Player Transfer Research Articles

Identifying successful football teams in the European player transfer network

This paper considers the European transfer market for professional football players as a network to study the relation between a team’s position in this network and performance in its domestic league. Our analysis is centered on eight top European leagues. The market in each season is represented as a weighted directed network capturing the transfers of players to or from the teams in these leagues, and we also consider the cumulative network over the past 28 years. We find that the overall structure of this transfer market network has properties commonly observed in real-world networks, such as a skewed degree distribution, high clustering, and small-world characteristics. To assess football teams we first construct a measure of within-league performance that is comparable across leagues. Regression analysis is used to relate league performance with both the network position and level of engagement of the team in the transfer market, under two complimentary setups. Network position variables include, e.g., betweenness centrality, closeness centrality and node clustering coefficient, whereas market engagement variables capture a team’s activity in the transfer market, e.g., total number of player transfers and total paid for players. For the season snapshots, the number of transfers correspond to weighted in- and out-degree. Our analysis first corroborates several recent findings relating aspects of market engagement with teams’ league performance. A higher number of incoming transfers indicates worse performance and better resourced teams perform better. Then, and across specifications, we find that network position variables remain salient even when engagement variables are already considered. This substantiates the notion in the existing literature that a high degree corresponds to better team performance and suggests that network aspects of trading strategy may affect a team’s success in their respective domestic league (or vice versa). In this sense, the approach and findings presented in this paper may in the future guide team’s player acquisition policies.

Statistical Modeling of Football Players’ Transfer Fees Worldwide

Professional football clubs invest vast amounts of money in the recruitment of players. This article presents the latest advances in statistical modeling of the factors that market actors take into consideration to determine the transfer prices of professional football players. It extends to a global scale the econometric approach previously developed by the authors to evaluate the transfer prices of players under contract with clubs from the five major European leagues. The statistical technique used to build the model is multiple linear regression (MLR), with fees paid by clubs as an independent variable. The sample comprises over 8000 transactions of players transferred for money from clubs worldwide during the period stretching from July 2014 to March 2024. This paper shows that a statistical model can explain up to 85% of the differences in the transfer fees paid for players. Despite the specific cases and other possible distortions mentioned in the discussion, the use of a statistical model to determine player transfer prices is thus highly relevant on a global scale.

A "Big5" európai labdarúgó bajnokságok játékos-transzfer tevékenységének összehasonlító elemzése

Transfer news and events generate as much, if not more, interest than the final matches of the most prestigious cup competitions. It is not uncommon for a player's playing rights (which clubs recognise as intangible assets) to change hands for sums in excess of €10 million or even €100 million, with the outstanding revenues of the most prestigious Big 5 leagues contributing greatly to the reality of these transactions. The aim of this study is to provide a comparative analysis of player transfer activity in the Big 5 leagues, focusing on the correlations and relationships between league revenues, transfer spending and revenues, using financial and player transfer data from 2017 and onwards. In terms of the extent of transactions, it can be concluded that player transfers are crucial for the financial management, profitability and stability of football clubs. Accordingly, the highest transfer spending occurs in the leagues with the highest revenue generating capacity. In total, between 2016 and 2021 - €7.5 billion will be diverted from the Big 5 leagues.

How do European and non-European players differ: Evidence from EuroLeague basketball with multivariate statistical analysis

This study has multiple purposes and these are (i) to divide EuroLeague players into clusters according to their statistics and positions, (ii) to examine the clusters according to the players’ characteristics and their continents of origin, and (iii) to compare statistics of European and non-European EuroLeague basketball players according to their positions. Dataset is based on the 2020–2021 EuroLeague season. Similarities and differences between players are explained by using the t-test/Mann-Whitney U test, effect sizes, Cluster Analysis (CA), and Multi-Dimensional Scaling (MDS). Six different MDS maps have been introduced to separate guards, forwards, and centers in terms of performance indicators (including both raw and per 40 min statistics). Moreover, the vast majority of MDS maps revealed for all positions are visualized with six clusters. MDS Results show that players playing in similar positions and exhibiting similar performances in the EuroLeague are on the same maps. Also, the results prove that European and non-European basketball players have different playing styles and certain clusters are dominated by either European or non-European players. The information to be obtained from this study may benefit on basketball players, coaches, and managers regarding various issues (player development plan and player transfer policy).

Consider implementing transfer fee model to benefit college sports programs

In international football — the sport we refer to as soccer here in the United States — a “transfer” refers to the reassigning of a player's registration from one football club to another. Typically, when a player transfer occurs, a negotiated compensation amount, or “transfer fee,” is paid by the interested team to the club that currently possesses the player's contractual rights. This financial transaction is seen as a necessary way to compensate the soon‐to‐be former club for the costs associated with the player's training, instruction, and development. (See bit.ly/3RPwqgD.)

Analysis of Transfer Fees in the Player Transfer System towards the Indonesian Football Industry

This paper wants to analyze the transfer fee mechanism for Indonesian football players in the system established by FIFA which is experiencing rapid progress. Like a business entity in general, a football club needs resources to carry out its activities. Player transfers are a very important aspect of the football business. The Federation Internationale the Football Association (FIFA) is the governing body of football in which it takes a global role in the relationship between sport and politics. Therefore, it is important to see how the transfer fee mechanism in the system established by FIFA affects the football industry in Indonesia. The paradigm used in this paper is the Liberalist paradigm, where individual freedom in liberalism helps society in developing ideas, and Neoliberalism, which is characterized by ideas that emphasize deregulation or market regulation, the non-opening of state-owned enterprises (SOEs), limited government interference, and a more open international market.

Analysis of Transfer Fees in the Player Transfer System towards the Indonesian Football Industry

This paper wants to analyze the transfer fee mechanism for Indonesian football players in the system established by FIFA which is experiencing rapid progress. Like a business entity in general, a football club needs resources to carry out its activities. Player transfers are a very important aspect of the football business. The Federation Internationale the Football Association (FIFA) is the governing body of football in which it takes a global role in the relationship between sport and politics. Therefore, it is important to see how the transfer fee mechanism in the system established by FIFA affects the football industry in Indonesia. The paradigm used in this paper is the Liberalist paradigm, where individual freedom in liberalism helps society in developing ideas, and Neoliberalism, which is characterized by ideas that emphasize deregulation or market regulation, the non-opening of state-owned enterprises (SOEs), limited government interference, and a more open international market.

The Regulatory Space of Baseball: Is Global Regulation Needed to Govern the International Movement of Players?

Occupying baseball’s regulatory space are multiple regimes that govern global labour mobility and the international transfer of amateur and professional players. Professional leagues are the key regulatory actors in this space, non-state actors responsible for establishing the controls on a player’s labour mobility within the league and to foreign competitions. The dominant regulator in baseball is Major League Baseball (‘MLB’) that acts as a de facto international sports federation and custodian of baseball. This article uses regulatory space theory to explore how globalisation forced the increased interaction between professional baseball leagues around the world. Despite the subsequent accelerated evolution of the rules and processes governing the international movement of professional baseballers, the global regime primarily consists of bilateral player agreements involving either MLB or Nippon Professional Baseball (‘NPB’) and other professional leagues in Latin America and Asia. This article will analyse how this system of regulation created numerous problems in the recruitment and movement of amateur and professional players, most notably an exploitative labour system in Latin America that potentially violates various international human rights laws and led to the human trafficking of Cuban baseball players. In response to these regulatory failures the authors propose a number of urgent reforms to ensure the global regulation of labour mobility in baseball facilitates the movement of players in line with international human rights through a global player transfer system that includes independent dispute resolution and that is operated by an independent global regulator.

The Impact of Soccer Player Transfer on Stock Prices: An Analysis on the Stoxx European Football Index

Bu çalışmada STOXX Avrupa Futbol Endeksinde işlem gören 14 futbol kulübünün transfer (satın alma) duyurularına yatırımcıların (futbol kulüplerinin hisse senedi için) tepkisi analiz edilmiştir. Bu doğrultuda toplam 14 futbol kulübünün 2010-2022 yılları arasında 5 milyon Avroya eşit veya üzerinde olan toplam 394 futbolcu transfer duyurusu analize dâhil edilmiştir. Çalışmada hisse senetlerine yatırımcıların tepkisini ölçmek için olay çalışması (event study) yöntemi kullanılmıştır. Bu doğrultuda toplam 11 farklı olay penceresinde birikimli ortalama olağanüstü getiriler (CAAR) hesaplanmıştır. Literatürdeki çalışmalardan farklı olarak, CAAR değerleri parametrik ve parametrik olmayan test istatistiklerine göre değerlendirilmiştir. CAAR değerleri bazı takımlar için transfer sayılarına ve büyüklüklerine göre üç farklı grupta, bazı takımlar için bir ve iki farklı grupta hesaplanmıştır. Analiz sonuçları her futbol kulübünün transfer duyurusuna yatırımcıların karışık (negatif veya pozitif) tepkiler verdiğini doğrulamaktadır. Analiz sonuçları genel olarak değerlendirildiğinde, tüm takımlar için bonservis bedeli yüksek (düşük) olan futbolcuların transfer duyurularına yatırımcıların daha yüksek (düşük) tepki (negatif veya pozitif) verdiğini göstermektedir.

European Football Player Transfer Scheme according to IFRS and IAS: Case Study on Juventus Football Club S.p.A

Recognition of soccer players as intangible assets in financial statements is still happening today, this case will affect the accounting treatment that will be received by player transfer activities from the old club to the new club. The purpose of this study is to analyze the transfer scheme of football players in Europe from the perspective of IFRS and IAS accounting regulations. This study uses secondary data in the form of player transfer data for the Italian football club Juventus Football Club S.p.A in the 2018/2019 to 2020/2021 season which is included in the club's annual financial statements. In addition, it also uses IFRS and IAS as a study in providing solutions to transfer activities experienced by European clubs. The results of this study indicate that European football players can be categorized as intangible assets and receive proper treatment as intangible assets. Although football activities are different from the industry in general, the transfer scheme carried out by the club can be explained as recognized under IFRS and IAS regulations and used by the club in its financial statements legally.

The Legal Certainty on Freedom of Foreign Football Player Transfer in Indonesia: Learning of Bosman Ruling

The Bosman ruling means that players could move or transfer to a new club at the end of their contract without paying their former clubs any fee. A player can now agree with a pre-contract with another club for a free transfer if the players' contract with their existing club has six months or less remaining. This means that the Bosman Rulingis a pioneer in the freedom of contract aspect in sports law. According to Law Number 3 of 2005 on National Sports System in Indonesia, the problem in Indonesia is that there is no regulation regarding the freedom of contract for the sporting member in Indonesia. Some cases violate the Bosman Ruling against foreign football players Damian Linzio. Damian Linzio cannot solve his problems because there is no regulation related to Bosman Ruling issues in Indonesia. This Research aims to adapted Bosman Ruling into Indonesian Sports Law based on Law Number 3 of 2005 concerning the National Sports System. Bosman Ruling is very important in sports law because it regulates the freedom of contract. The type of this research is normative legal research. Normative legal research is a process to find the rule of law, principles of law, and the legal doctrines to address the legal issues faced equal treatment of foreign players in freedom to transfer, the need to introduce bosman ruling into indonesian sports law, study case of damian linzio and bring the case to the court of arbitration for sport. This research found that Indonesia needs to apply Bosman Ruling as the pioneer of freedom of contract to Indonesian Sports Law. Bosman Ruling can be adapted in Law Number 3 of 2005 on the National Sports System in Indonesia. For Damian Linzio, author recommend to bringing the case to Court of Arbitration for Sport because they are more competent to resolve sports cases.

Football players and asset manipulation: the management of football transfers in Italian Serie A

ABSTRACT Research question: We consider player trading as one of the distinctive features of football clubs’ business models and how these models have been influenced since the implementation of Financial Fair Play (FFP). Considering the Italian financial and economic context in the football sector, we investigate whether Serie A football clubs adopt earnings manipulation as a result of trading players’ economic rights. Research methods: The empirical analysis includes the football clubs competing in the Italian Serie A from 2005 to 2018. Using an unbalanced panel dataset composed of 275 club-year observations (38 different clubs), our estimations are run by using fixed effects OLS models. Clubs’ net capital income from sales of football players is used as a proxy of earning manipulation. We control for clubs’ football performance, size and reputation. Results: and Findings: By applying Bartov’s model based on asset sales, our results confirm the adoption of asset manipulation behaviours in the football industry, wherein players’ economic rights sales are a preponderant financial item affecting economic performance. Specifically, net income from trading players is significant and positively linked with Serie A clubs’ profitability, while leverage is significant after the introduction of FFP and especially for the most prominent Serie A clubs. Implications: Our results confirm that Italian Serie A has been chronically dependent on player trading to maintain their financial sustainability. UEFA’s regulations could be assessed and modified to enable them to prevent and reveal the real activities and dynamics behind the player transfer market (profit smoothing behaviours).

When Pep comes calling, the oil market answers: The effect of football player transfer movements on abnormal fluctuations in oil price futures

We examine the effect of player-transfers entered into by football clubs owned, or financed, by individuals who are key players in the oil market on abnormal returns in oil futures. In oil-financed football clubs, the sums expended buying players frequently far exceeds the amount received from selling players in the player-transfer market. We find that in order to finance these deficits in the player-transfer market, the owners act opportunistically by withholding the oil supply, resulting in higher abnormal oil spot returns. We also find that these spot price adjustments are reflected in abnormal returns in the futures market. The exception is when the deficit in the player-transfer market is above a very high threshold, which is typically only the case when the highest profile players in football are transferred. The high-profile transfers attract widespread media attention, making oil futures investors aware of the potential transmission from the player-transfer market to the oil market on a wide-scale, which dissipates the effect of a deficit in the player transfer market on abnormal returns in oil futures.

When Pep Comes Calling, the Oil Market Answers: The Effect of Football Player Transfer Movements on Abnormal Fluctuations in Oil Price Futures

We examine the effect of player-transfers entered into by football clubs owned, or financed, by individuals who are key players in the oil market on abnormal returns in oil futures. In oil-financed football clubs the sums expended buying players frequently far exceeds the amount received from selling players in the player-transfer market. We find that in order to finance these deficits in the player-transfer market, the owners exercise their market power to adjust the oil spot market, resulting in higher abnormal oil spot returns. We also find that these spot price adjustments are reflected in abnormal returns in the futures market. The exception is when the deficit in the player-transfer market is above a very high threshold, which is typically only the case when the highest profile players in football are transferred. The high-profile transfers attract widespread media attention, making oil futures investors aware of the potential transmission from the player-transfer market to the oil market on a wide-scale, which dissipates the effect of a deficit in the player transfer market on abnormal returns in oil futures.

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

With the wide popularity of various sport items and the continuous prosperity of sport business, player transfer events are becoming more and more frequent, which gives birth to a big player transfer market with attractive attentions of spectators. However, for the purposes of business competition or concerns of privacy disclosure, the multiple parties involved in an identical player transfer event are often reluctant to release the concrete transfer fees of a player as the transfer fee is always sensitive enough for the player himself or herself as well as the involved multiple sport clubs. The hidden player transfer fees block the fair and transparent sport business trades and cheat prospective spectators to some extent, which decreases the spectator trust towards player market value and finally, lowers the spectators’ satisfaction towards sport business. In this situation, it is becoming a necessity to develop a trusted player transfer evaluation method supported by all the parties involved in player transfer events. Inspired by this challenging task, a trusted player transfer evaluation method with privacy protection is put forward in this paper, which is mainly based on Locality-Sensitive Hashing (LSH) and Blockchain technologies and named TPTELSH+B. At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTELSH+B algorithm. Experimental results report that TPTELSH+B has a good evaluation performance with competitive approaches.

The transfer system in European football: A pro-competitive no-poaching agreement?

We assess the proclaimed pro-competitive effects of the “transfer system”, the no-poaching agreement governing the European football (soccer) labor market. A major argument to legitimize this system is that transfer fees, which hiring clubs pay to release players from their current clubs, redistribute revenues from large market to small market clubs. This would strengthen small clubs’ financial clout and their ability to compete in sporting terms. Player transfer fees represent over 10 billion Euros in asset value in the financial statements of the 202 clubs we analyze. Still, small market clubs rarely obtain substantial revenues from the transfer market. The main beneficiaries are clubs around the middle of the market size distribution. A select group of large market clubs makes significant transfer losses, but this does not undo their initial financial advantage. Overall, the transfer system therefore leads to a very minor reduction in revenue inequality.

Who Benefits from Transfers?

We assess the proclaimed pro-competitive effects of the “transfer system”, the no-poaching agreement governing the European football (soccer) labor market. A major argument to legitimize this system is that transfer fees, which hiring clubs pay to release players from their current clubs, redistribute revenues from large market to small market clubs. This would strengthen small clubs’ financial clout and their ability to compete in sporting terms. Player transfer fees represent over 10 billion Euros in asset value in the financial statements of the 202 clubs we analyze. Still, small market clubs rarely obtain substantial revenues from the transfer market. The main beneficiaries are clubs around the middle of the market size distribution. A select group of large market clubs makes significant transfer losses, but this does not undo their initial financial advantage. Overall, the transfer system therefore leads to a very minor reduction in revenue inequality.

The formation mechanism of the player transfer network among football clubs

ABSTRACT Clubs compete for talented players to succeed in the global football industry. Drawing on a network analytical framework, this research explores how endogenous and exogenous factors influence the partner selection process in the player transfer market. The boundary of the transfer network consists of 98 football clubs from the top five European leagues. Using exponential random graph models (ERGMs), the current study confirms that clubs have a significantly high propensity of exchanging players. Player transactions often occur through closed triads, such that clubs tended to make a deal with those who shared the same third partners. Additionally, league type and match performance produce stable and significant effects on the formation of transfer ties. Practical implications are also discussed.

Data-driven team ranking and match performance analysis in Chinese Football Super League

Abstract Recent years have seen an increasing body of research into the evaluation of the team-level technical-tactical performance in association football using match events data. However, most studies used mono-dimensional approach and modeled the influence of each performance aspects on match result in isolation, which limited the interpretability of the results. The study was aimed to apply a state-of-the-art algorithm to the ranking of team performance and exploitation of key performance features in relation to match outcome based on massive match dataset. Data of all 1200 matches from 2014 to 2018 Chinese Football Super League (CSL) were used. From the original 164 match events, we extracted 22 features that were related to attacking, passing, and defending performance and most. A Linear Support Vector Classifier (LSVC) model was subsequently built with these 22 input features and trained in order to rank the teams by their performance and analyze the features that influence most match outcome (win/not win), with the dataset being divided into a ratio of 4:1 to train and validate the model. The results have shown that the data-driven LSVC model displayed a prediction accuracy of 0.83 and the ranking of teams’ match performance and prediction of teams’ league standings were highly correlated with their actual ranking. Saves, pass success and shot on target in penalty area were demonstrated as top positive features for winning whereas shots on target during open play, pass and bad shot% were three negative features most influential for the match result. The team ranks of all teams were highly correlated with their real final league rankings. In general, CSL winning teams build their success based on defensive ability and shooting accuracy, and high-ranked teams could always maintain better performance than their counterparts. The team-rank framework could provide a consolidated and complex approach to evaluate the match performance quality of the teams, refining decisions-making during match preparation and player transfer at different periods of the season.