top of page
  • Writer's pictureBruin Sports Analytics

How can Premier League Transfer Spending Predict Domestic Success?

By Donovan Rimer


Source: thesun.co.uk

Introduction


Last summer, premier league clubs spent a combined 2.9 billion euros, shattering the record for transfer spending during just one window. This figure, significantly bolstered by contributions from the burgeoning Saudi Pro league, highlights the EPL's status as the biggest spender in global football. The core of the investigation revolves around the impact of these lavish expenditures. Specifically, it examines whether the hefty transfer spending by EPL clubs correlates with improved performances in key areas: goal difference, table position, and point total over the past five seasons.


The research aims to establish a link between financial outlay in player acquisitions and tangible on-field success. This involves a comprehensive analysis of the net transfer spending (balance) of each Premier League club over the last five transfer windows. By scrutinizing these financial figures in relation to the clubs' subsequent performances, the study seeks to answer a critical question: Can the analysis of incoming Premier League transfers accurately predict domestic success?


Analysis


To start, let us observe the monetary balance from each premier league club over the past five windows.


Figure 1

The observations from the analysis of Premier League transfer spending raise intriguing points about the financial strategies of the top clubs, particularly the "Big 6", which are composed of the 6 most dominant teams during the last twenty years: Manchester City, Manchester United, Chelsea, Arsenal, Tottenham and Liverpool. It's notable that these clubs dominate the list of biggest spenders, with Liverpool being an exception. This pattern underscores the financial muscle of these clubs and their significant investment in player acquisitions.


One key insight from the data is Manchester City's financial management. Despite being one of the big spenders, Manchester City has managed to accrue less debt compared to its peers. This is largely attributed to their effective strategy in player sales, which has generated substantial revenue, offsetting their expenditure on new acquisitions. This indicates a savvy approach to balancing player investment and revenue generation, a critical aspect of modern football economics.


Chelsea's financial approach stands out, particularly for its massive spending, which has not always correlated with success on the field. This brings into question the efficiency of their spending compared to other clubs. The scrutiny they face highlights the complex relationship between financial investment and sporting success, suggesting that merely spending more does not guarantee better results.


Season Point Total


The study also explores the broader question: Does higher spending on players correlate with more points in the league? To examine this, the analysis compares the net spending of clubs with their average point totals over the past five seasons. This comparison includes data from teams that have been relegated, offering a comprehensive view of spending across different tiers of league performance.


Figure 2

The Pearson R-value, or correlation coefficient, in this context is 0.6434. It measures the strength and direction of the linear relationship between two variables, in this case, net transfer spending and average points accumulated. The value ranges from -1 to 1; a value of 1 implies a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 suggests no correlation at all. A coefficient of 0.6434 suggests a moderately strong positive relationship, indicating that as net spending increases, there is a tendency for the points total to also increase. The formula for r is as follows:




Where:

n → number of pairs of scores

Σxy →  sum of the product of paired scores

Σx, Σy →  sums of the x scores and y scores respectively

Σx2, Σy2 → sums of the squares of the x and y scores


The P-value, which in this analysis is 0.000294, indicates the probability of obtaining the observed results, or more extreme, if the null hypothesis (that there is no relationship between spending and success) were true. A P-value less than 0.05 is typically considered statistically significant. In this case, the P-value of 0.000294 is much lower than 0.05, suggesting that the observed correlation is highly unlikely to have occurred by chance. This low P-value supports the hypothesis that there is a statistically significant correlation between transfer spending and league success.


While the data shows a trend suggesting that increased spending correlates with better performance, it's crucial to remember that this is an average over the past five seasons. This broad approach might miss specific variations in individual clubs' strategies, such as Newcastle United's increased spending post-ownership change and Brighton & Hove Albion's notable financial gains from player sales. These variations highlight that the correlation, while statistically significant, is not the sole determinant of success and can be influenced by various club-specific factors and strategies.


League Position


In order to account for the most recent transfer window and current season, another graph has been constructed using league position, rather than total points. It also only considers the most recent 3 seasons, accounting for the 2021/22 season until 13 game weeks into the 2023/24 season. It aims to estimate the correlation between league finish and transfer spending in recent windows.


Figure 3

The study reveals that for every 100 million euros spent, there's an average climb of approximately 1.22 positions in the league standings. The regression analysis yields an R-value of 0.48, which indicates a weak correlation between the transfer spending and league position improvements. However, the P-value of 0.032202 suggests that the results are statistically significant, as it falls below the 0.05 threshold. This indicates that there is a correlation between a team's transfer balance and its domestic performance, albeit not as strong as the correlation with total points.


This segment of the analysis also sheds light on some distinct trends and exceptions. Notably, Brighton and Hove Albion's recent performance stands out. Despite being net positive in terms of transfer spending, they have consistently managed a top-half finish in the league. On the other hand, Chelsea, despite being the highest spender, hasn't seen a commensurate return in terms of points. Furthermore, the traditional dominance of the 'big 6' teams in the Premier League is being increasingly challenged by clubs like Newcastle United, Brighton, and Aston Villa, reflecting the dynamic and evolving nature of the league.


Consecutive Season Performance


The next figures involve analyzing the impact of transfers on a team's performance by looking at changes in both goal difference and point totals after a year of transfer activities. These two metrics were selected because they encapsulate a team's real performance in a domestic season. The aim is to establish a coefficient that predicts the expected increase in points for a team after spending 100 million Euros.

To illustrate this, I've analyzed changes in point totals from the 2018/19 to the 2019/20 season for each team in relation to their net transfer balance. For instance, let's take West Ham United as an example. If they ended the 2018/19 season with 52 points and the 2019/20 season with 39 points, their point difference would be -13. Coupled with a net transfer balance of -€64.34 million, this scenario indicates that despite significant investment, West Ham slipped from a mid-table position to fighting relegation.

The analysis reveals counterintuitive results regarding the impact of transfer spending on team performance in the subsequent season. According to the linear regression model, for every 100 million euros spent by a club, there is a predicted decrease of 2.18 points in the next season's point total. This is accompanied by an R-value of -0.146 and a P-value of 0.265666, indicating that this result is not statistically significant as the P-value is higher than the 0.05 threshold.


Similarly, for the same amount of spending, the model predicts a decrease in goal difference by 3.17. This finding comes with an R-value of -0.1357 and a P-value of 0.30375, again showing a lack of statistical significance as the P-value exceeds 0.05.


These outcomes suggest that there is no significant correlation between spending in one or two transfer windows and an immediate increase in points or improvement in goal difference in the following season. Interestingly, the model predicts a decrease in team performance following significant spending. This reflects the reality of soccer where building a winning team often requires multiple years. High-profile signings need time to integrate into a new team, adapt to different systems, and develop chemistry with new teammates. Consequently, evaluating the impact of transfer spending in isolation for each window provides limited insights into the long-term success of a team. This analysis underscores the complexity and the time-sensitive nature of team building in professional soccer.


Age of Incoming Transfers


If not the transfer fee, perhaps the position, age, or team the incoming player came from can be 

used to predict the success of transfers. Does a younger average age of players brought in correlate to more successful seasons down the road? Perhaps clubs which fail to purchase younger players will lack the pace and energy needed to win tough games. Plotted below are the average age of each Premier League club’s transfers in the past 6 seasons:


Figure 6

The analysis reveals a notable trend linking the average age of players in a team to their performance in terms of points gained during the season. Specifically, the data suggests that for each year decrease in the average age of the squad, there is an average gain of 12.43 points throughout the season. This relationship is underscored by an R-value of -0.6072, indicating a strong negative correlation between average age and points gained. Moreover, the significance of this finding is reinforced by a P-value of 0.001293, well below the 0.05 threshold, thus marking it as statistically significant.


However, it's important to approach these results with caution regarding the causation aspect. The trend observed might not necessarily imply that younger teams are inherently more successful. Instead, it could reflect a strategy among top-performing teams to recruit younger players as replacements for their prime-aged stars, while lower-ranking teams might rely more on experienced, older players.


This leads to the consideration that a blend of ages within a team might be a key to success. Having seasoned professionals in the squad could provide mentorship and guidance to younger, less experienced players, fostering a balanced and dynamic team environment. To further understand whether younger players directly contribute to better transfer outcomes, an in-depth examination of the age variance within teams and its impact on performance would be beneficial. Such an analysis could offer more insights into the optimal age mix for a successful team composition in the Premier League.


Positions


For a soccer formation, there are a variety of ways to classify positions. In the simplest way possible, there is the goalkeeper, defenders, midfielders, and forwards. Defenders can be further subdivided into “full backs” which defend the left and right, and “center backs” who usually play in pairs and defend the goal centrally. The most common formations include 3, 4, or 5 defenders, each of which having a slightly different role for the full backs to play. 


For midfielders, there are a wide variety of classifications to play as this is the most flexible position on the pitch. Defensive midfielders usually play closer to the backline, right and left midfielders on the sides, and attacking midfielders closer to the opposing goal. There are a plethora of ways for midfielders to play, so this survey does not further subdivide them.


Typically teams play with 1 center forward, and 2 “wingers” who play on the left and right respectively. Oftentimes the term right midfield is synonymous with right winger. Additionally, players can play many different positions, so this refers to the position they were classified.



In the Premier League transfer market, strikers, often referred to as center-forwards, are noticeably over-represented. This trend likely stems from managers prioritizing the acquisition of players who can reliably score goals. On the other hand, recruitment for other positions appears to be more proportionate to their distribution in a typical team lineup.


An intriguing financial aspect is the comparative cost of players based on their positions. Goalkeepers, interestingly, tend to be valued at about half the price of players in other positions. Wingers, in contrast, are among the most expensive, with the average transfer fee for a winger exceeding 7 million euros.


While these insights are fascinating and reveal trends about the demand for different positions in the league, their direct correlation with domestic success might not be as significant as the overall amount of money spent. This suggests that while the choice of player positions is an important aspect of team strategy, the total financial investment in the squad could be a more crucial determinant of a team's success. However, a more detailed investigation into how the recruitment of specific positions correlates with team performance would be necessary to draw firmer conclusions on this aspect.


Conclusion


This investigation explored a connection between the financial expenditure of clubs on new players and their subsequent success in the Premier League. The analysis, particularly evident in figures 1 and 2, utilized linear regression models to quantify this relationship. It was observed that for every 100 million euros spent, there was an average increase of 5.41 points in the league standings. Additionally, a significant but weaker correlation was noted between spending and improvement in league positions.


Another interesting finding is the correlation between the average age of incoming transfers and the average point total over the past five seasons. The data indicated a trend where younger average ages in transfers are associated with higher point totals, suggesting that investing in youth can be a beneficial strategy.


Regarding player positions, the study found that recruitment across the league is relatively uniform, with strikers being slightly over-represented due to their goal-scoring capabilities. However, the analysis suggested that positional recruitment might not be as strong a predictor of domestic success as other factors like financial investment and average age of players.


It's crucial to acknowledge that success in football is multifaceted and influenced by a myriad of factors beyond just financial expenditure and age demographics. Elements such as managerial prowess, the individual quality of players, tactical approaches, team chemistry, and an element of luck all intertwine to shape a team's performance. Nevertheless, the findings from this study highlight that the average age of transfers and the amount spent on player acquisitions have shown a notable correlation with domestic success in recent years. These factors, therefore, could potentially serve as indicators in predicting team performance throughout the season.

In summary, while financial power and youthfulness in transfers emerge as significant variables, they are part of a broader and more complex equation that determines success in the highly competitive landscape of the Premier League.


Sources


“Football Web Pages.” Barclays Premier League | League Table | 2012-2013 | Football Web Pages, www.footballwebpages.co.uk/premier-league/league-table/2012-2013


“Premier League - Transfer Balance and Five-Year Comparison.” Transfermarkt, www.transfermarkt.us/premier-league/fuenfjahresvergleich/wettbewerb/GB1.


GitHub/ewenme. “Premier League Transfer Data.” GitHub, https://github.com/ewenme/transfers/blob/master/data/premier-league.csv.


“Pearson Correlation Coefficient Calculator.” Social Science Statistics, www.socscistatistics.com/tests/pearson/default2.aspx





218 views

Recent Posts

See All

Comments


bottom of page