Predicting match outcome according to the quality of opponent in the English premier league using situational variables and team performance indicators

Bilek, Gunal; Ulaş, Efehan

doi:10.1080/24748668.2019.1684773

Cited by 25 publications

(34 citation statements)

References 24 publications

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“…It is not clear why this occurs but it may relate to the psychological impact on both teams. This aligns with the findings of previous research in women’s [ 10 ] and men’s soccer [ 38 , 39 ]. Even though this might be hard to implement in training and is very dependent on the match situation, awareness of this fact is important to coaches.…”

Section: Discussionsupporting

confidence: 92%

Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights

et al. 2020

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Knowledge of optimal technical performance is used to determine match strategy and the design of training programs. Previous studies in men’s soccer have identified certain technical characteristics that are related to success. These studies however, have relative limited sample sizes or limited ranges of performance indicators, which may have limited the analytical approaches that were used. Research in women’s soccer and our understanding of optimal technical performance, is even more limited (n = 3). Therefore, the aim of this study was to identify technical determinants of match outcome in the women’s game and to compare analytical approaches using a large sample size (n = 1390 team performances) and range of variables (n = 450). Three different analytical approaches (i.e. combinations of technical performance variables) were used, a data-driven approach, a rational approach and an approach based on the literature in men’s soccer. Match outcome was modelled using variables from each analytical approach, using generalised linear modelling and decision trees. It was found that the rational and data-driven approaches outperformed the literature-driven approach in predicting match outcome. The strongest determinants of match outcome were; scoring first, intentional assists relative to the opponent, the percentage of shots on goal saved by the goalkeeper relative to the opponent, shots on goal relative to the opponent and the percentage of duels that are successful. Moreover the rational and data-driven approach achieved higher prediction accuracies than comparable studies about men’s soccer.

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Section: Discussionsupporting

confidence: 92%

Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights

et al. 2020

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“…If the opponent is presenting a lower level of quality, a home match does not affect the odds of winning, but it increases the probability of a draw and reduces the probability of a loss. However, if the opponent is stronger, playing at home provides an extra 14% chance of winning ( Bilek and Ulas, 2019 ). Other respondents indicate that playing at home and playing against high-ranked opponents increases the effectiveness of the offense but does not affect the goal-scoring opportunities ( Gonzalez Rodenas et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, if the team is the host, its probability of winning increases to 0.62. In addition, if the team manages to keep the percentage of possession of the ball above 44%, it is very likely that the team will win the game with a probability of 0.89 ( Bilek and Ulas, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, researchers indicate a number of other determinants of players’ activity during a match, including quality of opposition ( Bilek and Ulas, 2019 ), team and opposition ability ( Redwood-Brown et al, 2019 ), team formation ( Aquino et al, 2020 ), match status ( Gonzalez Rodenas et al, 2019 ), players’ age and experience ( Lorenzo-Martínez et al, 2019 ), and climate conditions ( Coker et al, 2020 ). However, during a soccer match, players experience all these factors simultaneously, which is why modern studies focus on multifactorial analyses, using advanced statistical methods ( Moura et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

“… Aquino et al (2020) noted that matches played at home or against “weaker” opponents presented greater running demands and that match outcome demonstrated influence only for running performance; matches in which the reference team won resulted in higher values than in matches lost. Bilek and Ulas (2019) used the decision tree identifying scoring first as the most influential on match outcome in each decision tree, and the impact of match location on the match outcome varied according to the quality of the opponent. Multivariate analyses (adjusted logistic multilevel models) by Gonzalez Rodenas et al (2019) showed how playing at home and against high-ranked opponents involved a higher likelihood of achieving offensive penetration although no associations were found in terms of creation of scoring opportunities.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Match Location and Players’ Physical and Technical Activities on Winning in the German Bundesliga

et al. 2020

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This study aimed to examine whether the physical and technical activities of soccer players and match locations can be associated with higher or lower odds of winning matches whose outcome can be described as “close.” The study comprised 7972 individual observations of German Bundesliga players during the 2014/2015 ( n = 2794), 2015/2016 ( n = 2494), and 2016/2017 ( n = 2684) seasons. A selection of “close matches” was made, which were defined as those in which the difference in numbers of scored goals was ≤1. Players’ five pitch positions were considered: central defenders, fullbacks, central midfielders, wide midfielders, and forwards. Data on 12 physical and 10 technical activities performed by players during matches as well as on match location were retrieved from the Impire AG (Germany) match analysis system. The study results show that the odds of winning at home are different for each playing position: from 41.99% for wide midfielders to 91.34% for central midfielders. Another conclusion was that one of the key components of the predictive model for forwards is the percentage of overall distance covered at speeds >24 km/h (7.99%), which is a variable with an increasing trend. The proposed model predicts that each 1% increase in this variable will theoretically be associated with a 4.08% raise of the odds of winning in further seasons. The presented statistical model may be used by trainers to identify players’ physical and technical activities and contextual variables that may significantly affect the match outcome. In addition, it can help to determine the individual training load related to the player’s position on the pitch.

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Evolution of the Beautiful Game: Analytics and Virtual Reality

Parekh,

Oyeleke

2024

Lecture Notes in Computer Science

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Predicting match outcome according to the quality of opponent in the English premier league using situational variables and team performance indicators

Cited by 25 publications

References 24 publications

Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights

Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights

The Impact of Match Location and Players’ Physical and Technical Activities on Winning in the German Bundesliga

Evolution of the Beautiful Game: Analytics and Virtual Reality

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