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Explaining the difference between men’s and women’s football

['Luca Pappalardo', 'Institute Of Information Science', 'Technologies', 'Isti', 'National Research Council', 'Cnr', 'Pisa', 'Alessio Rossi', 'Department Of Computer Science', 'University Of Pisa']

Date: 2021-08

Abstract Women’s football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men’s football. While the two sports are often compared based on the players’ physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match’s playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men’s and women’s football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier’s decisions. The differences between men’s and women’s football are rooted in play accuracy, the recovery time of ball possession, and the players’ performance quality. Our methodology may help journalists and fans understand what makes women’s football a distinct sport and coaches design tactics tailored to female teams.

Citation: Pappalardo L, Rossi A, Natilli M, Cintia P (2021) Explaining the difference between men’s and women’s football. PLoS ONE 16(8): e0255407. https://doi.org/10.1371/journal.pone.0255407 Editor: Anthony C. Constantinou, Queen Mary University of London, UNITED KINGDOM Received: January 22, 2021; Accepted: July 15, 2021; Published: August 4, 2021 Copyright: © 2021 Pappalardo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Data were generated by company Wyscout as part of this study and provided to us for research purposes. Data are not publicly available, but aggregated data are available on request by contacting ISTI-CNR ([email protected]) or SoBigData ([email protected]). Funding: LP, AR, MN and PC have been funded by EU project H2020 SoBigData++ RI, grant #871042. Competing interests: The authors have declared that no competing interests exist.

1 Introduction Women’s football took its first steps thanks to the independent women of the Kerr Ladies team, who gave the most significant impetus to this sport since the early twentieth-century [1]. As time passed, the Kerr Ladies intrigued the English crowds for their ability to stand up to male teams in numerous charity competitions. The success and enthusiasm of these events aroused concerns within the English Football Association, which on December 5th, 1921, decreed that “football is quite unsuitable for females and ought not to be encouraged”, and requested “the clubs belonging to the Association to refuse the use of their grounds for such matches” [1]. Unfortunately, this measure drastically slowed down the development of women’s football, which, after a long period of stagnation, resurfaced in the first half of the 1960s in Europe’s Nordic countries, such as Norway, Sweden, and Germany. From that moment on, the development of women’s football was unstoppable, spreading to the stadiums of Europe and the world and carving out a notable showcase among the most popular sports in the world. From 2012 the number of women academies has doubled [2], with around 40 million girls and women playing football worldwide nowadays [3]. In the last decade, the attention around women’s football has stimulated the birth of statistical comparisons with men’s football [2, 4, 5]. Bradley et al. [6] compare 52 men and 59 women, drawn during a Champions League season, and observe that women cover more distance than men at lower speeds, especially in the final minutes of the first half. However, at higher speed levels, men have better performances throughout the game [6]. Perroni et al. [7] show that speed dribbling with and without the ball is higher in male players than in female ones. Cardoso de Araújo et al. [8] highlight that men show a higher explosive capacity, intermittent endurance, sprint performance, and jump height than women. Moreover, women show lower blood lactate, maximal heart rate, and distance covered during an incremental endurance test than men. Sakamoto et al. [4] examine the shooting performance of 17 men and 17 women belonging to a university league, finding that women have lower average values than men on ball speed, foot speed, and ball-to-foot velocity ratio [4]. Pedersen et al. [3] question the rules and regulations of the game and, taking into account the average height difference between 20–25 years-old men and women, estimate that the “fair” goal height in women’s football should be 2.25 m, instead of 2.44 m. Gioldasis et al. [5] recruit 37 male and 27 female players from an amateur youth league and find that, while among male players, there is a significant difference between roles for almost all technical skills, among female players, just the dribbling ability presents a significant difference. Sakellaris [9] finds that, in international football competitions, female teams have a higher average number of goals scored per match than their male counterparts. Finally, Van Lange et al. [2] follow 157 female and 207 male young Dutch footballers to investigate the tendency to stop the game to permit a teammate’s or opponent’s care on the ground, finding that women show, on average, a greater willingness to help. An overview of the state of the art cannot avoid noticing that current studies focus on physical features and analyze small samples of male and female players using data collected on purpose. At the same time, although massive digital data about the technical behavior of players are nowadays available at an unprecedented scale and detail [10–15], investigations of the differences between women’s and men’s football from a technical point of view are still limited. Is the intensity of play in women’s matches higher than men’s ones? Are women more accurate than men in passing? Furthermore, does the statistical distribution of male players’ performance quality differ from that of female players? This article analyzes a large dataset describing 173k spatio-temporal events that occur during the last men’s and women’s World Cups: 64 and 44 matches, respectively, and 32 men’s and 24 women’s teams with 736 male players and 546 female players. To the best of our knowledge, ours is the largest sample of men’s and women’s football matches and players. We quantify players’ and teams’ performance in several ways, from the number of game events generated during a match to the proportion of accurate passes, the velocity of the game, the quality of individual performance, and teams’ collective behavior. We then tackle the following interesting question: Can a machine distinguish a male team from a female based on their technical performance only? Based on the use of a machine learning classifier, we show that men’s and women’s football do have apparent differences, which we investigate by extracting global and local explanations from the classifier’s decisions. Opening the classifier’s black box allows us to reveal that, while the game’s intensity is similar, the differences between men’s and women’s football are rooted in play accuracy, time to recover ball possession, and the typical performance quality of the players. Our methodology is helpful to several actors in the sports industry. On the one hand, a deeper understanding of female and male performance and playing style differences may help coaches and athletic trainers design training sessions, strategies, and tactics tailored for women players. On the other hand, our results may help sports journalists tell, and football fans understand what makes women’s football a distinct sport.

2 Football data We use data related to the last men’s World Cup 2018, describing 101,759 events from 64 matches, 32 national teams, 736 players, and the last women’s World Cup 2019, with 71,636 events 44 matches, 24 national teams, and 546 players. Each event records its type (e.g., pass, shot, foul), a time-stamp, the player(s) related to the event, the event’s match, and the position on the field, the event subtype, and a list of tags, which enrich the event with additional information [10] (see an example of an event in Table 1). Events are annotated manually from each match’s video stream using proprietary software (the tagger) by three operators, one operator per team and one operator acting as responsible supervisor of the output of the whole match. We have recently released the dataset regarding the men’s World Cup 2018 [10], in companion with a detailed description of the data format, the data collection procedure, and its reliability [10, 16]. Match event streams are nowadays a standard data format widely used in sports analytic for performance evaluation [11, 13, 16, 17] and advanced tactical analysis [18–20]. Fig 1a shows some events generated by a player in a match. Fig 1b shows the distribution of the total number of events in our dataset: on average, a football match has around 1600 events, whereas a couple of matches have up to 2200 events. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 1. (a) Example of events observed for a player in our dataset. Events are shown at the position where they have occurred. This plain “geo-referenced” visualization of events allow understanding how to reconstruct the player’s behavior during the match. (b) Distribution of the number of events per match. On average, a football match in our dataset has 1600 events. https://doi.org/10.1371/journal.pone.0255407.g001 PPT PowerPoint slide

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larger image TIFF original image Download: Table 1. Example of event corresponding to an accurate pass. eventName indicates the name of the event’s type: there are seven types of events (pass, foul, shot, duel, free kick, offside and touch). eventSec is the time when the event occurs (in seconds since the beginning of the current half of the match); playerId is the identifier of the player who generated the event. matchId is the match’s identifier. teamId is team’s identifier. subEventName indicates the name of the sub-event’s type. positions is the event’s origin and destination positions. Each position is a pair of coordinates (x, y) in the range [0, 100], indicating the percentage of the field from the perspective of the attacking team. tags is a list of event tags, each describing additional information about the event (e.g., accurate). A thorough description of this data format and its collection procedure can be found in [10]. https://doi.org/10.1371/journal.pone.0255407.t001

5 Conclusions While current works focus on the differences in physical characteristics between men and women, we reconstruct a mosaic of the differences in playing style using spatio-temporal match events related to the last men’s and women’s World Cups. However, the only performance metric relevant to the classifier’s classification is the PR score, which captures how good players were on average during a match, rather than the team’s playing style as captured by FC and the H indicator. Therefore, our model learns how to detect the differences in the performance and the technical characteristics of the teams rather than their playing style. Our analysis reveal that differences do exist in several technical features: the time between two consecutive events and the time required to recover possession are the lowest in women’s football, and women’s game is more “loyal”, i.e., women do fewer fouls than men). At the same time, men are typically more accurate in passing, and they kick the ball from a greater distance than women. Among the metrics that characterize team performance, just the PlayeRank score [16] reveals significant differences among men’s and women’s football. The inspection of a model that classifies team gender from the technical features through local explanations provides a novel perspective to reason about the difference between men and women in football, highlighting the reason behind the peculiar cases in which the classifier has been “fooled” by a team’s technical performance. Our results are open to various interpretations. The lack of statistically significant difference in the number of events and shots suggests that, overall, men’s and women’s football have similar play intensity. In contrast, the higher accuracy of passes in men’s matches may be due to the higher technical level of male players, which may be rooted in the fact that national teams in the men’s World Cup are mainly composed of professional players. In contrast, several female national teams (e.g., Italy) are composed of non-professional players or professional players for a short time. This difference reflects in a lower training time spent by women and therefore in a lower technical level compared to men, as previous studies demonstrate that training time is related to technical capabilities [7, 31, 32]. In this regard, dedicating more time to train specific technical capabilities, such as neuromuscular (i.e., strength) and cognitive (i.e., decision-making, visual searching processes, ability to maintain alert) functions, is crucial to make the training of women more effective [7, 33]. Although women’s football is progressively shifting to professionalism and technical level is increasing rapidly, there is still a technical gap between the two sports. The shorter recovery time observed for women’s matches may be due to the lower pass accuracy (i.e., more balls lost), to a better capacity to press the opponents and recover ball possession (e.g., high number of duels), and the higher number of interruptions (i.e., offside and free kicks). Differently, player centrality is typically higher in men’s football, denoting the presence of “hubs” that centralize the game (higher flow centrality) and higher variability in the performance quality across teammates (higher H indicator and PR score). In other words, passes in women’s football are more uniformly distributed across the teammates. Women’s football also has a preference for short passes over long balls. Since accurate long balls are more difficult than short ones, this preference may be a solution to compensate for women players’ lower technical level or different physical characteristics. Indeed, several technical variables are linked to the physiological and anthropometric differences between genders: for example, passing and shooting distances are affected by muscle strength and anthropometrical factors, which differ between the sexes [7]. As future work, we plan to investigate differences in men’s and women’s football in national tournaments and to investigate to what extent these differences vary nation by nation and between national and continental competitions. Is the difference we found in this paper more marked in the longer competitions for clubs?

Acknowledgments We thank WyScout Spa for providing the match events, Daniele Fadda for his support on data visualization, and Giuseppe Pontillo for his contribution.

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