Using Analytics to Predict the Impact of New Signings

Sports have always been about skill, passion and strategy. In football, those elements still matter deeply. But in top football leagues, the real advantage often comes from something less visible. Data. Clubs are no longer relying only on gut feeling or reputation when signing players. Instead, they are using analytics to predict how a new signing will actually perform once they are onboarded.

This shift has changed how transfers work. Player signing meetings now include analysts, dashboards and prediction models. For fans following Premier League predictions, this has also changed how teams are judged before a season even starts. Platforms like APWin reflect this data-driven approach by analysing team form, player statistics, tactical trends, and historical performance to assess how new signings can influence matches, making football analysis clearer and more accessible for bettors and fans.

This article explores how analytics is used to predict the impact of new signings, using real Premier League examples and verified performance data.

The Evolution of Analytics in Football Transfers

Football scouting was once based almost entirely on watching matches. Scouts would travel far and wide to watch players. They focused on technique, physical presence, attitude, etc. While this approach still matters, it has clear limits. A player could look impressive in one league but struggle badly in another, and vice versa. 

Around the early 2010s, Premier League clubs began investing heavily in data departments. This was driven by these factors:

• Rising transfer fees
• Increased availability of detailed match data
• Competitive pressure to reduce costly transfer mistakes

Clubs started collecting data on every on-ball and off-ball action. This included passes, pressures, sprints, positioning and decision-making under pressure. The aim was not to replace scouting, but to support it with evidence.

What Is Predictive Analytics in Football?

Predictive analytics combines historical data, statistical models and machine learning to forecast future outcomes. In football transfers, it is used to estimate how a player will adapt to:

• A new league
• A different tactical system
• Higher match intensity
• Increased physical demands

Analysts look at past performance data and adjust it for context. For example, A midfielder playing in a slower league may have strong passing numbers, but analytics will test whether those passes were made under pressure or in open space.

Key Performance Metrics Used in Transfer Analytics

Modern recruitment models rely on plenty of metrics, not just goals and assists. Below are some of the most important ones used by Premier League clubs in 2025. 

Table 1: Common Metrics Used in Player Evaluation

Metric	What It Measures	Why It Matters
Expected Goals (xG)	Quality of chances taken	Predicts sustainable scoring
Expected Assists (xA)	Quality of chances created	Measures creative impact
Pressures per 90	Defensive work rate	Fits high-press systems
Progressive Passes	Forward ball movement	Shows attacking intent
Sprint Distance	Physical output	Indicates league readiness
Duel Success Rate	Physical competitiveness	Important in EPL intensity

These metrics are adjusted for league strength, team style and opponent quality. This helps clubs avoid being misled by raw numbers alone.

Case Study 1: Mohamed Salah – Liverpool

When Liverpool signed Mohamed Salah from Roma in 2017, many questioned the move. His previous Premier League spell at Chelsea had not gone well. However, Liverpool’s analytics team saw something others missed. 

What the Data Showed: At Roma, Salah consistently ranked high for:

• xG per shot
• High-speed runs behind defences
• Touches in the right half-space

Liverpool’s data models suggested that under Jurgen Klopp’s pressing and transition-heavy system, these traits would translate into elite output.

Outcome: Salah scored 32 league goals in the 2017-18 Premier League season, a verified record for a 38-game season. Since then, he has remained one of the most consistent forwards not just in England but in Europe overall.

Case Study 2: Virgil van Dijk – Liverpool

Virgil van Dijk’s transfer from Southampton to Liverpool in January 2018 came with a then-world-record fee for a defender. The price raised eyebrows, but the data supported it.

Liverpool’s analysts focused on key analytical insights like:

• Aerial duel success rate
• Defensive errors leading to shots
• Recovery pace and positioning

Van Dijk ranked among the top central defenders in Europe for defensive efficiency, not just volume. He faced fewer dribbles because of elite positioning, something raw tackling stats often miss.

Impact: Liverpool conceded 22 league goals in the 2018-19 season, the joint-lowest in Premier League history. Van Dijk finished second in the Ballon d’Or voting in 2019, a rare feat for a defender.

Case Study 3: Kevin De Bruyne – Manchester City

Manchester City’s decision to re-sign Kevin De Bruyne from Wolfsburg in 2015 was also driven by analytics. Despite failing at Chelsea (like Salah), City believed his underlying numbers showed elite potential. 

Data Indicators: At Wolfsburg, De Bruyne led the Bundesliga in:

• Chances created
• Key passes per 90
• Expected assists

City’s signing model predicted that his vision and passing would scale very well under Pep Guardiola’s possession-based system.

Results: De Bruyne became one of the Premier League’s most productive and creative midfielders. In the 2019-20 season, he recorded 20 assists, equalling the Premier League record.

January Transfers and Analytics Risk Management

The January transfer window is especially challenging. Clubs have no pre-season to integrate players into their systems. Analytics helps reduce risk during this period, as after January, most European football clubs will be unable to sign any new players for the next 6 months. 

Scouting teams use short-term impact models that focus on tactical familiarity, match fitness levels, injury history, etc. This allows clubs to target players who can contribute even on shorter loan spells, rather than long-term projects. For teams battling relegation, these models can be the difference between survival and dropping.

How Analytics Shapes Premier League Predictions

The same principles used by clubs are now used by analysts and prediction platforms. Match previews increasingly focus on underlying data rather than recent results alone.

Premier League predictions today consider:

• Player availability impact
• Tactical matchups
• Expected goals trends

Table 2: Successful Analytics-Backed Signings (Recent Years)

Player	Club	Transfer Year	Key Data Insight
Mohamed Salah	Liverpool	2017	Elite xG movement
Virgil van Dijk	Liverpool	2018	Defensive efficiency
Kevin De Bruyne	Man City	2015	Chance creation
Rodri	Man City	2019	Ball retention + positioning
Bruno Fernandes	Man United	2020	High xA and shot volume

All players listed showed strong underlying metrics before their transfers, which later translated into successful Premier League impact. 

The Future of Analytics for New Player Signings 

Analytics will continue to evolve. As data becomes more detailed and easily accessible, transfer decisions will become even more precise. However, uncertainty will always remain. Football is still played by humans, not algorithms. 

Using analytics to predict the impact of new signings has changed the Premier League forever. From Salah to De Bruyne, the evidence is clear. Data-driven signings work when done correctly. For fans, this also improves how matches are analysed and predicted.

As platforms like APWin increasingly rely on statistical trends, team form, and match data to shape football discussions, the line between professional analysis and fan insight continues to blur. In today’s game, data and analytics are no longer an optional edge. They are the foundation for understanding performance, predicting outcomes, and judging teams long before the first ball of the season is kicked.

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