or Tableau.
- StatsBomb open data library - Deserves its own mention even though StatsBomb is also a professional provider. The open data library is the single best free resource for aspiring analysts in 2026. Full event data from multiple competitions, well-documented, and widely referenced. If you're building a portfolio project, start here.
None of these will give you the depth of a professional data subscription. But together, they're more than enough to build real projects, learn real skills, and create a portfolio that gets noticed.
If you want a curated collection of these resources in one place, the Analysis & Scouting Toolkit pulls together the best free tools and data sources I've found.
The professional tools used at club and agency level
Once you're working in football - at a club, agency, or data company - you'll encounter a different tier of tools. These are the data providers and platforms that power recruitment decisions at the professional level.
You won't have access to most of these until you're in a role. But understanding what they do matters. It's the language you'll need to speak in interviews, and it's what you'll be using on the job.
StatsBomb
What it is: One of the leading football data providers. Offers detailed event data, advanced metrics, and the StatsBomb 360 dataset which includes player positioning data for every event. What clubs use it for: Recruitment longlists, player comparison, performance analysis, opposition scouting. StatsBomb data sits behind many of the metrics you see discussed online - pressures, ball progression, shot-creating actions. What you should know: StatsBomb also has a free open data library, mentioned above. It's the single best free resource for aspiring analysts right now. Start there if you want to understand what professional-grade data looks like.
SkillCorner
What it is: Physical and tracking data provider that uses broadcast footage to generate tracking data without needing in-stadium cameras. What clubs use it for: Physical metrics, high-speed running data, pressing intensity, off-ball movement analysis, physical benchmarking across leagues. When someone at a club asks "what's his high-speed running distance compared to other centre-backs in the league?" - that's SkillCorner. What you should know: You probably won't touch this until you're in a role. But understanding what physical data exists is valuable even before you have access. Clubs increasingly want analysts who can blend event data with physical data, and knowing the landscape puts you ahead.
Impect
What it is: A data and analytics company focused on packing rate and other advanced metrics that measure how many opponents a player bypasses with their actions. What clubs use it for: Evaluating how effective a player is at breaking lines - whether through passing, dribbling, or receiving. Packing data answers a fundamentally different question to traditional stats. It's not about how many passes you complete. It's about how many defenders those passes take out of the game. What you should know: Impect's metrics are increasingly used in recruitment departments across Europe. Even without access, understanding the concept of packing shows you're thinking about football beyond goals and assists. That matters in interviews.
Opta (Stats Perform)
What it is: One of the oldest and most established football data providers. Collects event data from hundreds of leagues worldwide. What clubs use it for: Event data for match analysis, historical performance data, league-wide comparisons. Opta data powers many of the stats you see on TV broadcasts and in media coverage. What you should know: Opta is now part of Stats Perform, which also provides AI-driven analytics products. Many clubs have Opta data integrated directly into their internal systems. If you've used FBref historically, that was Opta data you were looking at.
Hudl Sportscode
What it is: Video coding and analysis software. What it costs: Club licences. Expensive for individuals. What you use it for: Tagging and coding match footage, building video presentations for coaches, creating clip packages.
The tactical analyst's bread and butter. If you want to work with coaching staff rather than recruitment departments, this is where you'll spend most of your time.
Coding matches in real time, tagging key events, building timelines, creating the video presentations that coaches watch before every game - it all runs through Sportscode. If tactical analysis is your path, learn this.
SciSports
What it is: A data intelligence platform specialising in player potential modelling and development forecasting. What clubs use it for: Projecting how a player will develop over the next 2-3 years, identifying undervalued talent before the market catches on. SciSports doesn't just tell you how good a player is now - it models how good they could become. That's a different question, and one recruitment departments care about deeply. What you should know: Particularly popular with clubs focused on buying to develop and sell. If a club's transfer model is built on finding players whose value will increase, SciSports is often part of that process.
Driblab
What it is: A football analytics consultancy that provides bespoke data analysis for clubs, agents, and federations. Covers over 300,000 players across 200+ competitions. What clubs use it for: Custom-built analytical frameworks tailored to a club's playing philosophy. Unlike off-the-shelf data platforms, Driblab works more like a consultancy - they build models around what a specific club needs rather than providing a one-size-fits-all product. What you should know: Driblab is a good example of how some clubs outsource their analytics rather than building everything in-house. Understanding that this consultancy model exists is useful if you're thinking about career paths beyond working directly at a club.
Twelve Football
What it is: An AI-powered analytics platform based in Stockholm. Their product Earpiece allows clubs to scout players through conversational AI. What clubs use it for: Scouting, talent discovery, match analysis, and opposition scouting. Twelve builds customised metrics for individual clubs to measure how players contribute to a specific team's style. They work with clubs from the Singapore Premier League to the English Premier League. What you should know: Twelve represents where the industry is heading - AI-driven analysis that adapts to each club's context rather than generic league-wide metrics. I've written more about this in AI Is Changing Who Wins in Football.
Analytics FC
What it is: A sports consultancy that provides analytics services to clubs, leagues, and federations. What clubs use it for: Strategic consulting, performance analytics, and recruitment support. Like Driblab, they operate as an external consultancy rather than a software platform. What you should know: Worth knowing because it represents a different career path. Some clubs hire analysts internally; others outsource to companies like Analytics FC and Driblab. If working directly at a club isn't the only route you're considering, the consultancy model is worth exploring.
How these tools work together in practice
In a typical recruitment workflow at my level:
- Transfermarkt - Initial context on a player's career and contract
- Wyscout - Video footage and basic stats
- StatsBomb or Opta data pulled into Python - Deeper analysis, percentile rankings, scatter plots, radar charts
- SkillCorner - Physical profiling
- Tableau or presentation software - Packaging everything into a dossier
No single tool does everything. The skill is knowing which tool answers which question, and stitching them together into a recommendation a sporting director can act on.
What you actually need at each stage
Not every tool matters at every stage. This is the breakdown I share with people I mentor:
| Stage | Essential | Worth Learning | Can Wait |
|---|---|---|---|
| Just starting | Excel, Transfermarkt, YouTube | Wyscout, FBref (historical) | Python, Tableau |
| Building a portfolio | Wyscout, Python OR Tableau | StatsBomb open data | SkillCorner, Sportscode |
| Applying for roles | All of the above + whatever the role requires | R (if data-heavy role) | |
| Working in a role | Whatever your club or agency uses | Everything else becomes context-dependent |
The key point: you don't need everything at once. The "just starting" column is deliberately simple. Excel, Transfermarkt, and watching football with intention on YouTube. That's it. That's enough to begin.
Too many people try to learn Python, Tableau, Wyscout, and Sportscode simultaneously in their first month. The enthusiasm is there. But they spread themselves thin and end up mediocre at all of them instead of competent at one or two.
Go deep before you go wide. The rest comes with time and with the demands of whatever role you land.
If you want a visual roadmap of the full journey from beginner to employed, I put together a free career roadmap that maps the whole thing out stage by stage.
The mistakes people make with tools
I've watched dozens of people go through this process. The same mistakes come up every time, and I made a few of them myself.
- Learning Python before understanding what questions to ask. Python is a tool for answering questions. If you don't know what you're trying to find out, you'll write code that goes nowhere. Spend time watching games, reading analysis, and understanding football problems before you open a Jupyter notebook.
- Spending months on Tableau when they should be watching games. Beautiful visuals mean nothing if the analysis behind them is shallow. A well-designed dashboard with bad thinking is still bad analysis.
- Ignoring Excel because it seems basic. I've said it already but it bears repeating. Excel is not beneath you. It's the tool you'll use most.
- Trying to learn everything at once. The best analysts I know are brilliant with two or three tools, not average with ten. Depth beats breadth every time.
- Buying expensive courses to learn tools you can learn for free on YouTube. There are excellent free tutorials for Python, Tableau, Excel, and almost every other tool on this list. Before you spend money, exhaust the free options.
The pattern I see in people who actually break into the industry is the same every time: they picked two or three tools, got genuinely good at them, and produced visible work. No secret combination. No magic stack. Just reps.
Frequently asked questions
What tools do professional football analysts use?
The core tools are:
- Excel - Data cleaning, squad management, day-to-day analysis
- Python - Statistical modelling, automation, advanced analysis
- Tableau - Data visualisation and dashboards
- Wyscout - Video footage and player data (near-universal across clubs)
Clubs also subscribe to data providers like StatsBomb, Opta (Stats Perform), SkillCorner, and Impect for advanced event data, physical tracking data, and metrics like packing rate. Tactical analysts use Hudl Sportscode for video coding. The exact stack depends on the club and role.
Where can I get free football data?
- StatsBomb open data library - The best free source. Detailed event data from multiple competitions.
- Transfermarkt - Free squad, transfer, and market value data.
- FBref - Still has historical statistics, but lost its Opta licence in January 2026.
- Understat - Expected goals data for the top five European leagues.
Do I need to know Python to work in football analytics?
For data analyst roles, Python is increasingly expected. For tactical analyst or scouting roles, it's not required. If you're drawn to the data side, learning Python will give you a significant edge over candidates who only use Excel. If you prefer video and tactical analysis, focus on Sportscode and presentation skills instead.
What happened to FBref?
FBref lost its Opta data licence in January 2026. The site still hosts historical data which remains useful for research, but it no longer receives updated advanced statistics. The StatsBomb open data library and Wyscout are now the primary alternatives for accessing detailed football data.
Is Wyscout worth paying for?
If you're serious about working in football, yes. Wyscout is the industry standard video and data platform used by clubs worldwide. The personal licence (from €299/year) gives you access to footage and basic statistics across hundreds of leagues. It's the single most useful paid tool for aspiring analysts and scouts.
Where to go from here
You don't need every tool on this list.
Pick your direction - data or tactical. Start with the essentials for that path. Build something real. Share it publicly. Expand your tools as your work demands it.
The tools don't make the analyst. The thinking does. Every good analyst I've worked with got there the same way: they picked two or three tools, went deep, and produced work that showed they could think about football clearly.
If you're not sure where to start with the broader journey, I wrote a full guide on how to become a football analyst that covers the entire pathway. And if you want a step-by-step visual of the route from beginner to employed, grab the free career roadmap.
Start small. Start now.