Asymmetries in Football: The Pass—Goal Paradox

Antequera, Daniel R.; Garrido, David; Echegoyen, Ignacio; Campo, Roberto López-Del; Serra, R. Resta; Buldú, Javier M.

doi:10.3390/sym12061052

Cited by 5 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We found that all passing indices for the matches between the first eight and the last eight were significantly greater than the matches between the first eight. This result is in accordance with a previous analysis that found that the top teams made more passes and scored more goals, while teams relegated to a lower level had, on average, a lower number of passes and goals [ 11 ]. However, there are also some studies showing that passing indices have a smaller contribution to ranking points obtained in a professional game [ 34 ] and are less predictable when considering team quality and home advantage [ 35 , 36 ].…”

Section: Discussionsupporting

confidence: 92%

“…The top teams in the season pass more times than other teams. The number of passes in the second half of the match was lower, but the number of goals was higher [ 11 ]. A comparison of the passing methods of Spain and South Korea, the champions of the 2010 World Cup in South Africa, found that there were significant differences in the number of short-passes in the first half and the number of short-passes, middle passes, long-passes, flat passes and direct passes in the second half [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of influencing factors of passes in the chinese super league

Zeng

Zhang

2022

BMC Sports Sci Med Rehabil

View full text Add to dashboard Cite

Background In football matches, passing is an important means of organizing attacks, creating shots, changing tactics, and achieving tactical objectives and is a frequently used technique. The purpose of the study was to explore the relevant factors that affect passes in the Chinese Super League (CSL) in different game contexts. Methods A total of 1,440 matches (2,880 samples) of 24 teams participating in the CSL in the 2014–2019 seasons were selected as the research object, and a mixed linear model was constructed to analyse the influencing factors of passes. Results (a) The passing success rate and the total number of forward passes were greatly affected by the stage of the season; (b) except for the passing success rate in the 30 m attack area, all other pass indicators in the home matches were significantly higher than those in the away matches; (c) the number of passes in the 30 m attack area was mainly affected by the team level (table position quartile); the higher the team level, the greater the number of passes in the 30 m attack area; (d) all passing indices for the matches between the first eight and the last eight were significantly greater than the matches between the first eight; (e) the passing success rate in the 30 m attack area in winning matches was higher than that in drawn matches; (f) the number of forward passes was significantly greater in matches won by two or more goals, and the number of defender passes was significantly greater in losing matches. Conclusion The various passing indices of the CSL were affected by different game contexts (including season stage, venue, team level, match type and match outcome), and understanding these influencing factors of passes will help coaches and players better understand football matches.

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Analysis of influencing factors of passes in the chinese super league

Zeng

Zhang

2022

BMC Sports Sci Med Rehabil

View full text Add to dashboard Cite

show abstract

“…A critical factor in predicting soccer match outcomes is successfully incorporating domain knowledge into the machine-learning modelling process 34 Building a standard linear model for an entire soccer team or subgroup, as in the APX-Grp approach, can be misleading and should be used cautiously and only when not enough individual data is available. We have encountered situations where the relationships in unique models contradict those that reveal joint modelling for the group, perfectly illustrating Simpson's paradox 35,36 . The experience reminds us to be careful when interpreting soccer data because relationships observed for a group of players usually refer to momentary relationships between players, not temporal relationships in player performances.…”

Section: Discussionmentioning

confidence: 87%

Forecasting extremes of football players' performance in matches.

Nowak,

Bok,

Wilczek

et al. 2024

Preprint

View full text Add to dashboard Cite

Based on training history, accurate predictive modelling of an athlete's performance in competition can be a cornerstone for prior optimal planning of exercise mix and intensity. While universal in many sports, such a goal is challenging in football due to the complexity of factors leading to the final score and the complexity of the preceding training process. We developed and tested a range of models, the best of which were to forecast selected play performance indices with an accuracy of 10-20%. Such score applies to models run on raw player location data and aggregating performance indices developed with expert knowledge in the football training domain. Results show that individual player models perform better than collective ones and that more recent training data are better predictors. While we consider the accuracy of the models still of limited reliability, their transparency and present quality make them useful in the daily planning of training activities that impact player performance in the coming match. Additionally, observations of training parameters generated in short-term intervals are more effective and correlated with extreme match results than long-term dates. Specific training parameters may be key in predicting exceptional football player performance, but they may also vary from person to person.

show abstract

“…Specifically, shots on target are one of the best variables to discriminate between successful and unsuccessful teams [11 -13]. Also indicators of success are ball possession [14 -16], total number of shots [17 -19], ball retrieval location [20,21], number of passes and success rate of completed passes [11,18,19,22,23].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Pass Length and Height in Europe's Top 5 Leagues in Men's Football

Cordón-Carmona,

Villavicencio Álvarez,

Morales

et al. 2023

TOSSJ

View full text Add to dashboard Cite

Aims: The goal is to investigate how the length and height of passes impact a team's performance in national championships. A predictive model will be developed to analyse the success of different pass characteristics, including short, medium, and long length and ground, low, and high height. The model will be based on the points earned and will determine which combination of pass characteristics is most effective. Background: Passing is a critical aspect of technical skill for any football team. It involves transferring the ball from one player to another. Using a multiple linear regression model makes it possible to determine the most effective combination of pass length and height for scoring points. This model can help establish an equation that relates the three types of passes to the points scored. Objective: The objectives of this study are to develop a predictive model of pass length and height with the points obtained to know which type or combination of pass characteristics is most successful. Methods: We analyse match data from the 2017-2018 to 2020-2021 seasons of the 5 main European leagues. The variables analysed are based on pass length (short, medium and long) and height (ground, low and high). The correlation coefficient was used to measure the relationship between the variables and the points. A hierarchical multiple regression model was applied to determine the influence. Results: The results obtained showed that short passes explained 51% of the points scored by the teams, and the combination of the three types of distance improved the prediction to 54% of the points. About the height of the pass, when the three types were combined in the model, they managed to explain 54% of the points, where a great difference was observed between low and high passes, the high ones being more important. Conclusión: It can be concluded that the length and height of the pass are variables to be taken into account in obtaining points and in the team's performance. Other: Teams should prioritise short passes and pass along the ground, seeking to combine them with other types of passes promptly.

show abstract

Asymmetries in Football: The Pass—Goal Paradox

Cited by 5 publications

References 17 publications

Analysis of influencing factors of passes in the chinese super league

Analysis of influencing factors of passes in the chinese super league

Forecasting extremes of football players' performance in matches.

The Influence of Pass Length and Height in Europe's Top 5 Leagues in Men's Football

Contact Info

Product

Resources

About