Goals are not enough: How data created the modern striker

“We don’t see goals like that anymore.” That was former Liverpool defender and Sky Sports analyst Jamie Carragher’s reaction having just seen Aston Villa striker Jhon Durán unleash a ferocious strike from 35 yards that slammed into the back of the net, sealing a 3-2 win over Everton last month. It’s not entirely true, but almost.

In the 2006-07 Premier League season, 22.3% of non-penalty goals were scored from outside the box. By 2023-24, that had nearly halved to 12.4%. However, that former campaign was the lowest-scoring season in Premier League history (931 goals; 2.45 per game), while last season broke the record for the most goals (1,246; 3.28 per game).

A striker’s primary role is to focus their efforts inside the penalty area rather than drift into less threatening areas or attempt long-range, goal-of-the-month contenders. However, this statistic highlights how data-driven insights have altered how teams score goals and how the striker’s role has evolved.

Tactical changes driven by data have shaped these shifts. To adapt and thrive, strikers now work closely with analysts and specialist coaches who provide guidance on the optimal number of touches in the box before shooting, the direction of these touches, key production zones, and their opponent’s weaknesses.

Strikers were once judged by a single metric: goals. Today, they are assessed on much more, and data has been at the heart of this evolution, transforming the way clubs recruit, train, and create chances for their finishers.

Durán, for example, has caught the eye for Villa this season with his spectacular goals against Everton and Bayern Munich, but it’s his hunger for close-quarter battles that have made him a crucial part of Unai Emery’s team. He averages 0.98 tackles per 90 minutes, placing him third among Premier League forwards this season. He also wins an average of 1.96 aerial duels per 90 minutes in the attacking third, the fourth-highest in England’s top flight. Durán has won 16 aerial duels for Villa this season, the most of any player across all competitions for the club, followed by Amadou Onana with 10. Those contributions, in the right areas of the pitch, keep the pressure on opponents and lead to more chances for the team as a whole.

Using data to optimise goal-scoring opportunities is nothing new. In the early 2000s, Bolton manager Sam Allardyce pioneered data use, playing the percentages to get the best out of target man Kevin Davies. But today, data is pervasive in nearly all areas of football operations, including tactical planning and scouting, all geared towards the game’s main objective: scoring goals and winning matches.

Since 2012, clubs and analysts have used expected goals (xG), a transformative metric for measuring the quality of goal-scoring chances. It’s no surprise that xG reveals the best areas to score are inside the penalty box, particularly near the six-yard box where tap-ins, one-on-one situations, and cutbacks across goal have the highest values.

Clubs and players aim to weaponise this data to create optimal goal-scoring opportunities for their forwards, which is where experts like Allan Russell come in. England‘s former attacking coach has turned goal scoring into a science through his training programme “Superior Striker.” His bespoke drills replicate game situations based on specific data inputs, such as production zones and the types of chances a striker is likely to receive.

“I break down a player’s last 50 chances and build a training programme to improve things like finishing or positioning,” Russell explains while using his iPad to navigate an interface reminiscent of the film Minority Report.

“They’re all coded in a certain way, and I make handwritten notes. They’re split into sections: where the move started, the final phase, the action, the production zone, the outcome and the coaching point. Then I take the data and allocate training as follows: 24% on crosses and cutbacks, 17% on one-on-ones with the goalkeeper, 17% on one-on-ones with a goalkeeper and defender, 20% on reactive finishing, and 22% on combination drills that incorporate all elements.”

Russell demonstrates how data interpretation provide game-changing guidance.

“If you take a forward touch in production zone three, around the edge of the box, there’s an 80% chance your shot will be blocked in a high-level game,” he says. “A forward touch exposes the ball to the defender, whereas a parallel safety touch away from pressure keeps me between the defender and the ball while still allowing me to shoot.

“The data — and your intuition — will tell you that in certain situations, specific touches or movements won’t realistically lead to more goals. Some people are detailed with their training, but not in the right way.”

Players like Harry Kane, Ivan Toney, and Danny Welbeck have drawn on Russell’s expertise to maximise their efficiency in front of goal. Another player benefiting from his knowledge is Chicago Fire striker Hugo Cuypers, who joined the MLS team from KAA Gent in February for a club-record $12 million fee. Cuypers says Russell’s data application has played a critical role in helping him maximise his chances in key areas of the pitch.

“He divided the penalty area into four zones and told me that the more chances I get in the first production zone, the higher my chances of scoring,” he says.

This data has influenced Cuypers’ off-the-ball movement and positioning. While defenders focus on the ball, he waits for space to open up and attacks it with intent. Russell’s analysis also helps Cuypers determine the number of touches he should take before shooting. This has helped the forward score 10 times for a team struggling at the bottom of MLS’ Eastern Conference.

Cuypers adds: “I’m 27 now, and seeing how much progress I still need to make in areas I once thought were strengths — areas I didn’t think needed much attention — has been eye-opening.”

Still, the former Belgium U19 international insists that, while data is important, a striker can’t ignore their intuition and experience. Whether it’s a sixth sense for where the ball will drop or a one-touch finish, instinct remains the weapon that renders goalkeepers helpless. For Cuypers, data-driven training is about forming habits that enhance a striker’s natural abilities.

“It’s about finding the fine line between being aware of the data but not overthinking during games,” he says. “When the play is developing, it’s about positioning yourself in the right spot. You have that split second to anticipate what the defender can’t. Every one-touch finish is purely instinctive.

“Where training and guidance come in is learning when and how to take that first or second touch, leading to a two-touch finish or two touches that set up a third-touch finish.”

Data has expanded the parameters by which strikers are evaluated, analysing their ability to finish, press, assist and create opportunities. Tactical trends, such as the rise of pressing made famous by coaches like Jürgen Klopp, demand a more versatile forward who contributes both defensively and offensively. This has influenced recruitment strategies.

“Most clubs will have some kind of game model they want to recruit for,” says Benjamin Torvaney, lead data scientist for Ludonautics, a sports consultancy that specialises in data analysis. “We’re now looking at what strikers contribute as a whole to goal scoring, rather than just their individual metric of scoring a goal.”

Ludonautics was founded by Ian Graham, the former director of research at Liverpool. Graham is known for pioneering the data revolution in football, particularly during his time at Anfield where he built the Premier League’s first in-house analytics department that played a pivotal role in transforming the club’s fortunes.

During his 11-year tenure, Liverpool signed Roberto Firmino in 2015. The Brazil forward made 256 Premier League appearances across eight seasons, scoring 82 goals — an average of 0.32 goals per game. While this may seem modest, Firmino established himself as one of the league’s most effective pressing forwards, leading the charge for Klopp’s high-intensity team. In Liverpool’s 2018-19 season, when they won the Champions League and finished second in the Premier League with 97 points, Firmino’s pressing was integral: He ranked fourth in the Premier League for total pressures (771) and second in the Champions League (239).

The use of data continues to evolve, with AI and virtual reality beginning to impact striker training. Russell is developing an app, due for release in November, that uses 3D and 4D animations and data-based drills that could revolutionise training methods. “It will change the way clubs train their strikers,” he says.

Data analysis has already transformed how strikers play and how their effectiveness is measured. Their overall contributions to scoring — whether through direct assists or regaining possession in the opposition’s final third — are now regarded as equally valuable as goals, as long as the team is winning.

Goal-scoring opportunities are increasingly generated not by individual brilliance but through collective efforts that create openings for higher-quality shots. Data has turned strikers into efficient decision-makers driven by goal-scoring intuition.

Durán’s goal against Bayern was a perfect example of what the modern striker has become. It began with Pau Torres delivering a precise pass in behind for the 20-year-old to chase. As the Colombian latched onto the ball 30 yards from goal, he noticed Manuel Neuer off his line and sent a shot arcing over the stranded goalkeeper. The finish appeared instinctive, but the execution was no accident.

Postmatch, Villa goalkeeper Emiliano Martínez revealed that Villa had spent the morning studying footage focusing on Neuer’s tendency to play high. While Durán’s strike may have seemed inspired, it was actually a perfect fusion of human instinct and data-driven analysis, transforming a split-second decision into the kind of finish that once felt born purely of intuition.