Premier League Season Research Articles

Own goals in football: do they occur randomly or exhibit systematic patterns?

ABSTRACT Due to the low-scoring nature of soccer every single goal can drastically change the outcome of a match. Therefore, most large-scale performance studies aim at defining systematic processes and factors involved in goal scoring, while the scoring of the seemingly unintentional goals (like own goals) appears to be overlooked so far. In this study, factors for scoring own goals are investigated and compared to the factors connected to other (normal) goals. Therefore, a large dataset consisting of all goals from seven full seasons of the English Premier League (seasons 12/13 to 18/19) was analysed. Factors investigated include a priori factors like matchday, playing minute and team strength, but also specific situational variables like the zone from where the goal was scored, the body parts used, scoreline, defensive order and the preceding action. Favourites (p < .001) and home teams (p <0.05) profit more often from own goals than outsiders and away teams, suggesting systematic influences on own goal scoring. However, own goals significantly differ from all goals in the zone of goal scoring where more than 50% of the own goals are scored from the zone closest to goal (Zone 1). Additional aspects and potential reasons for the observed patterns are discussed.

Analysis of the market value of Premier League attacker

The main purpose of this study is to use the method of multiple linear regression to conduct a comprehensive discussion on "Factors affecting the market price of Premier League striker players". In the era of increasingly hot soccer, the transfer of stars is a big attraction in the transfer period every year, but there are still many clubs signing overpaid and underpaid players. The overall objective of this study is to find the determinants of players' price, so as to provide a reference for clubs to improve the utilization of funds in the transfer period. In this study, a dataset of player data for the 17-18 Premier League season was first downloaded via Kaggle. Then, the dataset obtained from Kaggle was used for empirical analysis to identify correlations that significantly affect the market price of players, and multiple linear regression analysis was performed after processing these data. Through the calculations, it was determined that match performance and goals scored had a significant positive impact on market value, and age and match possession had a non-significant negative impact on market value, which suggests that there is a need for the relevant team managers to optimize these aspects in order to promote a virtuous cycle of club development and team performance.

Analysis of the market value of Premier League attacker

The main purpose of this study is to use the method of multiple linear regression to conduct a comprehensive discussion on "Factors affecting the market price of Premier League striker players". In the era of increasingly hot soccer, the transfer of stars is a big attraction in the transfer period every year, but there are still many clubs signing overpaid and underpaid players. The overall objective of this study is to find the determinants of players' price, so as to provide a reference for clubs to improve the utilization of funds in the transfer period. In this study, a dataset of player data for the 17-18 Premier League season was first downloaded via Kaggle. Then, the dataset obtained from Kaggle was used for empirical analysis to identify correlations that significantly affect the market price of players, and multiple linear regression analysis was performed after processing these data. Through the calculations, it was determined that match performance and goals scored had a significant positive impact on market value, and age and match possession had a non-significant negative impact on market value, which suggests that there is a need for the relevant team managers to optimize these aspects in order to promote a virtuous cycle of club development and team performance.

Rebounds in football: A systematic investigation of characteristics of goals scored after rebounded balls in English Premier League seasons 2012/2013 to 2018/2019

The literature on systematic aspects of goal scoring in football is rich, while little is known about random influences. This study focuses on an unsystematic element of goal scoring, namely rebounds, where a shot that bounces back from another player or the goal creates contested opportunities. Aspects including causes, match situation, player positioning and pitch zones were investigated for 554 rebound goals from seven seasons (2012/2013–2018/2019) of the English Premier League. Rebounds were predominantly caused by short distance and defensive errors, with goalkeepers playing a crucial role. Interestingly, the superiority of home teams and stronger teams to score significantly more goals than away teams and weaker teams can also be observed for rebound goals. Rebound goals are particularly prevalent in zones close to the goal, more prevalent after corners than non-rebound goals and associated with a high number of players in the penalty box. Overall, high similarities exist between rebound and non-rebound goals, while differences in match situations and spatial patterns suggest unique characteristics of rebound goals. These results are in line with the notion of systematicity and randomness concurrently contributing to success in football. More research appears useful to further disentangle the influence of both factors.

Expected Pass Turnovers (xPT) - a model to analyse turnovers from passing events in football

ABSTRACT The aim of this study was to create a novel metric, Expected Pass Turnovers (xPT), that could evaluate possession retention from player-passing events in football. Event and positional data were analysed from all 380 matches in the 2020/21 English Premier League season, which encompassed 256,433 passes in the final dataset. A logistic mixed-effects model was implemented to attribute the probability of each pass getting turned over. The use of positional data enabled the identification of a) opposition players present in radii surrounding the ball carrier and b) availability of teammates with respect to the ball carrier. The addition of these positional features improved the accuracy (+6.1 AUC Score) of the model. xPT serves as a practitioner Key Performance Indicator, as analysts can identify players that lose possession more often or not than expected, given the situational context of each pass, from game to game. Future work may include modelling the turnover probability of dribble and carry actions, as this would lead to a more comprehensive understanding of turnover events in football.

Exploring Premier League Clubs Performance and Home-Away Differences Based on Passing Network Analysis

Abstract Whereas many studies have investigated the home advantage in football, only few studies focused on different passing patterns of home and away teams. Therefore, the aim of this study was to use two holistic indicators of social network analysis to explore potential differences: transitivity and density. As these metrics are not born in sport science, a further contribution of this study was to evaluate if these can serve as performance indicators. Based on a sample of the complete 2017/18 Premier League season, this study shows that higher ranked teams show significantly higher values for density (Z = 12.00; p &lt; .001; r = 0.795) and transitivity (Z = 7.08; p &lt; .001; r = 0.469) with large effect sizes. The differences of the teams’ performances for home and away games were not pronounced, and only with a small effect size (density: Z = 5.20; p&lt; .001; r=0.267; transitivity: Z = 1.73; p = 0.084; r=0.089). Overall, results contribute to the current knowledge base in two ways: First, we could show that density and transitivity are correlated with performance, which makes sense as they can be interpreted as a team’s coopration variability. Second, we could show that the degree of successful collaboration is not significantly higher for matches played at home.

Predicting Team Success in the Indian Premier League Cricket 2024 Season Using Random Forest Analysis

Background. Random Forest is a popular machine learning algorithm used for classification and regression tasks. The study purpose is to investigate the use of Random Forest machine learning to predict the winning chances of teams in the 2024 Indian Premier League (IPL) season. Objectives. By analyzing comprehensive player statistics, including matches played, batting and bowling averages, as well as fielding contributions, the study aims to understand the factors that influence team success in T20 cricket and provide actionable insights for team management, betting markets, and cricket enthusiasts. Material and methods. The study involved 10 cricket teams competing in the IPL 2024 season. Player statistics and match outcomes data from previous IPL seasons were collected and cleaned, with missing values addressed through imputation. The features were based on player statistics, including both aggregate measures and calculated metrics. A Random Forest is chosen as the machine learning model, trained using preprocessed data, with features derived from player statistics as input and match outcomes as the target variable. The dataset was split into training and validation sets, using methodologies such as cross-validation to ensure a robust model performance. Results. The Random Forest model demonstrates strong predictive performance, with a low Mean Squared Error (MSE) of 8.2174, Root Mean Squared Error (RMSE) of 2.8666, and a high R-Squared value of 0.9173, indicating approximately 91.73% variance explained in the data. Chennai Super Kings emerge as frontrunners with a predicted performance percentage of 83.4%, while teams like Punjab Kings and Lucknow Super Giants show lower predicted performance percentages, suggesting potential areas for its improvement. Conclusions. This study reveals the effectiveness of Random Forest machine learning in forecasting IPL match outcomes based on player statistics. It provides valuable insights into team dynamics and offers actionable recommendations for team management and cricket enthusiasts. The findings enrich our understanding of IPL match dynamics, contribute to the evolution of cricket analytics, and promote greater engagement with sport, ultimately enhancing the fan experience in the IPL.

A new similarity function for Pythagorean fuzzy sets with application in football analysis

&lt;abstract&gt;&lt;p&gt;The idea of Pythagorean fuzzy sets (PFSs) has been extensively applied in various decision-making scenarios. Many of the applications of PFSs were carried out based on similarity functions. Some methods of similarity functions for PFSs (SFPFSs) cannot be trusted for a reliable interpretations in practical cases due to some of their setbacks. In this work, a new method of SFPFSs is developed with the capacity to outsmart the efficiency of the extant SFPFSs in terms of precise results and appropriately satisfying the rules of SFs. The new method is described with some results to validate the properties of SFs. In terms of practical application, we use the newly developed method of SFPFSs to discuss the relationship between the players of the Liverpool Football Club (FC) in the 2022/2023 English Premier League (EPL) season to assess their performances in their resurgent moments within the season. Using data from BBC Sport analysis (BBCSA) on the players' rating per match in a Pythagorean fuzzy setting, we establish the players' interactions, communications, passing, contributions, and performances to ascertain the high ranking players based on performances. Similarly, a comparative analyses are presented in tables to undoubtedly express the superiority of the newly developed method of SFPFSs. Due to the flexibility of the newly developed method of SFPFSs, it can be used for clustering analysis. In addition, the new method of SFPFSs can be extended to other uncertain environments other than PFSs.&lt;/p&gt;&lt;/abstract&gt;

A Mathematical Analysis of Team Impact and Individual Player Contribution in Football

In this paper, we present an important application of the Hungarian Method - a well-known combinatorial optimization tool for solving assignment problems. For our purposes, we consider the assignment of players to specific roles in a football team. It involves the broad classification of team players as defensive, midfield or attacking, while assigning the main roles associated with each of these positions. This provides insight on specific role of each individual player, thereby facilitating an optimal team selection. To illustrate this method, we utilize the average player statistics per game for two teams from the 2016/2017 Premier League Season. In addition, a team rating index is created by identifying six sub-indices. The first is called team contributions - which includes set piece goals, percentage tackles won, percentage take-ons won, percentage aerial duels won, number of interceptions, number of blocked shots, number of clearances, number of red and yellow cards. To visualize the method, a multiple correlation is carried out on team data for the 2016/2017 Premier League season to generate a correlation coefficient for each contribution. The resulting team index can be a useful tool for measuring the overall strengths of competing teams in a football league. Keywords: Hungarian method, football, team rating index, multiple correlations, team comparisons

Tracking Abuse on Twitter Against Football Players in the 2021 – 22 Premier League Season

Tracking Abuse on Twitter Against Football Players in the 2021 – 22 Premier League Season

The Association Between Subjective Wellness Symptoms and Blood Biomarker Data in English Premier League Footballers

The present study investigates the association between subjective wellness symptoms, and categorical point-of-care (POC) blood biomarkers of the free oxygen radical test (FORT), and systemic inflammation through high sensitivity C-reactive protein (Hs-CRP), in English Premier League footballers. Data from 38 male professional elite athletes (Mean Age = 25.8, SD = 4.4) from the English Premier League were included in the study, with a total of 674 individual testing records collected over an entire Premier League season. A player wellness questionnaire, along with fasted and rested point-of-care blood biomarker testing were collected weekly across the season. The wellness questionnaire collected subjective symptoms of illness and fatigue, while FORT and Hs-CRP were assessed through point-of-care analysis to highlight periods of excessive hydroperoxide production and systemic inflammation. Using a chi square goodness of fit model, results showed that there was a significant association between the frequency of symptoms logged and categorical POC blood biomarker data of FORT and HsCRP (P < 0.01). Of the records demonstrating normal levels of Hs-CRP and FORT concentrations, 27% logged symptoms with an average of 1.5 symptoms reported per answered record. Comparatively, excessive biomarker values demonstrated 55% of records having symptoms logged, averaging 2.4 symptoms reported per record.

Discrimination on football Twitter: the role of humour in the Othering of minorities

This article explores the footballing Twitter community and the abundance of tweets that use discriminatory humour to ‘Other’ minorities. With a 32% increase in instances of discrimination during the 2018/19 Premier League season, this qualitative study collected tweets containing some form of discriminatory language or slurs to be analysed. Two main concepts were identified, Othering and humour, where the Othering of minorities was often concealed by humour. It appears that discriminatory comments, no matter their inconspicuousness, have found a new home online as discrimination at games is now met with condemnation and punishment. Ultimately, this article highlights the justification of discriminatory language on football Twitter, whether stated outright or otherwise, when it is encompassed with humorous undertones. The conclusions reflect on how this understanding can be helpful for authorities and social media companies to use in identifying this form of abuse.

PREDICTION OF THE ENGLISH PREMIER LEAGUE CHAMPION TEAM FOR THE 2021/2022 SEASON USING THE NAÏVE BAYES METHOD

The English Premier League is the top division league in England, the most popular football league for football fans. The competition date for the 2021/2022 season was announced on March 25, 2021 and the schedule was released on June 16, 2021. Manchester City won last season (2020/2021). The purpose of this study is to find out the results of Naive Bayes' accuracy in determining which team deserves to win in the English Premier League 2021/2022 season and which team does not deserve to win in the final standings. The method used for prediction is Naïve Bayes, which is to predict an opportunity in the future based on events in the past which is then called the Bayes theorem. Combined with Naïve's theorem, it can be said that a condition between attributes can be independent. Naïve Bayes is an arrangement of a system that calculates a collection of opportunities that are summed at a frequency and a combination of values ​​from the existing data set. Based on the research conducted, it was generated from predictions to determine the winning team quickly and accurately and from the test results it was necessary to process training data and test data with the Naïve Bayes method, it was obtained an accuracy of 32.7% for the chances of Manchester City winning and Liverpool with 42.06% accuracy to be able to win the English Premier League season 2021/2022.

Investigating the efficiency of the Asian handicap football betting market with ratings and Bayesian networks

Despite the massive popularity of the Asian Handicap (AH) football (soccer) betting market, its efficiency has not been adequately studied by the relevant literature. This paper combines rating systems with Bayesian networks and presents the first published model specifically developed for prediction and assessment of the efficiency of the AH betting market. The results are based on 13 English Premier League seasons and are compared to the traditional market, where the bets are for win, lose or draw. Different betting situations have been examined including a) both average and maximum (best available) market odds, b) all possible betting decision thresholds between predicted and published odds, c) optimisations for both return-on-investment and profit, and d) simple stake adjustments to investigate how the variance of returns changes when targeting equivalent profit in both traditional and AH markets. While the AH market is found to share the inefficiencies of the traditional market, the findings reveal both interesting differences as well as similarities between the two.

Effect of a new concussion substitute rule on medical assessment of head collision events in Premier League football

ObjectiveTo investigate the utilisation of additional permanent concussion substitutes (APCSs) and its efficacy with regards to rate and duration of medical assessment of head collision events (HCEs) in the 2020–2021...

Re-assessing the Psychometric Properties of Stress Appraisal Measure in Ghana Using Multidimensional Graded Response Model.

Despite the widespread use of the stress appraisal measure questionnaire in sport psychology literature, information on the psychometric properties of this survey instrument across different cultures and samples is still lacking. This study sought to validate the stress appraisal measure among male football players in the Ghana’s Premier League using the multidimensional item response theory. The descriptive cross-sectional survey design was adopted to recruit 424 footballers from the 2020/2021 Ghana Premier League season using the census approach. The 28-item Stress Appraisal Measure was used to assess six (6) appraisal mechanisms under primary and secondary cognitive appraisals. The ordered polytomous item response theory was used for analyzing the data. The study found that although some items were problematic, the majority of them were found to have good item parameters, effective scale option functioning, and provided adequate empirical information in the measurement of stress appraisal. This research concluded that the stress appraisal measure has promising applicability among male footballers who participated in the premier league in Ghana. Future researchers are encouraged to re-validate the stress appraisal measure with a different sample to contribute to the understanding of the applicability of the instrument in non-western populations.

Poisson Modeling and Predicting English Premier League Goal Scoring

The English Premier League is well-known for being not only one of the most popular professional sports leagues in the world, but also one of the toughest competitions to predict. The first purpose of this research was to verify the consistency between goal scoring in the English Premier League and the Poisson process; specifically, the relationships between the number of goals scored in a match and the Poisson distribution, the time between goals throughout the course of a season and the exponential distribution, and the time location of goals during football games and the continuous uniform distribution. We found that the Poisson process and the three probability distributions accurately describe Premier League goal scoring. In addition, Poisson regression was utilized to predict outcomes for a Premier League season, using different sets of season data and with a large number of simulations being involved. We examined and compared various soccer metrics from our simulation results, including an English club’s chances of being the champions, finishing in the top four and bottom three, and relegation points.

The development of metrics for measuring the level of symmetry in team formation and ball movement flow, and their association with performance

ABSTRACT Objective In the current study, we examined how football players’ usage of space and the teams’ ball movement flow throughout the match are associated with the quality of performance achieved in competition. Methods We constructed two novel metrics that aimed at measuring spatial symmetry and passing flow on a team-wide scope. Our aim was to clarify whether the way players position themselves throughout the match, and the passing patterns they produce, can explain team success. Results An analysis of all matches played in the 2018–2019 English Premier League season (N = 380) revealed a positive relationship between the teams’ spatiotemporal dynamics and team performance. Conclusion Based on the findings of our analysis, we discuss the strengths and limitations of the newly-developed metrics, and stress the need for additional studies examining their effectiveness.

English Premier League manager perceptions of video assistant referee (VAR) decisions during the 2019-2020 season

ABSTRACT Video Assistant Referee (VAR) has become an emerging component of soccer. Prior inquiries have explored its impact on various game-related foci, but there remains a research gap surrounding participant and manager reactions to its usage. In an effort to provide such insights, this study examines the manager reaction to every VAR decision of the 2019–2020 English Premier League (EPL) season, its inaugural year of use within the league. While the EPL indicated the decision to adopt VAR was intended to aide in increasing fairness within the sport, there remained questions as to whether managers would perceive these indications as reality. Using secondary sourced post-match comment interviews of managers, reactions to VAR decisions were coded and themed. Results indicate mixed themes of manager perceptions of VAR decisions in the EPL during its inaugural year. The implication of this study provides insight to managers’ perceptions of an emerging component of soccer.

Estimating the England Premier League Ranking with Artificial Neural Network

ABSTRACT The aim of this study is to estimate the teams’ league rankings at the end of the season by using different parameters peculiar to soccer with artificial neural networks (ANNs). In this study, the values belonging to stealing the ball, number of passes (pass on target, forward pass, and pass before goal scoring), number of possessions during the match, attack time resulting in the goal scoring and number of shots in 1140 competitions played in 2015/2016, 2016/2017, and 2017/2018 England Premier League seasons have been evaluated. Season ranking in the 2017/2018 season has been estimated by analyzing the data in the first two seasons (2015/2016, 2016/2017). All data have been separated randomly for training and test. League ranking has been modeled numerically as 0 and 1. Because the generated value is between 0 and 1, the league ranking has been obtained by multiplying this value by 100 for a trained network. Thanks to the ANN model developed by training and testing according to the findings, the training, validation, test, and all regression values of the English Premier League have been obtained as 0.99779, 0.98123, 0.96981, and 0.98769, respectively. With respect to this result, it has been seen that number of shot, stealing the ball, attack time, and number of possessions parameters are determinant in team ranking at the end of the season along with the other parameters in the England Premier League. We think that analyzing matches with the ANN model provides fast and objective results for team managers, trainers, athletes, and betting shops.