Application of multivariant decision tree technique in high performance football: The female and male corner kick

Maneiro, Rubén; Casal, Claudio A.; Ardá, Antonio; Losada, José Luís

doi:10.1371/journal.pone.0212549

Cited by 31 publications

(36 citation statements)

References 32 publications

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“…This study also confirms that one of the two most productive methods of machine learning are the decision trees [26]. Previous studies that included machine learning application in soccer used as discriminating variables the participation of star players or not [27], technical variables such as passes or shots [28][29][30] or the previous results in the season [31]. A more current study is the only one found that uses physical variables to establish cut-off points but applied to simulated games [32].…”

Section: Introductionsupporting

confidence: 78%

Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players

et al. 2020

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The aim of this study was to analyse different success models and split time on cut-off point values on physical demands to keep category in semi-professional football players. An ad hoc observational controlled study was carried out with a total of ten (840 match data) outfield main players (25.2 ± 6.3 years, 1.79 ± 0.75 m, 74.9 ± 5.8 kg and 16.5 ± 6 years of football experience) and monitored using 15 Hz GPS devices. During 14 official matches from the Spanish division B in the 2016/2017 season, match data were coded considering the situational variable (score) and classified by match results (winning, losing or drawing). The results show significant differences between high-intensity attributes criteria that considered split time in velocity zones of 0–15 min (p = 0.043, ηp2 = 0.065, medium), 30–45 min (p = 0.010, ηp2 = 0.094, medium) and 60–75 min (p = 0.015, ηp2 = 0.086, medium), as well as sprint 60–75 min (p = 0.042, ηp2 = 0.066, medium) and 75–90 min (p = 0.002, ηp2 = 0.129, medium). Decision tree induction was applied to reduce the disparity range of data according to six 15-min intervals and to determine the cut-off point values for every parameter combination. It was possible to establish multivariate models for the main high-intensity actions criteria, allowing the establishment of all rules with their attributes and enabling the detection and visualisation of relationships and the pattern sets of variables for determining success.

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Section: Introductionsupporting

confidence: 78%

Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players

et al. 2020

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“…For this, the FIFA Women's World Cup 2015 has been analyzed, where the most representative and highest quality teams and players have met. The main novelties or contributions of this work could be summarized in two: the study and analysis of a sport still lacking robust scientific literature, such as women's football; and a new multivariate technique in the world of football research, such as decision trees, has been put to the test (Maneiro et al, 2019c). A first general approach, the available results allow us to think that significant differences exist between successful and unsuccessful teams regarding the "match status" variable in the three analysis performed.…”

Section: Discussionmentioning

confidence: 99%

“…For this, the chi-square automatic interaction detector (CHAID) was used as a growth method, which consists of a statistical and multidirectional tree algorithm that scans data quickly and efficiently, and creates segments and profiles compared to the desired result. In each step, CHAID chooses the predictor variable that presents the strongest interaction with the explained variable (Maneiro et al, 2019c). The categories of each predictor merge if they are not significantly different from the predictive variable.…”

Section: Statistic Analysismentioning

confidence: 99%

The Influence of Match Status on Ball Possession in High Performance Women’s Football

et al. 2020

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The objective of this study was to examine the effect of the situational match status variable on the ball possession of the teams that participated in the 2015 FIFA Women's World Cup. The 52 games played during the championship have been collected, and 3,740 ball possessions made by the teams were analyzed. The teams have been divided into successful and unsuccessful. Three types of analysis have been carried out: a univariate analysis for both groups with the categorical and continuous variables selected; a bivariate analysis, using chi-square tests and the exact Fischer test; and finally, a multivariable technique such as the decision trees was incorporated. The available results show significant differences between the two groups considered. Specifically, there are significant differences between winning and losing teams in terms of match status. The results of the post hoc test have shown that unsuccessful teams make few ball possessions with a winning match status, most of the possessions are performed when they are losing. Instead, successful teams make more possessions when they are winning than when they are losing. Also, spend more time keeping the ball in their offensive zone, and completing a greater number of passes in it. The results of the decision tree identified that the unsuccessful teams have more ball possessions in forward and middle lines with a draw during the first half, while in the second, a large percentage of possessions are made with an unfavorable match status. Instead, the successful teams have more ball possessions in the first part with a draw, while in the second it happens with a favorable match status.

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“…There are no two equal matches, nor two identical competitions. Therefore, the flexibilization of motor, technical, tactical and strategic behaviors, concretized in a wide inter and intra-individual variability (Moura et al, 2013;Casal et al, 2016;Maneiro et al, 2019b), will allow the emergence of solutions to changing and unpredictable environmental problems. Adaptation to the environment is essential (Seifert et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Variability of the Game Space in High Performance Football: Implementation of the Generalizability Theory

2020

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The analysis of variability in sport has shown significant growth in recent years. Also, the study of space management in the game field has not been object of research yet. The present study pretends to describe the variability in the use of strategic space in high performance football. To do this, the spatial management of the Spanish men's soccer team when it is in possession of the ball has been analyzed, during its participation in the UEFA Euro 2012 championship. Specifically, 6861 events have been collected and analyzed. Different zoning of the field have been used, and the location of the ball has been recorded in each offensive action. Using the observational methodology as a methodological filter, two types of analysis have been carried out: first, a General Linear Model was implemented to know the variability of the strategic space. Models with two, three, four and five variables have been tested. In order to estimate the degree of accuracy and generalization of the data obtained, the Generalizability Theory was implemented. Next, and in order to estimate the degree of accuracy and generalization of the data obtained, the Generalizability Theory was implemented. The results showed that the model that produces greater variability and better explanation is the four-variable model (P = 0.019; r 2 = 0.838), with the inclusion of the variables match half, rival, move initiation zone and move conclusion zone. Next, an optimization plan was implemented to know the degree of generalization with the Rival, Start Zone (SZ) and Conclusion Zone (CZ) facets. The available results indicate that it is based on an adequate research design in terms of the number of observations. The results of the present study could have a double practical application. On the one hand, the inclusion of the game's space management in training sessions will potentially conceal the true tactical intention. On the other hand, knowing the variability of the strategic space will allow to exploit areas of the optimal playing field to attack the rival team.

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Application of multivariant decision tree technique in high performance football: The female and male corner kick

Cited by 31 publications

References 32 publications

Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players

Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players

The Influence of Match Status on Ball Possession in High Performance Women’s Football

Analysis of the Variability of the Game Space in High Performance Football: Implementation of the Generalizability Theory

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