Player Valuation in European Football

Nsolo, Edward; Lambrix, Patrick; Carlsson, Niklas

doi:10.1007/978-3-030-17274-9_4

Cited by 15 publications

(11 citation statements)

References 13 publications

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“…On the other hand, the statistical non-significance of the middle rank groups (4-7) reflects the complexity of the problem. There are many other factors that affect the transfer values, such as player popularity or coach preferences as well as the inflation and even the country of origin of the player [78]. Figures 3 and 6 show the impact of age on market values as well.…”

Section: Discussionmentioning

confidence: 99%

A Dimension Reduction Approach to Player Rankings in European Football

Aydemir¹,

Temizel

et al. 2021

IEEE Access

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Player performance evaluation is a challenging problem with multiple dimensions. Football (soccer) is the largest sports industry in terms of monetary value and it is paramount that teams can assess the performance of players for both financial and operational reasons. However, this is a difficult task, not only because performance differs from position to position, but also it is based on competition, time played and team play-styles. Because of this, raw player statistics are not comparable across players and must be processed to facilitate a fair performance evaluation. Furthermore, teams may have different requirements and a generic player performance evaluation does not directly serve the particular expectations of different clubs. In this study, we provide a generic framework for estimating player performance and performing player-fit-to-criteria assessment, under different objectives, for left and right backs from competitions worldwide. The results show that the players who have ranked high have increased their transfer values and they have moved to suitable teams. Global nature of the proposed methodology expands the analyzed player pool, facilitating the search for outstanding players from all available competitions.

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Section: Discussionmentioning

confidence: 99%

A Dimension Reduction Approach to Player Rankings in European Football

Aydemir¹,

Temizel

et al. 2021

IEEE Access

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“…The availability of massive data portraying soccer performance has facilitated recent advances in soccer analytics. The so-called soccer-logs [4,15,40,46], capturing all the events occurring during a match, are one of the most common data formats and have been used to analyze many aspects of soccer, both at team [8,11,25,35,50] and individual levels [6,12,33]. Among all the open problems in soccer analytics, the data-driven evaluation of a player's performance quality is the most challenging one, given the absence of a ground-truth for that performance evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, PSV is a passbased metric that thus omits all the other kinds of events observed during a soccer match and lacks of a proper validation. Instead of proposing their own algorithm for performance quality evaluation, Nsolo et al [33] extract performance metrics from soccer-logs to predict the WhoScored.com performance rating with a machine-learning approach. The resulting model is more accurate for specific roles (e.g., forwards) and competitions (e.g., English first division) when predicting if a player is in the top 10%, 25%, or 50% of the WhoScored.com ranking.…”

Section: Related Workmentioning

confidence: 99%

PlayeRank

Pappalardo

Cintia

Ferragina

et al. 2019

ACM Trans. Intell. Syst. Technol.

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The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank-i.e. searching players and player versatility-showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics. 59:2 L. Pappalardo et al. INTRODUCTIONRankings of soccer players and data-driven evaluations of their performance are becoming more central in the soccer industry [4,14,15,20,32,34,40,46]. On the one hand, many sports companies, websites, and television broadcasters-such as Opta, WhoScored.com, and Sky, as well as the plethora of online platforms for fantasy football and e-sports-widely use soccer statistics to compare the performance of professional players with the purpose of increasing fan engagement via critical analyses, insights, and scoring patterns. On the other hand, coaches and team managers are interested in analytic tools to support tactical analysis and monitor the quality of their players during individual matches or entire seasons [11,27]. Not least, soccer scouts and performance analysts are continuously looking for data-driven tools to improve the retrieval of talented players with desired characteristics, based on evaluation criteria that take into account the complexity and the multi-dimensional nature of soccer performance. While selecting talents on the entire space of soccer players is unfeasible (if not impossible!) for humans, data-driven performance scores help select a small subset of the best players who meet specific constraints (e.g., age, performance features and trends, roles). This allows scouts and performance analysts to analyze a smaller set of players, thus saving considerable time and economic resources while broadening scouting operations and career opportunities of talented players.The problem of data-driven evaluation of player performance is gaining interest in the scientific community, too, thanks to t...

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“…In this paper, we compare and contrast which attributes and skills best predict the success of individual ice hockey players in different positions. First, using the method in [14] we investigate which performance features were important for the three main position types in the National Hockey League (NHL) for four different seasons. For the data processing, feature selection and analysis we used R 3.6.3 and packages dplyr 0.8.3, ggplot2 3.0.0, gridExtra 2.3 and caret 6.0 as well as Weka 3.8.4 [6].…”

Section: Introductionmentioning

confidence: 99%

“…For the data processing, feature selection and analysis we used R 3.6.3 and packages dplyr 0.8.3, ggplot2 3.0.0, gridExtra 2.3 and caret 6.0 as well as Weka 3.8.4 [6]. Our work (including [14] for football) distinguishes itself from other work on player valuation or player performance, by working with tiers of players, i.e., the top 10%, 25% and 50% players in different positions (in contrast to individual ratings). An exact ranking of players may not always be available, and for several tasks, e.g., scouting, an exact ranking of players is not necessary.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tiers in the Ranking of Ice Hockey Players

Persson

Kozlica

Carlsson

et al. 2020

Communications in Computer and Information Science

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Many teams in the NHL utilize data analysis and employ data analysts. An important question for these analysts is to identify attributes and skills that may help predict the success of individual players. This study uses detailed player statistics from four seasons, player rankings from EA's NHL video games, and six machine learning algorithms to find predictive models that can be used to identify and predict players' ranking tier (top 10%, 25% and 50%). We also compare and contrast which attributes and skills best predict a player's success, while accounting for differences in player positions (goalkeepers, defenders and forwards). When comparing the resulting models, the Bayesian classifiers performed best and had the best sensitivity. The tree-based models had the highest specificity, but had trouble classifying the top 10% tier players. In general, the models were best at classifying forwards, highlighting that many of the official metrics are focused on the offensive measures and that it is harder to use official performance metrics alone to differentiate between top tier players.

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Player Valuation in European Football

Cited by 15 publications

References 13 publications

A Dimension Reduction Approach to Player Rankings in European Football

A Dimension Reduction Approach to Player Rankings in European Football

PlayeRank

Prediction of Tiers in the Ranking of Ice Hockey Players

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