It is an easy question to ask. The amount of information surrounding professional football has grown enormously. Analysis meetings, individual clips, opposition reports, set piece work, physical information, nutrition, recovery and tactical preparation all compete for the player’s attention. At the same time, the amount of data and analysis available to staff continues to increase.

Every time I think about the amount of data points within a game and the potential analysis that could lead to, I’ll be honest, it gets a little overwhelming. There are literally thousands of data points, an unlimited combination of clips but only a limited amount of time to process things.

The obvious assumption is that more analysis must eventually lead to information overload. But I think the reality is a little more complicated than that.

The issue may not be how much analysis we are doing. It may be whether we are being intentional enough about when we deliver it, what we expect players to take from it and how much cognitive demand we are placing on them.

I remember the podcast I did with Josh Kirk, head of analysis at Brentford, and he made a comments saying something along the lines of ‘I don’t know how players cope… They are given so much, and expected to take it all on board, whilst trying to perform at the top of their game”.

That leads to ‘Analysis Periodisation’ that my recent podcast guest, Andrew Douglas described really well.

The idea came from looking at how intentional coaches are with the delivery of training. Football clubs already periodise physical work. They think about technical and tactical development across the week. Training sessions are designed with a particular purpose, and the demands placed on players change depending on what the team is trying to achieve.

Should the same thinking should apply to analysis?

If we are deliberate about the physical load we place on a player, why wouldn’t we also be deliberate about the cognitive load? I think this also goes for coaches too, not just the players.

That seems like a fairly obvious question, but it is one that is easy to overlook.

As analysts, we are often focused on what information we can provide. We have the data, we have the clips and we have the technology to put it all together. There is an understandable temptation to make use of those resources because we know they might contain something useful.

But useful information is not necessarily useful if the player cannot absorb it.

Should analysts be trying to focus on what players can understand and remember, rather than simply what they can be shown? That distinction is important. The objective of an analysis session is not to maximise the amount of information that passes in front of a player. It is to give them knowledge that can ultimately support what they do on the pitch.

That means we need to think about the complexity of the analysis we deliver.

The definition of complexity goes well beyond simply asking whether a meeting is 15 minutes or 45 minutes long. It can include the number of clips being shown, the length of those clips, the amount of detail within them, the number of themes being discussed and whether the information is completely new or something the player has already encountered.

All of those things affect the cognitive demand of an analysis session.

That creates an interesting challenge for analysts. We can produce an excellent analysis session that contains genuinely valuable information and still deliver it badly if we don’t consider what the player is being asked to process.

This leads to another interesting point…

Analysis happens in a very different environment from football. A player sitting in a meeting room has the benefit of a paused video, fancy telestrations, the ability to rewind and somebody explaining exactly what they should be looking at. None of those things exist when the player is actually on the pitch.

That does not mean we should stop using them. They can obviously be extremely useful. But it does raise an interesting question about what we are trying to teach. If we always pause the video at exactly the right moment and draw the relevant spaces on the screen, we have made the problem easier to understand. But we have also removed some of the uncertainty and speed that exists in the game itself.

Apparently, Ange Postecoglou, during his time at Celtic, did not want video to be paused in certain situations. Whether that story is entirely accurate or not, the principle is interesting. If the video keeps moving, the player has to think at something closer to the speed of the game.

The same applies to telestration. Drawing an arrow on the screen can make an idea much clearer, but there are no arrows on a football pitch. Again, this is not an argument against using these tools. It is an argument for thinking about when and why we use them. Sometimes we want to make the information as obvious as possible. At other times, perhaps we want the player to do more of the thinking themselves.

That is where the idea of periodising analysis becomes particularly interesting.

Should it be, that sessions could move from relatively low cognitive load towards higher cognitive load. Rather than every analysis session being designed in the same way, the demand placed on the players could change depending on the purpose of the session and where they were in the wider training process.

It is a relatively simple concept, but it challenges the way we often think about analysis.

We tend to ask whether the content is good. Is the clip relevant? Is the insight accurate? Is the presentation clear? Does the player understand the tactical point?

Perhaps we should also be asking whether the analysis is appropriate for that particular moment.

A player can understand something in an analysis room without necessarily being able to recall it when the game is moving at full speed. Knowledge acquisition and skill acquisition are not the same thing. That distinction becomes increasingly important as the amount of analysis available to professional football continues to grow.

We now have an extraordinary ability to analyse almost every aspect of the game. There is more data, more video and more technology available to coaches and analysts than ever before. The temptation is therefore to keep adding to what we give players.

But perhaps the next step in the evolution of football analysis is not finding more things to show. It is becoming better at deciding what players actually need to see, when they need to see it and how they need to interact with it.

That might mean that the best analysis session is sometimes the one with fewer clips. It might mean revisiting something familiar rather than introducing another new concept. It might mean allowing the video to run rather than stopping it every few seconds. It might even mean taking players away from the screen altogether and getting them to interact with a tactics board, a tactics mat or a ball.

The point is not to make analysis simpler for the sake of it. It is to make it more intentional.

I think that distinction matters because there is a danger that the growth of analytics becomes confused with the growth of information. Having more information does not automatically mean that players and coaches will make better decisions. The value comes from how that information is understood, remembered and ultimately applied.

So, is there too much analysis in football?

Possibly. But I’m not sure that is quite the right question.

Perhaps the better question is whether we are giving players the right amount of information, in the right format, at the right time, with the right level of cognitive demand.

Anyway, if you found this interesting, you may want to check out the full conversation with Andy, just below. We speak about the role of a coach analyst, more on coach development, and plenty of other related topics which I found really interesting.

I’d be keen to hear how other analysts and coaches think about this. Are we becoming better at managing the amount and timing of information we give players, or are we still guilty of assuming that more analysis is always better?

Thanks for reading 👌🏼

One massive benefit of doing my podcast is being able to pick the brains or people working at the very top of the game. Ask questions, get opinions, and of course try to learn as much as I can.

During my recent conversation with Jonny Whitmore, Director of Analytics at Opta, he mentioned a statistic that genuinely caught my attention. Some recent analyst roles that he had hired for, had attracted around 1,500 applications.

On the surface, that number tells you something about how competitive the football analytics industry has become. But I think it also tells us something much more interesting about how the profession itself is changing.

Ten or fifteen years ago, having the ability to code or build statistical models was enough to make you stand out. Football analytics was still a relatively new field, and simply being able to work with data was a significant advantage.

Today, that is no longer the case.

The industry has matured. There is more public data than ever before, there are countless online courses teaching everything from Python and SQL to machine learning, and open source projects have made it easier for people to develop their skills and showcase their work. AI tools such as ChatGPT and Claude have also made it much easier for people to learn new techniques and accelerate their technical development.

That is undoubtedly a positive thing. The analytics community has grown because knowledge has been shared more openly, and the number of people capable of contributing to the field has increased significantly.

However, there is a consequence to that progress.

Technical skills alone are becoming less of a differentiator.

If 1,500 people are applying for the same role, it is unlikely that knowing Python is what separates the successful candidate from everyone else. The ability to manipulate data, create visualisations or build models is increasingly becoming the entry point rather than the thing that makes someone exceptional.

This raises a more interesting question…

What value does an analyst actually create?

For years, aspiring analysts have understandably focused on improving their technical ability. Which programming language should I learn? Which statistical methods should I understand? How can I build a more advanced model? These are all useful questions, and technical development remains incredibly important.

But perhaps they are not the questions that matter most anymore. The more important question is whether the analysis helps someone make a better decision.

One of the things that stood out from my conversation with Jonny was that we spent surprisingly little time discussing algorithms or coding. Instead, we kept returning to the importance of communication, application and understanding the problems that need solving.

That is a subtle but important shift.

The value of analytics has never really come from producing more numbers. It comes from using information to help people make better decisions.

A recruitment analyst does not need another metric simply because another metric can be created. They need information that helps them identify the right player. A coach does not need the most complex dashboard possible. They need insights that improve preparation and decision making. A broadcaster does not need endless statistics. They need context that helps supporters understand the game in a more meaningful way.

The challenge has changed.

For much of the last decade, football analytics was focused on proving what could be measured. The industry wanted richer event data, tracking data, better expected goals models, possession value models and increasingly sophisticated ways of describing what happens on the pitch.

Those developments have transformed our understanding of football. We can now quantify elements of the game that would have seemed impossible to analyse not long ago. We can evaluate chance quality, passing difficulty, pressing intensity, off ball movement and countless other aspects of performance.

The answer to the question, “Can we measure this?” is increasingly yes.

The more interesting question is whether we should.

Measurement itself is no longer the difficult part. The difficult part is judgement. Understanding which problems are worth solving, which questions are worth asking and which insights will genuinely influence decisions.

That is where great analysts separate themselves.

The best analysts are not necessarily the ones who can build the most complicated models. They are the ones who understand why a model matters in the first place. They know how to connect data with football. They understand the context behind the numbers and can explain complex ideas in a way that coaches, recruiters and decision makers can actually use.

Technical ability still matters. Of course it does. Statistics matter. Coding matters. Understanding methodology matters.

But increasingly, those skills are becoming the foundation rather than the differentiator.

The analysts who stand out will be those who combine technical knowledge with curiosity, judgement and communication.

Interestingly, when I asked Jonny whether he thought artificial intelligence would create more or fewer analyst jobs, his answer was more nuanced than simply saying it would replace people.

His view was that AI could potentially lead to fewer analyst roles in the short term as organisations become more efficient and some tasks become automated. However, he also suggested that this may only be a temporary effect. As organisations begin to understand what skilled analysts can achieve when they are supported by AI, rather than replaced by it, demand could increase again.

I think that is an important distinction.

AI is likely to change the role of the analyst, but changing a profession is not the same as removing it. Throughout history, technology has often reduced the time required to complete certain tasks, but it has also raised expectations. The people who benefit most are usually those who know how to use new tools to create more value.

The same is likely to be true with AI.

A person who can use AI to write code faster, explore ideas more efficiently and test hypotheses more quickly will be more valuable than someone who simply performs those tasks manually. But the technology still needs someone who understands the problem, questions the output and knows whether the answer is actually useful.

That is the part that is much harder to automate.

Football analytics has, in many ways, become a victim of its own success. The barriers to entry have never been lower, but that means the challenge of standing out has never been greater.

More people have access to the same tools. More people can produce impressive looking analysis. More people can build models and create visualisations.

That is a good thing for the industry.

But it means the definition of a great analyst has to evolve.

The future does not belong solely to the people who can create the most advanced models. It belongs to the people who understand football problems, ask better questions and communicate ideas clearly enough that others trust their conclusions.

The question aspiring analysts should perhaps be asking is no longer:

“Can I analyse this?”

It is:

“Can this analysis help someone make a better decision?”

Because that is where the real value lies.

Not in the complexity of the model. Not in the number of metrics produced. Not in how technically impressive something appears. The best analysts have always been problem solvers. The difference today is that everyone has access to better tools.

The advantage will belong to those who know how to use them to solve the right problems.

Check out the full conversation with Jonny, right here:

I’d be interested to hear your thoughts. Has football analytics reached a point where technical skills are no longer the biggest differentiator? Or do you think coding and modelling will continue to be the qualities organisations value most when hiring analysts?

Thanks for reading 👌🏼

Chris Gill here and as you can see, I’ve joined Substack 👀.

Firstly, to anyone from my existing email list, I have migrated from the old platform for sending emails (MailerLite) to here (Substack) instead.

You can of course unsubscribe at any time, but if you do decide to stick around, it would be great to stay connected.

So, why Substack, and what to expect?

Since starting my journey of ‘creating content’ I’ve experimented on various platforms and social medias. And to be honest, it’s hard work!

A website. A podcast. An email list. Don’t forget to tweet, oh and then there’s Instagram and Bluesky and whatever else. Short form, long form, etc.

Honestly, it’s exhausting.

Especially for someone that doesn’t typically use social media (and when this isn’t my full time job!)

So, my goal moving forward is to simplify things. No strict deadlines for ‘X amount of posts a week’ or anything like that.

I’ve built a new website and purposely kept it super simple.

Simple, but I really like it and it does exactly what I want it to do.

I built it completely using Claude Code. I might post more on the experience of that as it really was fascinating for me. It truly has opened my eyes to what else could be possible and so I’ll likely be playing around and see what other cool things I may be able to create.

My old website (which was also done on Mailerlite) had a number of blog posts. However, if I’m being honest they weren’t great, or at least not as good as I would have liked them to be. Partly because I felt some were rushed or published ‘for the sake of posting something’.

That is where I intend to change things up.

This Substack is where I will be writing from now. It might not be as regular, but I want to use this as a space to curate more in depth articles.

In fact, it definitely won’t be as regular. No weekly schedule or strict deadlines. I’ll share only when I have something I want to write about.

The main goal: Improve my writing, share and build on ideas, and connect with others.

So, let’s see how it goes…

As the sub-header indicates, I will of course be writing mainly about football analysis, scouting and recruitment. But there may be times I express my thoughts and opinions of other things too. Basically, I love a rabbit hole. And if I feel like going down a random one, I’ll see where it takes me!

As I’m new here, I’m still finding my way around. So any recommendations for newsletters or publications that I should be checking out, please do let me know. I’m into all things football, tactics, analysis, finance, business and tech!

For those of you that follow along with my podcast, the next guest episode is with Jonny Whitmore (Director or analytics at Opta). In the next few months, there will also be episodes with:

• A recruitment analyst from a TOP club

• An expert on transfer and permit rules

• An individual analyst

• A football data scientist

• A head of football analytics

I think that’s enough for my first post. Already looking forward to my next!

Thanks for reading, and I hope you stick around 👌🏼