Influence of crowdsourcing, popularity and previous year statistics in market value estimation of football players

Singh, Prabhnoor; Lamba, Puneet Singh

doi:10.1080/09720529.2019.1576333

Cited by 28 publications

(51 citation statements)

References 9 publications

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“…Strong evidence was found in employing the Probability only to make the final decision. This result is coherent with [10,11], as the crowdsourcing, from the introduction of Transfermarkt, has become the main source for evaluation of football players. Conversely, the opinions concerning Plausibility, Credibility, and Possibility are essential in conditions of infoincompleteness; in this context, the optimal decision can be achieved by adopting an arbitrary set of opinions, except for the couple Credibility-Possibility, which was found to be more promising than the Possibility considered exclusively.…”

Section: Discussionsupporting

confidence: 85%

“…Furthermore, the sentiment can affect the athlete evaluation: an interesting study was conducted by Singh and Lamba [10]; they described how crowdsourcing, previous year statistics, and popularity of players can affect the evaluation. Regarding crowdsourcing, in [11], the authors proved how, in the context of German soccer, a community became the main source for reporting market values to predict the actual transfer fees.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions

Iovane

Landi²,

Rapuano

et al. 2021

Applied Sciences

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Researchers are interested in defining decision support systems that can act in contexts characterized by uncertainty and info-incompleteness. The present study proposes a learning model for assessing the relevance of probability, plausibility, credibility, and possibility opinions in the conditions above. The solution consists of an Artificial Neural Network acquiring input features related to the considered set of opinions and other relevant attributes. The model provides the weights for minimizing the error between the expected outcome and the ground truth concerning a given phenomenon of interest. A custom loss function was defined to minimize the Mean Best Price Error (MBPE), while the evaluation of football players’ was chosen as a case study for testing the model. A custom dataset was constructed by scraping the Transfermarkt, Football Manager, and FIFA21 information sources and by computing a sentiment score through BERT, obtaining a total of 398 occurrences, of which 85% were employed for training the proposed model. The results show that the probability opinion represents the best choice in conditions of info-completeness, predicting the best price with 0.86 MBPE (0.61% of normalized error), while an arbitrary set composed of plausibility, credibility, and possibility opinions was considered for deciding successfully in info-incompleteness, achieving a confidence score of 2.47±0.188 MBPE (1.89±0.15% of normalized error). The proposed solution provided high performance in predicting the transfer cost of a football player in conditions of both info-completeness and info-incompleteness, revealing the significance of extending the feature space to opinions concerning the quantity to predict. Furthermore, the assumptions of the theoretical background were confirmed, as well as the observations found in the state of the art regarding football player evaluation.

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Section: Discussionsupporting

confidence: 85%

Section: Related Workmentioning

confidence: 99%

Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions

Iovane

Landi²,

Rapuano

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…When dealing with the topic of transfer fees, both the media and academics have most often focused on the concept of players' market value (Coates and Parshakov 2021;He et al 2015;Herm et al 2014;Kirschstein and Liebscher 2019;Majewski 2016;Müller et al 2017;Prockl and Frick 2018;Romann et al 2021;Serna Rodríguez et al 2018;Singh and Lamba 2019;Velema 2018). However, we consider that the concept of a players' transfer value is more appropriate, as it clearly refers to transfer fees as described above, while the concept of market value is more ambiguous.…”

Section: Literaturementioning

confidence: 99%

Econometric Approach to Assessing the Transfer Fees and Values of Professional Football Players

2021

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Billions of euros are invested every year by professional football clubs for the recruitment of players. How do market actors decide prices? This paper presents an econometric model unveiling the key factors coming into play in determining fees on the transfer market for professional football (soccer) players. 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 2000 transactions of players transferred for money from clubs in the five major European leagues during the period stretching from July 2012 to November 2021. This paper notably highlights the importance of taking into consideration the remaining duration of contracts binding players with the club to which they belong, a factor often neglected in the existing literature. It also shows that a statistical model can explain over 80% of the differences in the transfer fees paid for players. This paper reveals various applications of the approach developed for the football industry to both assess and predict football players’ transfer fees and values: transfer negotiations, club sales or purchases, bank credit, fund raising, financial planning and communication, legal disputes, etc.

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“…The third criterion, "CS%" (clean sheets), calculates the percentage of matches in which the goalkeeper does not concede goals (Apostolou and Tjortjis, 2019;Schultze and Wellbrock, 2018;Singh and Lamba, 2019). CS is such a relevant criterion that a trophy is traditionally awarded for it in several tournaments, including the English Premier League and the FIFA World Cup.…”

Section: Variablesmentioning

confidence: 99%

Performance analysis of professional soccer goalkeepers by Composition of Probabilistic Preferences

Gavião¹,

Gavião

Sant’Anna

et al. 2021

Rev. Bras. Ciênc. Esporte

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This research aims to assist managers and technical commissions to choose professional soccer goalkeepers. A sample of 64 goalkeepers playing in Argentina and Brazil was studied. Their performance in the matches of two seasons were analyzed considering three criteria: goals against per minute played, percentage of goals and percentage of matches without conceded goals. The Composition of Probabilistic Preferences (CPP) was the method chosen for modeling, considering the random variability in the problem data and in football, considered one of the most unpredictable sports. CPP allowed to compare the choice based on the data analysis to the latest goalkeeper call-ups for these countries’ national teams. The selected goalkeepers corresponded to those presenting the best individual performance, which confirms the model.

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Influence of crowdsourcing, popularity and previous year statistics in market value estimation of football players

Cited by 28 publications

References 9 publications

Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions

Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions

Econometric Approach to Assessing the Transfer Fees and Values of Professional Football Players

Performance analysis of professional soccer goalkeepers by Composition of Probabilistic Preferences

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