Modeling the impact of players’ workload on the injury-burden of English Premier League football clubs

2018 ◽  
Vol 28 (6) ◽  
pp. 1715-1721 ◽  
Author(s):  
C. W. Fuller
GYMNASIUM ◽  
2019 ◽  
Vol XIX (2) ◽  
pp. 44
Author(s):  
Christos Koutroumanides ◽  
Panagiotis Alexopoulos ◽  
Athanasios Laios ◽  
John Douvis

In the last years football clubs games broadcasting rights selling process, internet and mobile phone packages are included too. Internet is a widely accepted and useful mean of communication, something that was immediately recognized by the football authorities and clubs in all countries. Same ways, the gradually increased use of smart phones led to the inclusion of the mobile broadcasting rights in the total selling rights packages. The latest rights auction is of paramount importance, not only because the rights value will break a new record, but also because among the interested parties and bidders are some of the world’s tech giants such as Amazon, Google and Netflix. Live streaming Premier League matches looks to be the next frontier and PL hope tech giants like Amazon, Google, Facebook and Netflix will enter the auction and push the price of the UK package above its current standing of £5.14billion


2018 ◽  
Vol 14 (4) ◽  
pp. 185-199 ◽  
Author(s):  
Francesco Audrino

Abstract We address the fiercely debated question of whether the strongest European football clubs get special, preferential treatment from match officials in their decisions on the teams’ players over the course of the teams’ trophy winning streaks. To give an empirical answer to this question, we apply a rigorous econometric analysis for causal effect estimation to a self-constructed data set. We consider the two clubs in the Italian Serie A that experienced a prolonged winning streak during the period 2006–2016, namely Internazionale Milan (Inter) and Juventus Turin, as well as one team from the German Bundesliga (Borussia Dortmund) and one from the English Premier League (Manchester United) that also experienced a winning streak during the same period. This allows us to perform an analysis with enough statistical power to be able to estimate properly the effect of interest. The general opinion among fans, sports journalists, and insiders that the strongest clubs are favored by match officials’ decisions is supported only by the results of the analysis we run for Juventus, whereas for the other clubs under investigation, we did not find any significant bias. During its winning streak, more yellow cards and total booking points (an aggregated measure of yellow and red cards) were given to Juventus opponents. These effects are not only statistically significant, but also have a sizeable impact.


2020 ◽  
Vol 6 (1) ◽  
pp. e000675
Author(s):  
Eyal Eliakim ◽  
Elia Morgulev ◽  
Ronnie Lidor ◽  
Yoav Meckel

BackgroundIn individual sports, the effect that injuries have on an athlete’s performance, success and financial profit is implicit. In contrast, the effect of a single player’s injury or one player’s absence in team sports is much more difficult to quantify, both from the performance perspective and the financial perspective.ObjectivesIn this study, we attempted to estimate the effect of injuries on the performance of football teams from the English Premier League (EPL), and the financial implications derived from this effect.MethodsOur analysis is based on data regarding game results, injuries and estimations of the players’ financial value for the 2012–2013 through the 2016–2017 seasons.ResultsWe found a statistically significant relationship (r=−0.46, 95% CI −0.6 to 0.28, p=0.001) between the number of days out due to injuries suffered by team members during a season and the place difference between their actual and expected finish in the EPL table (according to overall player value). Moreover, we can interpolate that approximately 136 days out due to injury causes a team the loss of one league point, and that approximately 271 days out due to injury costs a team one place in the table. This interpolation formula is used as a heuristic model, and given the relationship specified above accounts for a significant portion of the variance in league placement (21%), the remaining variance is related to other factors. Calculating the costs of wage bills and prize money, we estimate that an EPL team loses an average of £45 million sterling due to injury-related decrement in performance per season.ConclusionProfessional football clubs have a strong economic incentive to invest in injury prevention and rehabilitation programmes.


2019 ◽  
Vol 89 (6) ◽  
pp. 633-653 ◽  
Author(s):  
Christian Gjersing Nielsen ◽  
Rasmus K. Storm ◽  
Tor Georg Jakobsen

2014 ◽  
Vol 19 (6) ◽  
pp. 390-399 ◽  
Author(s):  
Stuart W. Flint ◽  
Daniel J. Plumley ◽  
Robert J. Wilson

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242728
Author(s):  
Otto Kolbinger ◽  
Melanie Knopp

Evaluative research of technological officiating aids in sports predominantly focuses on the respective technology and the impact on decision accuracy, whereas the impact on stakeholders is neglected. Therefore, the aim of this study was to investigate the immediate impact of the recently introduced Video Assistant Referee, often referred to as VAR, on the sentiment of fans of the English Premier League. We analyzed the content of 643,251 tweets from 129 games, including 94 VAR incidents, using a new variation of a gradient boosting approach to train two tree-based classifiers for text corpora: one classifier to identify tweets related to the VAR and another one to rate a tweet’s sentiment. The results of 10-fold cross-validations showed that our approach, for which we only took a small share of all features to grow each tree, performed better than common approaches (naïve Bayes, support vector machines, random forest and traditional gradient tree boosting) used by other studies for both classification problems. Regarding the impact of the VAR on fans, we found that the average sentiment of tweets related to this technological officiating aid was significantly lower compared to other tweets (-0.64 vs. 0.08; t = 45.5, p < .001). Further, by tracking the mean sentiment of all tweets chronologically for each game, we could display that there is a significant drop of sentiment for tweets posted in the periods after an incident compared to the periods before. A plunge that persisted for 20 minutes on average. Summed up, our results provide evidence that the VAR effects predominantly expressions of negative sentiment on Twitter. This is in line with the results found in previous, questionnaire-based, studies for other technological officiating aids and also consistent with the psychological principle of loss aversion.


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