How to Use FPL Statistics Effectively to Pick Players

Effective use of statistics in FPL is enormously valuable in giving you an edge. We don’t claim to a member of the elite FPL analytics crew but we know how to use FPL statistics to pick a team (or at least help pick a team) and we want to show you how.

In this piece, we will look at why FPL stats matter, what the best FPL stats are, look at some free fpl stats websites, show you how to use FPL data and, most importantly, how to analyse and use FPL data to pick a team.

Why FPL Statistics Matter

FPL statistics matter for three key reasons:

1. Reliance on the Eye Test is not feasible: Making smart FPL picks is already incredibly difficult. It’s not just hard for you but its hard for the sporting directors who are trying to find good players for their team in real life. If you rely solely on what your eyes see then you need to watch 900 minutes of football at the weekend which just isn’t happening.
2. Uncovering Players: Predictive fantasy football stats (like expected goals (xG), expected assists (xA), and big chances created) help you look beyond surface-level results. These metrics show which players are consistently getting into the right positions, even if they haven’t hauled recently. In other words, they help you spot players who are due a return—before the rest of the FPL world catches on.
3. Removing Emotion From Decision-Making: Even the biggest FPL data analytics nerds make this mistake from time to time. You watch your player blank again, rage-transfer him out, only to see him haul the next week. Sound familiar? That’s emotion taking the wheel—and it can seriously derail your season. Using stats is one of the best ways to stay grounded. When emotions start nudging you toward rash decisions—like chasing last week’s hat-trick hero or ditching a player who blanked twice—stats pull you back to reality.

Best FPL Statistics to Track and Use

If you are serious about beating your mates to mini-league glory, then knowing which are the best fpl stats to use can give you a serious edge. Here’s a breakdown of the best FPL statistics to track and how they help identify the players most likely to deliver.

Minutes

The most boring of FPL stats to track but an incredibly valuable one. Put simply, players who don’t play, don’t score FPL points. 

Obviously, some players do – occasionally – go on hot streaks coming off the bench, or keep returning despite getting hooked after 60 minutes or so

But far more often than not, this is unsustainable.

The more the player is on the pitch, the better their chance of getting returns. It’s as simple as that. Minutes are one of the first things we look at, when evaluating any player.

Expected Goals (xG), Expected Assists (xA) and Expected Goal Involvement (xGI)

xG measures the quality of a chance based on factors like shot location, angle, and type of assist. A higher xG means a better chance of scoring. xA or xAG works the same way, but for potential assists. xGI or xG+xA is merely the two added together.

Take a break from this piece and have a watch of this 4 minute video from the Athletic if you don’t have a clue what xG is.

These two or three stats measure how good the chances your FPL pick is getting and that’s what makes them some of the best FPL stats to consider. You can see how likely it is they would have scored and how many good chances they are getting – all in one stat.

Big Chances Created, Touches in the Box and Shots

Before xG, these were the FPL data that engaged managers used most. You will still hear and see some of the old guard talking about them quite a lot and they still have value in my opinion.

Big chances created show when players create good chances, touches in the box show when a player is in the most dangerous area and shots show, well, shots. They’re still good for identifying good picks.

Expected Goals Conceded (xGC)

xGC measures the quality of chances a team is allowing their opponents to take. The higher a team’s xGC, the more likely they are to concede goals based on the chances they’re giving up.

It’s essentially the defensive equivalent of xG. Instead of telling you how dangerous a team is going forward, it tells you how vulnerable they are at the back.

This is useful when picking defenders and goalkeepers. A team with low xGC is limiting high-quality chances—which means they’re more likely to keep clean sheets, even if they concede the odd shot.

xGC is the main metric we use to determine our fixture difficulty ratings for attacking teams every week.

Where to Find Free FPL Statisitics?

Besides our occasional foray into big stats deep dives, where can you find useful free fpl statistics?

The first place to go is the Official FPL website. Ignore this ICT index nonsense and look at the minutes, xG, xA, xGI and xGC of players here:

If you scroll down you can even see the full season, per 90 data and previous seasons (this is example is all Mo Salah).

The trouble with using the Official FPL website for fantasy football stats is that there isn’t a good comparison tool (yet – depends if they listened to me when I filled in that survey).

There are number of free fpl statistics websites you can use though:

• FBref.com – The OG source for the FPL analytics crew.
• Opta is probably the most recognisable name in fantasy football stats. 
• WhoScored.com – also has a section on stats. 
• SofaScore – I use SofaScore when I look back at the games over the weekend. I like how you can click on a player and see their xG as well as look at the xG of each shot.

All of these places offer free FPL statistics but you can also pay to have access to things like Fantasy Football Scout to get access to more data that you can play with interactively.

You can also use FPL Review. That looks at your team and uses these underlying data points to suggest transfers and “paths” to success. We are planning on writing something about how to use this tool so keep your eyes peeled for that or search for it on here.

How to Analyse and Use FPL Statistics to Pick a Team

There are lots of various FPL data you can use but the simplest ones are xG and xGC so I am going to focus on those. In my opinion, these are the best stats to use for your FPL team anyway.

xG Difference Versus G (Delta or Δ) Shows Elite Strikers

xG shows how many goals an average team and/or player would score that goal. So, the first way we can use xG is to determine how much better a player is than the average. This is because xG does not account for who is shooting. 

Players like Harry Kane, Alexander Isak and Heung-Min Son have historically outperformed their xG, so we know they are/were elite finishers. In contrast, players like Darwin Nunez needed way more high-quality chances to score but his skill comes from getting into those positions in the first place.

So, if you look at the long-term statistics of a player and see that he is better than his xG, then he is likely a good goalscorer.

xG Shows How Many Chances a Player Gets

xG can also show how many good chances a player is getting.

We can use this as a proxy for how likely we think that player is to score.

Taking Liverpool in the 24/25 season as an example. We know they scored the highest amount of goals (86) and had the highest xG (83.46). Therefore, I know they were the best attack at creating chances, I just need to find a player who gets a good amount of them. Naturally you would see Salah…

xG Shows Who Is Overperforming and Underperforming

We can also use xG to identify players who are overperforming or underperforming. This one is possibly a bit contentious.

In the 24/25 season, I used xG (and fixtures) to identify a good forward pick in Chris Wood when I wildcarded in gameweek 6. His long-term stats were good and his stats last year and the year before we positive and showing a good number of chances coming his way as well as a reasonable ability to convert. 

It was this same logic that led me to sell him in gameweek 11, you can read all about it here. I was right by the way he did blank in 12, 14 and 16. But in 17 when the fixtures got good again I bought Raul Jimenez because his underlyings were similar and he was mega cheap. I didn’t take my own advice and look for players who play 90 and that was stupid (THE WEBSITE IS CALLED FULL90FPL FFS!).

Anyway, ask me in gameweek 17 and I would argue, as would many other managers, that I made the “correct decision” and that Wood overperformed his stats. As you can tell, that doesn’t necessarily always work out.

xGC Shows Which Defenders to Buy

A team with low xGC is limiting high-quality chances—which means they’re more likely to keep clean sheets, even if they concede the odd shot.

For example, if a team has conceded a couple of goals recently but still boasts a consistently low xGC, that suggests the defence is solid and the goals were likely down to finishing variance (e.g. worldies or penalties).

xGC Shows Which Fixture To Target

As well as the above, xGC helps us pick good fixture runs. If a team plays against three teams with high xGC in a run of five then we will look to grab a player from that team.

We won’t buy just any player and hope that he gets a return of course, we will look to identify a good pick from that team, but the xGC has given us a team to target which is incredibly valuable.

Expected Stats Help Evaluate Your Decision-Making

You may bring in a player and think he is great. He might blank five weeks in a row in a series of easy fixtures and you might think: “I am an idiot, this guy is awful.” But that might not be true. 

If he got an xG of three over those five weeks and didn’t score once then you can consider yourself unlucky and know that the pick was a good one but the outcome just didn’t go your way. This is one of the big problems of football and one of the reasons why we love football – variance is everywhere.

How To Avoid Mistakes

To avoid bad picks in FPL, we need to think about the limitations of data and the limitations of those who use the data. 

Don’t Rely on Small Sample Sizes

There is so much randomness and noise in football statistics that you need to be careful. A two or three-game burst can be driven by luck, soft fixtures, or freak goals (hello, 30-yard screamers). Without context or enough data, we risk overreacting to short-term noise instead of spotting long-term trends.

Confirmation Bias: Seeing What You Want to See

Confirmation bias is when we seek out information that supports what we already believe—and ignore anything that contradicts it. In FPL, this is a classic trap.

Let’s say you’re convinced a certain player you like the look of is about to explode. You scroll through stats and highlight his xG, his shots in the box, that one assist. But you ignore the missed big chances, reduced minutes, or tough upcoming fixtures. Why? Because you want your opinion to be right.

Try not to think about how you can prove your point but think about what does the data say.

Don’t Ignore Context

All data needs to be read in context. If Man City and Chelsea are playing like crap, we shouldn’t be surprised that Palmer and Haaland don’t score as many as their xG say they should.

xG is Probability – Not Guaranteed

xG doesn’t predict goals like a crystal ball. It tells you the likelihood of a goal being scored based on the quality of the chance.

So if a player racks up 0.8 xG in a game, that doesn’t mean they should have scored or that they definitely will next time. It means, based on thousands of similar chances, a player typically scores about 80% of the time. It’s like flipping a coin. Do it once, anything can happen. But flip it enough times, and it’ll start landing how you’d expect.

FPL managers often get frustrated when their high xG player blanks, or worse—sell them thinking they’re “wasteful.” But that’s missing the point. xG isn’t a one-game tool. It shines over time. A player consistently posting strong xG numbers is putting themselves in the right places—and over a stretch of matches, that’s going to lead to points.

Use xG to spot patterns, not predict single match results. It’s about process over outcome – which is the key to smart FPL management.

Lies, Damned Lies and FPL Statistics

Closely linked to the above. If I took 100 0.01 xg shots then my xG would be 1 but the probability I had scored would be about 64%. 

Looking back, that shot is rated at 0.01 xG because I know it goes in about once every 100 attempts. But looking forward, missing it 99 times doesn’t mean the next one is due to go in. Each attempt still has just a 1 in 100 chance of scoring. That’s why even across 100 shots worth 0.01 xG each, there’s only about a 64% chance of scoring at least once.

So, be careful of guys shooting too much and never delivering.

Be Prepared to Change Your Mind

If you haven’t read Superforecasting then I would encourage you to. It is basically a book about how people predict things with accuracy. One of the key takeaways is that those who predict things right more often, change their views when new data arises.

Don’t be a politician and refuse to U-turn when presented with facts. 

This is one I find particularly tough because I have to argue with people on Reddit as to why I am right. And then next week I might have to backtrack that. Sometimes that’s because I am wrong but I like to think it is because new data has emerged.

That’s It.

In a game that thrives on emotion and impulse, stats give you clarity. They strip away the noise, the knee-jerks, and the hype—and replace them with a framework for smart, consistent decision-making.

By focusing on metrics like xG, xA, xGC, and key involvement stats, you’re not just picking players based on vibes or highlight reels. You’re betting on repeatable actions—the kind that lead to points over time.

Remember, not every great stat leads to instant returns. But over a 38-game season, the managers who trust the data and play the long game are usually the ones climbing the ranks come May.

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