introduction to soccer essay

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Thrilling Soccer Essay: Here’s Your Guide To Writing!

Discover how you can pen down a fascinating soccer essay in minutes! Get tips and a free essay sample to kick start your journey today cozily.

One of the most-watched sport in the world is soccer. Almost everybody is aligned to one soccer team or the other regardless of age, gender, or even occupation. My grandfather still supports Manchester United until now from his youth.

So what makes an essay about soccer as impressive as the sport itself? That is why you are here. Your thirst will be quenched in a few.

Outline of Soccer Essays

Before a soccer match begins, the referee gives the rules to the players to ensure that the game runs smoothly. That is what we want to look at, the structure of a soccer essay.

Introduction

Someone once said, show me your friends, and I’ll tell you who you are. I would rephrase the same, too, show me your intro, and I will tell you whether I will read your essay or not. What am I insinuating here?

The soccer essay introduction will have an impact on your readers. It will either ignite the readers or turn them off, just like the battery’s role in a car. Thus, the importance of soccer essay hooks, such as quotes from famous players.

Your thesis statement about soccer in the introduction should connect to the background information through a transition. Being the heart of the essay, it should, therefore, be manageable and researchable.

The body of an essay about soccer is composed of paragraphs supporting the thesis statement. It should, therefore, be concise to allow for easy readability.

The same logical connection to the thesis statement should follow in the body paragraphs. Their length varies depending on the assignment.

The 5-paragraph essay is, however, the standard recommended essay body length.

When concluding a soccer essay, try to act like the referee. Let the players know that the match has come to an end.

Briefly, let’s see some soccer essay topics that can get your piece a Wembley stadium audience.

Striking Soccer Essay Topics

Benefits of playing soccer essay
An essay on the history of soccer
My passion is soccer essay
My favorite sport is soccer essay
Soccer as a unifying factor essay

Using one of the topics, we are going to explore a soccer essay sample for practice.

Sample of a Soccer Essay

Benefits of Playing Soccer Essay

“God gives gifts to everyone; some can write, some can dance. He gave me the skill to play football, and I am making the most of it.” A quote by Ronaldinho. Soccer is not a sport only but an oasis that quenches the thirsty hearts of many. Dating back to the Egyptians who used to play games involving kicking a ball, soccer has now spread like wildfire globally. Both men and women can now play this sport, not forgetting, the World Cup, help after every four years. It is indeed a sport that has come with great benefits not only to humanity but the whole planet at large.

Soccer has united people now more than ever. Initially, people would only mingle at a community or country level through their unique games and sports. However, soccer has broken these limits. Different people from all walks of life, race, gender, and age, and occupation, social, and political classes have come together. During the World Cup, this phenomenon is evident. Presidents, ordinary people can be seen on the stadium stands cheering their teams. What more could unite such classes than soccer?

The society has grown healthier as a result of soccer. Unhealthy eating habits have been a significant cause of diseases such as obesity, high blood pressure, and heart attacks. The cost of treating such conditions is expensive. Soccer provides a way of staying healthy, fit, durable, and ability to endure. One can join a community club or team and engage in vigorous soccer training. They have helped many to remain healthy and keep out of hospitals for years.

Generally, soccer is beneficial. The thoughts discussed may not be exhaustive, but the point is home. Everyone, both children and adults, blacks or whites, should embrace this excellent uniting and healthy sport. To have soccer is to score big!

Soccer Essay Made Simple

From the sample above, one can note that such an essay on soccer is as easy as getting pizza from McDonald’s. Its impact and role can be seen in everyday society and, therefore, easy to relate with at any stage of your writing. As always, the jargon should remain to create the context of your essay.

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How To Write an Interesting Soccer Essay: Examples, Topics, Titles, Outlines

Mon Oct 18 2021

If you are a sports enthusiast, you will probably write an essay about your favourite sport. So what does this paper mean to you? At what point do students write about it? Here are the tricks to writing a compelling soccer essay.

Why is Soccer Important?

It is necessary to understand that soccer essays are encouraged as the involvement in the sport brings about physical fitness besides other advantages. Students and especially from lower primary classes are encouraged to participate in extracurricular activities for all-around wellness. Some of the benefits include:

· Improving cardiovascular health

· Builds muscle and bone strength

· Improves flexibility and endurance

What Do You Write About in A Soccer Essay?

As long as you are passionate about the sport, writing an essay about soccer may not be a challenging experience for many students. There are many approaches for writing your assignment, and below are some soccer essay topics:

· Why it's your favourite sport

· A recap of a recently concluded match

· The world cup

· Soccer’s history

· The rewards of soccer players

· How to play soccer

· Overview of modern-day soccer

· Types of soccer

· How to nurture your soccer talent

· The importance of unity in playing soccer

Essentially, you need to identify your inspiration to write a winning essay. Once you do, carry out some research or sample some soccer essay samples online to help get you started.

How To Write A Soccer Essay

Are you worried about how to go about the 'why I love soccer essay?' Don't be nervous, as our extensive guide is your ultimate hack to scoring an excellent score in that assignment.

1. Conduct sufficient research

You may imagine that you know everything about soccer since you watch it on TV. However, watching the game and expounding on the game are two different things. To help get your way around with this coveted paper, read existing content to understand the language used and how to express your thoughts.

2 Create an outline

Your soccer essay outline will help organize your data. For instance, it assists with putting each thought chronologically. This way, you can distinguish the main ideas and how to form them into paragraphs and sub-titles.

3. Introduction

Having identified your key points in your blueprint, the next mission is to develop a compelling introduction. At this point, your goal is to capture your readers' attention while drawing them into reading the entire essay. Therefore, think of a controversial tagline, a question or a mind-blowing introduction.

4. Thesis statement

Although not compulsory when writing an essay, students writing research papers must incorporate a thesis statement in their soccer essay introduction. This is usually the last statement and takes a sentence or two. A thesis expresses your paper's objectivity.

5. Body

This is where all the meat goes, and you need to write your essay creatively because your paper's format will be graded. By this, matters relating to grammar, syntax and plagiarism all need to be dealt with while expressing your soccer ideas. Likewise, a paragraph about soccer should range somewhere between four to six sentences.

The soccer essay conclusion is where you provide a summary of your thoughts in a single paragraph. Your essay’s end should read as though the paper is coming to a close. Therefore don’t just finish it abruptly. Still, you could incorporate a call to action or a quote at the end to get people thinking about what they read in your essay.

7. Proofread and edit

This will be your last task. Go through the paper once more. Look out for errors, punctuation mistakes, language and incomplete sentences. You can even seek a second or third opinion to ensure that the paper is perfect before submission. After all, who wants to be penalized for avoidable mistakes? Of course, none of us does.

The best way to get you started with that assignment is to check out some of our soccer essay prompts. You only need to sign up to access everything you need regarding that project. Still, you can talk to us for help on writing an exemplary soccer essay. Our writers are capable of helping earn you nothing less than an A+.

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Essay on Soccer Game

Students are often asked to write an essay on Soccer Game in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Soccer Game

Introduction to soccer.

Soccer is a popular sport played worldwide. It involves two teams, each with 11 players. The game’s objective is to score more goals than the opposing team within a set time.

The Gameplay

Players move the ball across the field by striking it with any part of their body except their arms and hands. The team with the most goals at the end of the game wins.

The Importance of Soccer

Soccer is more than just a game. It teaches teamwork, discipline, and sportsmanship. It’s a fun way to stay active and make new friends.

250 Words Essay on Soccer Game

Introduction.

Soccer, also known as football in many parts of the world, is more than just a game; it’s a global phenomenon. Its simplicity, accessibility, and universal appeal make it the world’s most popular sport, transcending cultures, languages, and borders.

The Essence of the Game

At its core, soccer is a game of strategy, teamwork, and skill. Two teams, each with eleven players, compete to maneuver a ball into the opponent’s goal using any part of the body except the hands and arms. The team with the most goals at the end of the match is declared the winner.

Strategic Complexity

Despite its apparent simplicity, soccer is a complex game that requires strategic thinking. Coaches and players must constantly adapt their strategies based on the strengths and weaknesses of their opponents, the current state of the game, and even the weather conditions.

Role of Teamwork

Teamwork is vital in soccer. Each player has a specific role, and the team’s success depends on how well these roles are executed. From the forwards who score goals to the defenders who protect their own goal and the goalkeeper who is the last line of defense, every player is important.

Soccer’s Global Impact

Soccer has a profound social and cultural impact. It brings people together, fosters a sense of community, and promotes values such as teamwork, discipline, and fair play. Major tournaments like the FIFA World Cup are watched by billions, demonstrating soccer’s unifying power.

In conclusion, soccer is more than just a game; it’s a strategic and social activity that promotes teamwork and unity. Its global popularity is testament to its universal appeal and its ability to transcend cultural and social barriers.

500 Words Essay on Soccer Game

The history and evolution of soccer.

Soccer, also known as football in many parts of the world, is a sport that has existed in various forms for over two millennia. The modern game we recognize today was formalized in England in the mid-19th century, but its roots trace back to ancient civilizations like China, Greece, and Rome. The universal appeal of soccer lies in its simplicity, requiring only a ball, a field, and two teams ready to compete.

The Rules and Structure of the Game

A standard soccer match is played by two teams of 11 players each, including a goalkeeper. The game is played on a rectangular field with a goal at each end. The objective is to score more goals than the opposing team within the 90-minute timeframe, divided into two halves of 45 minutes each. The game’s rules, maintained by the International Football Association Board (IFAB), govern aspects such as fouls, offside, throw-ins, and penalties.

Strategy and Tactics in Soccer

Soccer is not just a physical game but a cerebral one as well. Teams employ various strategic formations, like the 4-4-2, 4-3-3, or the 3-5-2, to maximize their strengths and exploit the opposition’s weaknesses. Tactics can vary from fast, direct play to a slower, possession-based approach. Managers and coaches play a pivotal role in devising these strategies, making soccer a fascinating blend of individual skill and collective strategy.

The Cultural Impact of Soccer

Soccer has a profound cultural impact worldwide. Major tournaments like the FIFA World Cup unite nations and create a sense of shared identity. Soccer clubs often reflect the character of their local communities, and players can become national heroes or symbols of cultural pride. The sport has also been a platform for social change, tackling issues like racism and inequality.

The Future of Soccer

The future of soccer is exciting, with technological advancements promising to revolutionize the game. Video Assistant Referees (VAR) is one such innovation, aimed at improving decision-making accuracy. Additionally, data analytics is being increasingly used to enhance player performance and tactical planning. However, these developments also raise questions about maintaining the sport’s spirit and tradition amidst rapid modernization.

In conclusion, soccer is a complex and vibrant sport that transcends beyond the boundaries of the playing field. Its simplicity, strategic depth, cultural significance, and promising future ensure its position as the world’s most popular sport. As we look forward, we can anticipate soccer continuing to evolve, captivate, and unite people across the globe.

That’s it! I hope the essay helped you.

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Review of The History and Benefits of Soccer

Categories: Competitive Sports Recreation and Sports Soccer

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Published: Mar 18, 2021

Words: 469 | Page: 1 | 3 min read

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Essay Samples on Soccer

The soccer discourse community: passion, identity, and global connection.

Soccer, known as football to most of the world, is more than just a sport; it is a universal language that transcends geographical borders and cultural differences. Within the realm of this beloved game lies a dynamic and tightly-knit soccer discourse community. This essay explores...

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The Issue of Racism in Soccer: Causes, Effects, and Ways to Combat

Introduction Picture yourself as a person of color, having to confront racism in the profession you cherish. Wouldn't you long to release all that anger and frustration? Unfortunately, this is the reality for the black community and people of color in the realm of sports,...

Soccer as My Hobby and How It Shapes My Life

Hobby is an activity, habit or favorite choice of a human, who regularly performs in leisure or extra time for pleasure, relaxation and enjoyment. Everyone has different hobbies that he or she would like to do to have fun or relax. They can be physical...

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Soccer Vs Basketball: The Uniqueness Of Each Sport

Playing sports is an emotional, physical, and mental adventure. You have the opportunity to know whether you are a team player or a maverick. Soccer and Basketball are two of the most popular sports that are played by people around the world. The purpose of...

Bend It Like Beckham: Exploring the Differences with One Hobby

Bend It Like Beckham at first glance is a lighthearted film about two young women who bond over their love of soccer. However, the use of comedy thinly veils the important issues about different cultures existing together, and the difficulties faced by minority cultures in...

Bend It Like Beckham

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The Joy Soccer Brings to Me and Many Others

Joy is an essential feeling for us human beings. It is basically the feeling you get when you are doing something you love. Joy has long been identified as an important feeling for humans. Ancient Greek philosophers such as Socrates, Aristotle, and Plato believed that...

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How Rules in Soccer Make the Game Entertaining

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On the Soccer Field: A Shared Language, Knowledge, and Values

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The Creation and Unification of the Game of Soccer

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Overview of Physical Requirements and Rules of Soccer

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Usage of Analytic Data and Devices in Soccer

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Report on the Improving Success of Malton Soccer Club

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Best topics on Soccer

1. The Soccer Discourse Community: Passion, Identity, and Global Connection

2. The Issue of Racism in Soccer: Causes, Effects, and Ways to Combat

3. Soccer as My Hobby and How It Shapes My Life

4. Soccer Vs Basketball: The Uniqueness Of Each Sport

5. Bend It Like Beckham: Exploring the Differences with One Hobby

6. The Joy Soccer Brings to Me and Many Others

7. How Rules in Soccer Make the Game Entertaining

8. On the Soccer Field: A Shared Language, Knowledge, and Values

9. The Creation and Unification of the Game of Soccer

10. Overview of Physical Requirements and Rules of Soccer

11. Usage of Analytic Data and Devices in Soccer

12. Report on the Improving Success of Malton Soccer Club

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Essays on Soccer

You should do your research before taking on a soccer essay, so it would provide the most thorough analysis of the topic. Most soccer essays define soccer, also known as Association football, as a team ball game, in which players from one team must score as many goals as possible at the other team's goal, and vice versa. As many mentions in their essays on soccer, during the game, the ball can be kicked with feet as well as passed with hands – that makes Soccer different from Football that is played in the UK. Authors of essays also note another difference – Soccer is played with an oval football. Hoverer, the game rules of both games basically the same. Before you start working on your essays, we suggest you look through our soccer essay samples – we listed the most compelling essay samples for you below.

Lionel Messi's Early Life and Struggles Lionel Messi, a professional soccer player, is an Argentine who plays an attacking role for his country (Argentina) and Barcelona football club in Spain. He came from a working family where his father was a factory steel worker and the mother a cleaner. He started...

Personally, for me it very unethical that a 20-year-old with no studies can earn so much by kicking a ball unlike compared to a doctor who studied most of their lives to save humans earn much less compared to footballers. In order to analyze and find out the reason for...

Sporting events are an important form of recreation for many people all over the world. With the growth of numerous forms of athletics, there has been an increase in the number of fans with a variety of characteristics. Soccer, hockey, football, and baseball are only a handful of the sports...

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Hosting a mega-event has always been a privilege that most countries want, and in today's world, soccer is one of the most popular sports, with millions of people watching it around the world. This makes FIFA one of the most prestigious names in the world, as well as the branding...

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The month of October in the complete of Europe comes with action packed stadia with football fans thronging to have a piece of the drama. With the needs of Champions league, domestic leagues and other cups, one can be sure to have a stay match every three days. The English...

Words: 1271

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Introduction Background: American soccer is one of the rising sports that are attracting hundreds of thousands of followers into various stadia across the states. The National Football League has been growing this game to rival other well established competitions like basketball. The magnitude of the fans in growing this sport can't...

Franck, Egon, and Markus Lang. A theoretical evaluation of the influence of money injections on risk taking in soccer clubs. This article offers a comprehensive approach to the impact of money on the progress of sporting activities related to football clubs. Frank et al illustrate the component of incentives in the soccer sector....

Soccer: A Love and a Lesson Soccer is a very popular sport. Millions of individuals play this sport and twice as many individuals are supporters and fans. Watching matches with my dad on TV and also following him to watch live matches between local teams is the earliest memory I have...

Soccer is the most universally diagnosed and popular sport across the globe. Unlike different types of sports such as athletics and basketball, soccer fields are not ample and easily accessible to many. However, it remains the most aggressive game. Different types of players play in soccer matches thereby contributing to failure...

Essay On Football for Students and Children

500+ words essay on football.

Essay On Football- Football is a game that millions of people around the world play and love. It can be called a universal game because every small and big nation plays it.

Moreover, it’s a great relaxer, stress reliever, teacher of discipline and teamwork . Apart from that, it keeps the body and mind fit and healthy. It’s a team game that makes it a more enjoyable game as it teaches people the importance of sportsmanship. Leadership, and unity .

History of Football

The history of football can be traced back to the ancient times of the Greeks. Everyone knows that the Greeks were great sportsmen and have invented many games.

Football happens to one of them. A similar game like football is played in many countries but the latest version of football that we knew originates in England. Likewise, England formulated the first rule of the game. From that day onwards the football has progressed in ways we can’t imagine.

Importance of Football

Football is an important game from the point of view of the spectator as well as the player. This 90 minutes game is full of excitement and thrill.

Moreover, it keeps the player mentally and physically healthy, and disciplined. And this ninety-minute game tests their sportsmanship, patience, and tolerance.

Besides, all this you make new friends and develop your talent. Above all, it’s a global game that promotes peace among countries.

Get the huge list of more than 500 Essay Topics and Ideas

How to Learn Football

Learning any game is not an easy task. It requires dedication and hard work. Besides, all this the sport test your patience and insistence towards it. Moreover, with every new skill that you learn your game also improves. Above all, learning is a never-ending process so to learn football you have to be paying attention to every minute details that you forget to count or missed.

Football in India

If we look at the scenarios of a few years back then we can say that football was not a popular game in except West Bengal. Also, Indians do not take much interest in playing football. Likewise, the All India Football Federation (AIFF) has some limited resources and limited support from the government.

But, now the scenario has completely changed. At this time football matches the level of cricket in the country. Apart from that, the country organizes various football tournaments every year.

Above all, due to the unpopularity of football people do not know that we have under-17 and under-23, as well as a football team.

Football Tournaments

The biggest tournament of Football is the FIFA world cup which occurs every 4 years. Apart from that, there are various other tournaments like UEFA cup, Asian Cup (AFC), African completions (CAF) and many more.

To conclude, we can say that football is very interesting that with every minute takes the viewer’s breath away. Besides, you can’t predict what’s going to happen the next second or minute in football. Apart from all this football keeps the one playing it fit and healthy. Above all, it can be a medium of spreading the message of peace in the world as it is a global game.

{ “@context”: “https://schema.org”, “@type”: “FAQPage”, “mainEntity”: [{ “@type”: “Question”, “name”: “What are soccer and rugby?”, “acceptedAnswer”: { “@type”: “Answer”, “text”: “Soccer is another name of the world-famous game Football. While on the other hand, rugby is an American version of Football in which they carry the rugby ball in their hands.” } }, { “@type”: “Question”, “name”: “Is football a dangerous or safe game?”, “acceptedAnswer”: { “@type”: “Answer”, “text”:”For school students and youngsters it’s a much safer game as compared to professionals. Because professionals can suffer from injuries and can cost them their careers. But overall football is a dangerous game.”} }] }

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FC Barcelona: Achievements and Impact Essay

Introduction.

Barcelona football club was undeniably the best soccer team in the last ten years. The professional football club based in Barcelona, a city in northeastern Spain on the Mediterranean has won numerous domestic, continental, and global titles in the last decade. The club has won 21 titles since the year 2004 (King, 2010). This success has propelled the club to the second position among the world’s richest soccer teams based on revenue and market value. The football club has also managed to increase its fan base in the last ten years, especially on major social networking sites such as Facebook, Twitter, and Google Plus. Barcelona boasts of a highly talented and dynamic team of young players drawn from various parts of the world. However, Spanish players such as Xavi Hernandez, Andres Iniesta, Cesc Fabregas, and Victor Valdes among others dominate the team (Hunter, 2013).

The team also boasts of Lionel Messi who is arguably the best player of the current generation of footballers in the world. Most players who have represented the Barcelona football club in the last decade have managed to receive individual accolades for their efforts with the club and their national soccer teams (King, 2010).

Success and achievements in the last decade

Barcelona is one of the most successful teams in the world in terms of overall achievements since its inception. From the year 2004, the club has won a number of domestic and continental titles, as well as global titles. The domestic honors won by the team in the last decade include six Spanish football league titles, six Spanish Super Cup titles, and two Copa Del Rey titles (Hunter, 2013). Continental honors include three European Champions League titles and the two European Super Cup titles. Barcelona also achieved global success when they won the FIFA Club World Cup in 2009 and 2011. The club has emerged as runners up in most of these competitions several times during the last decade. In the last ten years, Barcelona has had three managers who helped to put it on the global map following the team’s impressive results (King, 2010).

The most successful of the three managers was Pep Guardiolas, who made history for winning numerous titles within a short time. The coach is also hailed for successfully tutoring Lionel Messi, who has dominated football awards all over the world (Hunter, 2013). Pep Guardiolas helped Barcelona to win 14 titles in the period he was the club manager between 2008- 2012. He is famous for the manner in which he developed a successful team around Lionel Messi, Andres Iniesta, and Xavi Hernadez after selling top players at the club such as Deco and Ronaldinho (Hunter, 2013). Under his guidance, Barcelona made history in 2009 for becoming the first team from Spain to win three major titles in one football season (King, 2010). The team also made history in 2009 when they won all the six competitions they participated in during the year. The football club almost repeated the same performance of 2009 in the year 2011when it won five out of six competitions.

Barcelona made great strides during the reign of Pep Guardiolas as the manager. In the year 2010, Barcelona made a mark in the history of world football when three of its academy products emerged as the top three players in the world. Messi, Xavi and Iniesta were shortlisted for the award of the prestigious FIFA Ballon d’OR in 2010 (King, 2010). Most football experts argue that Barcelona’s team of the last one decade easily qualifies as the greatest squad of the current generation of footballers.

Barcelona is among the three teams that have remained in top-flight football in Spain alongside Real Madrid and Athletic Bilbao. The central player for this team in the last decade has been Loniel Messi, who has won the FIFA Ballon d’OR for a record four consecutive years from 2009- 2012 (Hunter, 2013). Apart from Messi, Barcelona has also been represented by a number of football stars such as Ronaldinho, who won the FIFA Balon d’OR in 2005, Deco, Carols Puyol, Samuel Eto’o, Cesc Fabregas, Victor Valdes, and Gerald Pique among others (King, 2010).

Effect of Barcelona players on Spanish team and the 2010 world cup

The FIFA world cup is one of the most-watched sports competitions in the world. Spain is the reigning world champion after they defeated the Netherlands in the final of the last edition held in South Africa in 2010 (Hunter, 2013). There is a lot of influence from the Barcelona football club on the Spanish national team. This team has the highest number of representatives in the national team. The Spanish football team applies the philosophy of short passes in the field, just like Barcelona. The presence of Barcelona players in the national team has popularized this philosophy on the global arena, as many people now consider it their style of play (King, 2010).

Barcelona boosts of not less than ten players every time the team gathers for national duty. During the 2010 FIFA world cup in South Africa, eight players in the Spanish national team came from Barcelona. The top scorer at the tournament was David Villa, who had signed for Barcelona from Valencia before the competition began.

Important members of Barcelona also scored crucial goals for Spain during the tournament that they eventually won. The most notable contribution of a Barcelona player happened during the finals when Andres Iniesta scored the winning goal in extra time to give Spain their maiden world title (Hunter, 2013). Famous Barcelona players who played in the world cup in South Africa and are still playing in the team include Victor Valdes, Gerald Pique, Carlos Puyol, Andres Iniesta, Xavi Hernadez, Sergio Busquests, and Pedro Rodriquez. Two years later, Barcelona players again made a mark in the Spanish national team when they helped their country to defend the European title that they had won in 2008 (Hunter, 2013). Spain will look forward to defending their world title this year when the tournament moves to Brazil in the summer.

Barcelona has undeniably been the best soccer team in the last ten years. The achievements of the team since 2004 have made it a force to reckon with in the history of club football, as they have managed to equal and even surpass records held by other great teams such as Real Madrid, AC Milan, Manchester United, and Bayern Munich among others. Barcelona is expected to produce the highest number of representatives in the Spanish national team during this year’s world cup in Brazil. Some of the players that will be hopping to represent their country in the world cup for the first time include Morata, Christian Tello, and Jodi Alba. Many football clubs will struggle to achieve what Barcelona has managed in the last ten years within a similar or even shorter timeline.

Hunter, G. (2013). Barcelona: The Making of the Greatest Team in the World . New York: Cengage Learning. Web.

King, J. (2010). FC Barcelona: A Tactical Analysis . New Jersey: John Wiley & Sons. Web.

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Essay on Football

Here we have shared the Essay on Football in detail so you can use it in your exam or assignment of 150, 250, 400, 500, or 1000 words.

You can use this Essay on Football in any assignment or project whether you are in school (class 10th or 12th), college, or preparing for answer writing in competitive exams.

Topics covered in this article.

Essay on Football in 150-250 words

Essay on football in 300-400 words, essay on football in 500-1000 words.

Football is a popular sport played and cherished by millions of people around the world. It is a game that brings together people of different backgrounds, cultures, and ages, uniting them in their love for the sport. Football is a thrilling and competitive game that requires skill, teamwork, and strategy.

The objective of the game is simple: to score goals by kicking the ball into the opposing team’s net. It is played on a rectangular field, with two teams of eleven players each. The players maneuver the ball with their feet, heads, or other parts of their bodies, except for their hands. The fast-paced nature of the game keeps players and spectators engaged throughout.

Football fosters discipline, perseverance, and sportsmanship among its players. It promotes physical fitness, coordination, and mental agility. It teaches valuable life lessons such as teamwork, communication, and the importance of fair play.

The excitement of football extends beyond the playing field. Fans passionately support their favorite teams, creating an electric atmosphere in stadiums. International tournaments like the FIFA World Cup bring nations together, igniting a sense of national pride and unity.

In conclusion, football is more than just a game; it is a global phenomenon that transcends boundaries and cultures. It brings people together, promotes physical fitness, and instills valuable qualities in its players. The love for football is universal, and its impact on individuals and communities is undeniable.

Football, also known as soccer, is the world’s most popular sport, played and celebrated by millions of people across the globe. It is a game that captures the hearts and minds of players and fans alike, offering excitement, camaraderie, and a sense of belonging.

Football is played between two teams, with each team consisting of eleven players. The objective of the game is to score goals by maneuvering the ball into the opposing team’s net while defending their own goal. The game is played on a rectangular field, and players use their feet, heads, or other parts of their bodies, except for their hands, to control and pass the ball.

What makes football special is its universal appeal. It transcends borders, cultures, and languages, bringing people together in a shared passion. Whether in a neighborhood park, a local stadium, or on the grand stage of international tournaments, football unites people from different backgrounds, fostering a sense of community and belonging.

Football instills important values and life skills in its players. It promotes teamwork, cooperation, and effective communication. Players learn to trust and rely on their teammates, developing strong bonds that extend beyond the field. The sport also teaches discipline, perseverance, and resilience, as players face challenges, setbacks, and the need for continuous improvement.

Beyond its physical and mental benefits, football has a profound social impact. It has the power to inspire and unite communities. Matches and tournaments bring people together, creating a shared sense of excitement, joy, and pride. Football has the ability to transcend social, cultural, and economic barriers, fostering inclusivity and breaking down stereotypes.

Furthermore, football has the potential to address societal issues and promote positive change. Many football organizations and players use their platforms to advocate for social justice, equality, and peace. Football can be a powerful tool in promoting values of fairness, respect, and diversity.

In conclusion, football is much more than just a game. It is a global phenomenon that has the power to unite people, transcend boundaries, and foster positive change. The sport teaches valuable life lessons, promotes teamwork and discipline, and brings communities together. Football is a universal language that speaks to the hearts of millions, igniting passion, excitement, and a sense of belonging.

Title: The Beautiful Game – Football’s Enduring Impact on Society

Introduction:

Football, also known as soccer, is a sport that has captivated the world for centuries. It is a game that unites people from all walks of life, transcending boundaries of nationality, culture, and language. This essay delves into the rich history, global popularity, and profound impact of football on society, highlighting its ability to inspire, unite, and bring about positive change.

Historical Evolution

Football has a fascinating historical evolution that can be traced back to ancient civilizations. Games involving kicking a ball have been played in various forms throughout history. The modern version of football emerged in England during the 19th century when standardized rules were established, leading to the formation of football clubs and the organization of official matches.

Global Popularity

Football’s popularity has soared over the years, making it the most widely played and watched sport in the world. The FIFA World Cup, held every four years, attracts billions of viewers and creates an atmosphere of excitement and national pride. Club football, with renowned leagues such as the English Premier League, Spanish La Liga, and Italian Serie A, generates fierce loyalty and passionate support from fans worldwide.

The Thrill of the Game

Football’s appeal lies in its simplicity and universal accessibility. All that is needed to play is a ball and an open space. The objective is straightforward: to score goals by maneuvering the ball into the opponent’s net while defending one’s own. The combination of physical prowess, skillful footwork, tactical strategy, and teamwork creates a thrilling spectacle for both players and spectators.

Values and Life Lessons

Football is more than just a game; it teaches valuable values and life lessons. Teamwork and cooperation are fundamental to success on the field, fostering a sense of unity and camaraderie among players. The sport instills discipline, perseverance, and resilience as athletes face challenges, setbacks, and the need for continuous improvement. Fair play, respect for opponents, and adherence to rules are ingrained in the spirit of the game, shaping character and sportsmanship.

Social Impact

Football’s impact extends beyond the boundaries of the field. It has the power to inspire and unite communities, creating a shared sense of identity and belonging. Matches and tournaments bring people together, generating an electric atmosphere of excitement, joy, and collective celebration. Football breaks down social, cultural, and economic barriers, fostering inclusivity and promoting diversity.

Football as a Catalyst for Social Change

Football has emerged as a powerful catalyst for social change, addressing pressing issues and promoting positive transformation. Many football organizations and players use their platform to advocate for social justice, equality, and peace. Initiatives focused on combating racism, gender inequality, poverty, and promoting education have gained momentum, leveraging football’s popularity to create awareness and drive meaningful change.

Economic and Infrastructure Development

Football has a significant economic impact on society, generating revenue through ticket sales, broadcasting rights, sponsorships, and merchandise. It supports job creation, infrastructure development, and tourism. Major sporting events like the FIFA World Cup and UEFA European Championships stimulate economic growth and leave lasting legacies in host countries, improving infrastructure, and boosting the local economy.

Football and Health

Football promotes physical fitness, contributing to a healthier society. Playing football enhances cardiovascular fitness, muscular strength, coordination, and agility. It encourages an active lifestyle and helps combat the growing prevalence of sedentary behavior and related health issues. Football’s accessibility and inclusivity make it an ideal sport for people of all ages and backgrounds to engage in regular physical activity.

Conclusion :

Football’s enduring impact on society is undeniable. Its universal appeal, thrilling gameplay, and ability to bring people together have made it the world’s most beloved sport. Football teaches valuable life lessons, fosters unity, and sportsmanship, and serves as a catalyst for positive change. It inspires individuals and communities, transcending borders, cultures, and languages. The sport’s economic, social, and health benefits are substantial, contributing to the overall well-being of society. As we continue to celebrate the beautiful game of football, let us embrace its values, harness its power, and work towards creating a more inclusive, just, and united world.

Why People Should Play Soccer

The introduction to persuasive essay about soccer.

There are many people in the world who are obsessed with soccer. It seems that it is a result of its popularity around the world and the willingness of people to make money on it. This argument has the right to go on. However, in fact, the reasons lie much deeper. People from the beginning of life on the Earth try to find a good means that can help them to be of the sound mind and body. They tried different things starting from ancient magical rituals to neoplasty. However, in the course of this searching, they understood that the most helpful one is a sport. Soccer has become an integral part of the social and cultural life in many countries and has been considered not only as a branch of sport but also as a good means to improve health. Soccer has a positive influence on the physical and mental state of a person and is a way to make the players as fit as a fiddle.

Thereby, it is obvious that the first functions of this game were to improve the physical and mental states of health. However, from that period soccer has undergone different transformations before it has become soccer that is now popular around the world. That is why it is necessary to return to the initial function of soccer and consider it not only as a good way to make money but a good way to physical and mental well-being.

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Soccer Has a Great Influence on the Health of Children

Soccer has a great influence on the health of children. Narula Shelley states that with the help of soccer young people can improve their health and gain such benefits as “enhanced cardio-respiratory fitness, increased muscular strength and endurance, and favorable cholesterol and other profiles, which further prevent heart diseases” (Shelley n.p.). Jordan Rubin supports this position and adds: “Soccer demands lung-bursting running that’s great for kids’ cardiovascular system and burning up calories” (Rubin 111). Narula Shelley presents a couple of examples to prove it.

One of them is the results of research published by the American College of Sports Medicine. “Researchers recruited a large group of prepubertal Spanish boys and followed them for more than three years. Those who regularly played soccer for at least three hours a week were compared to those who only engaged in regular in-school physical education of two, 45-minute sessions per week. … The results of the study showed that the soccer group not only increased whole-body bone mineral density but also had higher regional measures in areas such as the lumbar spine (13 percent) and the femoral neck in the hip (10 percent). These increases correlated statistically to increases in other fitness factors such as anaerobic capacity and force generated during jumping” (Shelley n.p.).

Thereby, it is obvious that soccer has a positive effect on children’s health. However, soccer improves adults’ state of health too. It requires from players a great physical training to become an elite soccer player. It is necessary to follow biological requirements (nutritional requirements, training activities and recovery strategies), the behavioral and social requirements (“skills, processes, and mechanisms underpinning the superior ability of elite players to “read the game”), performance analysis, biomechanics and coaching requirements (the methods available for undertaking match and motion analysis) and so on (Williams 10). All these requirements altogether contribute to the physical well-being of players.

Soccer Improves the Mental Health

Soccer improves not only the physical state of health but also mental. Firstly, it contributes to the socialization of a person. Narula Shelley admits that soccer is helpful for the improvement of social position because it is a great way to socialize with friends through working as a team and doing all to gain a victory (Shelley n.p.). Secondly, soccer is a source that teaches how to cooperate with other people in order to achieve a common goal. Playing soccer, a person sacrifices himself and his interests for the benefit of the team. It builds thankfulness relationships between the players. Thankfulness creates a positive team relationship. When teammates feel one appreciates their efforts, they will work with enthusiasm to help this one, and it will contribute to the harmony in the team.

Moreover, soccer teaches people to become selfless because to achieve a common goal, players have to become one entire organism (Jones and Sanders). All these can be applied in real-life practice. Thereby soccer teaches how to live properly. Thirdly, soccer brings many benefits to the person who plays it. Those benefits are an improvement in academic performance, coordination, discipline, concentration, and self-regulation. In addition, it helps to reduce anxiety and reject all the prejudices about the weakness of women as girls and boys play soccer together.

“Happy Like Soccer” by Maribeth Boelts

However, without real examples, all the benefits of soccer seem very abstract. As almost all literary works are based on real-life experience, a good way to understand whether soccer can improve physical and mental health is to find the answer in literary books. “Happy Like Soccer” is a children’s picture book written by Maribeth Boelts. It has gained unparalleled popularity among young readers throughout the world.

It deals with the story of a little girl, Sierra, who likes to play soccer. This game is a mix of joy and sadness for her. It is a joy because nothing brings Sierra more pleasure than playing soccer with her new team where she is one of the best players. It is sad because Sierra’s aunt is not able to attend her games. However, one day she manages to do it. It makes the girl the happiest person in the world. Returning to the theme of soccer, from this book, it is clear that soccer is an opportunity for children to start new friendships, overcome loneliness, hardships of a poor family and disappointment, gain the courage to change a bad event into a positive one, and understand the importance of the nearest and dearest (Boelts).

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“Breathing Soccer” by Debbie Spring

All these are proved by the example described in another book “Breathing Soccer” by Debbie Spring. This book deals with the story of a young girl Lisa who is forbidden to play soccer because of her illness. Liza has asthma and continuation of the soccer career may have lethal consequences for her. However, girl risks because she cannot imagine her life without soccer. To the surprise of all, she recovers and brings her dream into life. From this book, it is clear that soccer brings benefits to health. Moreover, it gives hope and power to stand against all the difficulties (Spring).

The Summary to Persuasive Essay about Soccer

To sum up, soccer is a play with a long history. Firstly soccer was played in China, and it reflected the social and spiritual beliefs of this country. Now it is deeply ingrained in popular culture as it helps to improve both the physical and mental state of a person. The benefits of soccer are not only abstract things that no one can achieve, but they are proved by the real examples described in literary works such as “Happy like Soccer” and “Breathing Soccer”.

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How to write an essay introduction | 4 steps & examples

How to Write an Essay Introduction | 4 Steps & Examples

Published on February 4, 2019 by Shona McCombes . Revised on July 23, 2023.

A good introduction paragraph is an essential part of any academic essay . It sets up your argument and tells the reader what to expect.

The main goals of an introduction are to:

Catch your reader’s attention.
Give background on your topic.
Present your thesis statement —the central point of your essay.

This introduction example is taken from our interactive essay example on the history of Braille.

The invention of Braille was a major turning point in the history of disability. The writing system of raised dots used by visually impaired people was developed by Louis Braille in nineteenth-century France. In a society that did not value disabled people in general, blindness was particularly stigmatized, and lack of access to reading and writing was a significant barrier to social participation. The idea of tactile reading was not entirely new, but existing methods based on sighted systems were difficult to learn and use. As the first writing system designed for blind people’s needs, Braille was a groundbreaking new accessibility tool. It not only provided practical benefits, but also helped change the cultural status of blindness. This essay begins by discussing the situation of blind people in nineteenth-century Europe. It then describes the invention of Braille and the gradual process of its acceptance within blind education. Subsequently, it explores the wide-ranging effects of this invention on blind people’s social and cultural lives.

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Step 1: hook your reader, step 2: give background information, step 3: present your thesis statement, step 4: map your essay’s structure, step 5: check and revise, more examples of essay introductions, other interesting articles, frequently asked questions about the essay introduction.

Your first sentence sets the tone for the whole essay, so spend some time on writing an effective hook.

Avoid long, dense sentences—start with something clear, concise and catchy that will spark your reader’s curiosity.

The hook should lead the reader into your essay, giving a sense of the topic you’re writing about and why it’s interesting. Avoid overly broad claims or plain statements of fact.

Examples: Writing a good hook

Take a look at these examples of weak hooks and learn how to improve them.

Braille was an extremely important invention.
The invention of Braille was a major turning point in the history of disability.

The first sentence is a dry fact; the second sentence is more interesting, making a bold claim about exactly why the topic is important.

The internet is defined as “a global computer network providing a variety of information and communication facilities.”
The spread of the internet has had a world-changing effect, not least on the world of education.

Avoid using a dictionary definition as your hook, especially if it’s an obvious term that everyone knows. The improved example here is still broad, but it gives us a much clearer sense of what the essay will be about.

Mary Shelley’s Frankenstein is a famous book from the nineteenth century.
Mary Shelley’s Frankenstein is often read as a crude cautionary tale about the dangers of scientific advancement.

Instead of just stating a fact that the reader already knows, the improved hook here tells us about the mainstream interpretation of the book, implying that this essay will offer a different interpretation.

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Next, give your reader the context they need to understand your topic and argument. Depending on the subject of your essay, this might include:

Historical, geographical, or social context
An outline of the debate you’re addressing
A summary of relevant theories or research about the topic
Definitions of key terms

The information here should be broad but clearly focused and relevant to your argument. Don’t give too much detail—you can mention points that you will return to later, but save your evidence and interpretation for the main body of the essay.

How much space you need for background depends on your topic and the scope of your essay. In our Braille example, we take a few sentences to introduce the topic and sketch the social context that the essay will address:

Now it’s time to narrow your focus and show exactly what you want to say about the topic. This is your thesis statement —a sentence or two that sums up your overall argument.

This is the most important part of your introduction. A good thesis isn’t just a statement of fact, but a claim that requires evidence and explanation.

The goal is to clearly convey your own position in a debate or your central point about a topic.

Particularly in longer essays, it’s helpful to end the introduction by signposting what will be covered in each part. Keep it concise and give your reader a clear sense of the direction your argument will take.

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As you research and write, your argument might change focus or direction as you learn more.

For this reason, it’s often a good idea to wait until later in the writing process before you write the introduction paragraph—it can even be the very last thing you write.

When you’ve finished writing the essay body and conclusion , you should return to the introduction and check that it matches the content of the essay.

It’s especially important to make sure your thesis statement accurately represents what you do in the essay. If your argument has gone in a different direction than planned, tweak your thesis statement to match what you actually say.

To polish your writing, you can use something like a paraphrasing tool .

You can use the checklist below to make sure your introduction does everything it’s supposed to.

Checklist: Essay introduction

My first sentence is engaging and relevant.

I have introduced the topic with necessary background information.

I have defined any important terms.

My thesis statement clearly presents my main point or argument.

Everything in the introduction is relevant to the main body of the essay.

You have a strong introduction - now make sure the rest of your essay is just as good.

Argumentative
Literary analysis

This introduction to an argumentative essay sets up the debate about the internet and education, and then clearly states the position the essay will argue for.

The spread of the internet has had a world-changing effect, not least on the world of education. The use of the internet in academic contexts is on the rise, and its role in learning is hotly debated. For many teachers who did not grow up with this technology, its effects seem alarming and potentially harmful. This concern, while understandable, is misguided. The negatives of internet use are outweighed by its critical benefits for students and educators—as a uniquely comprehensive and accessible information source; a means of exposure to and engagement with different perspectives; and a highly flexible learning environment.

This introduction to a short expository essay leads into the topic (the invention of the printing press) and states the main point the essay will explain (the effect of this invention on European society).

In many ways, the invention of the printing press marked the end of the Middle Ages. The medieval period in Europe is often remembered as a time of intellectual and political stagnation. Prior to the Renaissance, the average person had very limited access to books and was unlikely to be literate. The invention of the printing press in the 15th century allowed for much less restricted circulation of information in Europe, paving the way for the Reformation.

This introduction to a literary analysis essay , about Mary Shelley’s Frankenstein , starts by describing a simplistic popular view of the story, and then states how the author will give a more complex analysis of the text’s literary devices.

Mary Shelley’s Frankenstein is often read as a crude cautionary tale. Arguably the first science fiction novel, its plot can be read as a warning about the dangers of scientific advancement unrestrained by ethical considerations. In this reading, and in popular culture representations of the character as a “mad scientist”, Victor Frankenstein represents the callous, arrogant ambition of modern science. However, far from providing a stable image of the character, Shelley uses shifting narrative perspectives to gradually transform our impression of Frankenstein, portraying him in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as.

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Your essay introduction should include three main things, in this order:

An opening hook to catch the reader’s attention.
Relevant background information that the reader needs to know.
A thesis statement that presents your main point or argument.

The length of each part depends on the length and complexity of your essay .

The “hook” is the first sentence of your essay introduction . It should lead the reader into your essay, giving a sense of why it’s interesting.

To write a good hook, avoid overly broad statements or long, dense sentences. Try to start with something clear, concise and catchy that will spark your reader’s curiosity.

A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

It gives your writing direction and focus.
It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

The structure of an essay is divided into an introduction that presents your topic and thesis statement , a body containing your in-depth analysis and arguments, and a conclusion wrapping up your ideas.

The structure of the body is flexible, but you should always spend some time thinking about how you can organize your essay to best serve your ideas.

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by Sydney Keller

Camilo Campos-Hurtado in court (Photo: FOX 17 News)

FRANKLIN, Tenn. (WZTV) — A federal grand jury has indicted the man accused of raping and drugging young boys on more child sex crimes Wednesday.

Camilo Campos-Hurtado, 63, is also accused of recording the attacks that span decades.

The former Franklin, Tennessee soccer coach has been indicted for the following charges:

Sexual exploitation of a minor (4 counts)
Receiving visual depictions of minors engaged in sexually explicit conduct
Using or possessing fraudulent immigration documents
Possessing an identification document or authentication feature which was stolen or produced without lawful authority

Campos-Hurtado was also indicted by a Williamson County jury on 17 rape charges and other sex crimes earlier this month.

These new indictments come after multiple search warrants found child pornography and fraudulent immigrant documents, according to the United States Attorney's Office.

The indictment was returned Wednesday by a federal grand jury. If convicted, Campos-Hurtado faces a mandatory minimum sentence of fifteen years and a maximum sentence of life in federal prison.

If you believe that you or someone you know may be a victim of, or have any information about, the conduct alleged in the indictment, please contact the Franklin Police Department at (615) 550-6829 or the Department of Homeland Security at (866) 347-2423.

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Published: 19 March 2024

TacticAI: an AI assistant for football tactics

Zhe Wang ORCID: orcid.org/0000-0002-0748-5376 1 na1 ,
Petar Veličković ORCID: orcid.org/0000-0002-2820-4692 1 na1 ,
Daniel Hennes ORCID: orcid.org/0000-0002-3646-5286 1 na1 ,
Nenad Tomašev ORCID: orcid.org/0000-0003-1624-0220 1 ,
Laurel Prince 1 ,
Michael Kaisers 1 ,
Yoram Bachrach 1 ,
Romuald Elie 1 ,
Li Kevin Wenliang 1 ,
Federico Piccinini 1 ,
William Spearman 2 ,
Ian Graham 3 ,
Jerome Connor 1 ,
Yi Yang 1 ,
Adrià Recasens 1 ,
Mina Khan 1 ,
Nathalie Beauguerlange 1 ,
Pablo Sprechmann 1 ,
Pol Moreno 1 ,
Nicolas Heess ORCID: orcid.org/0000-0001-7876-9256 1 ,
Michael Bowling ORCID: orcid.org/0000-0003-2960-8418 4 ,
Demis Hassabis 1 &
Karl Tuyls ORCID: orcid.org/0000-0001-7929-1944 5

Nature Communications volume 15 , Article number: 1906 ( 2024 ) Cite this article

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Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose TacticAI, an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. TacticAI incorporates both a predictive and a generative component, allowing the coaches to effectively sample and explore alternative player setups for each corner kick routine and to select those with the highest predicted likelihood of success. We validate TacticAI on a number of relevant benchmark tasks: predicting receivers and shot attempts and recommending player position adjustments. The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC. We show that TacticAI’s model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90% of the time, and that TacticAI offers an effective corner kick retrieval system. TacticAI achieves these results despite the limited availability of gold-standard data, achieving data efficiency through geometric deep learning.

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Introduction

Association football, or simply football or soccer, is a widely popular and highly professionalised sport, in which two teams compete to score goals against each other. As each football team comprises up to 11 active players at all times and takes place on a very large pitch (also known as a soccer field), scoring goals tends to require a significant degree of strategic team-play. Under the rules codified in the Laws of the Game 1 , this competition has nurtured an evolution of nuanced strategies and tactics, culminating in modern professional football leagues. In today’s play, data-driven insights are a key driver in determining the optimal player setups for each game and developing counter-tactics to maximise the chances of success 2 .

When competing at the highest level the margins are incredibly tight, and it is increasingly important to be able to capitalise on any opportunity for creating an advantage on the pitch. To that end, top-tier clubs employ diverse teams of coaches, analysts and experts, tasked with studying and devising (counter-)tactics before each game. Several recent methods attempt to improve tactical coaching and player decision-making through artificial intelligence (AI) tools, using a wide variety of data types from videos to tracking sensors and applying diverse algorithms ranging from simple logistic regression to elaborate neural network architectures. Such methods have been employed to help predict shot events from videos 3 , forecast off-screen movement from spatio-temporal data 4 , determine whether a match is in-play or interrupted 5 , or identify player actions 6 .

The execution of agreed-upon plans by players on the pitch is highly dynamic and imperfect, depending on numerous factors including player fitness and fatigue, variations in player movement and positioning, weather, the state of the pitch, and the reaction of the opposing team. In contrast, set pieces provide an opportunity to exert more control on the outcome, as the brief interruption in play allows the players to reposition according to one of the practiced and pre-agreed patterns, and make a deliberate attempt towards the goal. Examples of such set pieces include free kicks, corner kicks, goal kicks, throw-ins, and penalties 2 .

Among set pieces, corner kicks are of particular importance, as an improvement in corner kick execution may substantially modify game outcomes, and they lend themselves to principled, tactical and detailed analysis. This is because corner kicks tend to occur frequently in football matches (with ~10 corners on average taking place in each match 7 ), they are taken from a fixed, rigid position, and they offer an immediate opportunity for scoring a goal—no other set piece simultaneously satisfies all of the above. In practice, corner kick routines are determined well ahead of each match, taking into account the strengths and weaknesses of the opposing team and their typical tactical deployment. It is for this reason that we focus on corner kick analysis in particular, and propose TacticAI, an AI football assistant for supporting the human expert with set piece analysis, and the development and improvement of corner kick routines.

TacticAI is rooted in learning efficient representations of corner kick tactics from raw, spatio-temporal player tracking data. It makes efficient use of this data by representing each corner kick situation as a graph—a natural representation for modelling relationships between players (Fig. 1 A, Table 2 ), and these player relationships may be of higher importance than the absolute distances between them on the pitch 8 . Such a graph input is a natural candidate for graph machine learning models 9 , which we employ within TacticAI to obtain high-dimensional latent player representations. In the Supplementary Discussion section, we carefully contrast TacticAI against prior art in the area.

A How corner kick situations are converted to a graph representation. Each player is treated as a node in a graph, with node, edge and graph features extracted as detailed in the main text. Then, a graph neural network operates over this graph by performing message passing; each node’s representation is updated using the messages sent to it from its neighbouring nodes. B How TacticAI processes a given corner kick. To ensure that TacticAI’s answers are robust in the face of horizontal or vertical reflections, all possible combinations of reflections are applied to the input corner, and these four views are then fed to the core TacticAI model, where they are able to interact with each other to compute the final player representations—each internal blue arrow corresponds to a single message passing layer from ( A ). Once player representations are computed, they can be used to predict the corner’s receiver, whether a shot has been taken, as well as assistive adjustments to player positions and velocities, which increase or decrease the probability of a shot being taken.

Uniquely, TacticAI takes advantage of geometric deep learning 10 to explicitly produce player representations that respect several symmetries of the football pitch (Fig. 1 B). As an illustrative example, we can usually safely assume that under a horizontal or vertical reflection of the pitch state, the game situation is equivalent. Geometric deep learning ensures that TacticAI’s player representations will be identically computed under such reflections, such that this symmetry does not have to be learnt from data. This proves to be a valuable addition, as high-quality tracking data is often limited—with only a few hundred matches played each year in every league. We provide an in-depth overview of how we employ geometric deep learning in TacticAI in the “Methods” section.

From these representations, TacticAI is then able to answer various predictive questions about the outcomes of a corner—for example, which player is most likely to make first contact with the ball, or whether a shot will take place. TacticAI can also be used as a retrieval system—for mining similar corner kick situations based on the similarity of player representations—and a generative recommendation system, suggesting adjustments to player positions and velocities to maximise or minimise the estimated shot probability. Through several experiments within a case study with domain expert coaches and analysts from Liverpool FC, the results of which we present in the next section, we obtain clear statistical evidence that TacticAI readily provides useful, realistic and accurate tactical suggestions.

To demonstrate the diverse qualities of our approach, we design TacticAI with three distinct predictive and generative components: receiver prediction, shot prediction, and tactic recommendation through guided generation, which also correspond to the benchmark tasks for quantitatively evaluating TacticAI. In addition to providing accurate quantitative insights for corner kick analysis with its predictive components, the interplay between TacticAI’s predictive and generative components allows coaches to sample alternative player setups for each routine of interest, and directly evaluate the possible outcomes of such alternatives.

We will first describe our quantitative analysis, which demonstrates that TacticAI’s predictive components are accurate at predicting corner kick receivers and shot situations on held-out test corners and that the proposed player adjustments do not strongly deviate from ground-truth situations. However, such an analysis only gives an indirect insight into how useful TacticAI would be once deployed. We tackle this question of utility head-on and conduct a comprehensive case study in collaboration with our partners at Liverpool FC—where we directly ask human expert raters to judge the utility of TacticAI’s predictions and player adjustments. The following sections expand on the specific results and analysis we have performed.

In what follows, we will describe TacticAI’s components at a minimal level necessary to understand our evaluation. We defer detailed descriptions of TacticAI’s components to the “Methods” section. Note that, all our error bars reported in this research are standard deviations.

Benchmarking TacticAI

We evaluate the three components of TacticAI on a relevant benchmark dataset of corner kicks. Our dataset consists of 7176 corner kicks from the 2020 to 2021 Premier League seasons, which we randomly shuffle and split into a training (80%) and a test set (20%). As previously mentioned, TacticAI operates on graphs. Accordingly, we represent each corner kick situation as a graph, where each node corresponds to a player. The features associated with each node encode the movements (velocities and positions) and simple profiles (heights and weights) of on-pitch players at the timestamp when the corresponding corner kick was being taken by the attacking kicker (see the “Methods” section), and no information of ball movement was encoded. The graphs are fully connected; that is, for every pair of players, we will include the edge connecting them in the graph. Each of these edges encodes a binary feature, indicating whether the two players are on opposing teams or not. For each task, we generated the relevant dataset of node/edge/graph features and corresponding labels (Tables 1 and 2 , see the “Methods” section). The components were then trained separately with their corresponding corner kick graphs. In particular, we only employ a minimal set of features to construct the corner kick graphs, without encoding the movements of the ball nor explicitly encoding the distances between players into the graphs. We used a consistent training-test split for all benchmark tasks, as this made it possible to benchmark not only the individual components but also their interactions.

Accurate receiver and shot prediction through geometric deep learning

One of TacticAI’s key predictive models forecasts the receiver out of the 22 on-pitch players. The receiver is defined as the first player touching the ball after the corner is taken. In our evaluation, all methods used the same set of features (see the “Receiver prediction” entry in Table 1 and the “Methods” section). We leveraged the receiver prediction task to benchmark several different TacticAI base models. Our best-performing model—achieving 0.782 ± 0.039 in top-3 test accuracy after 50,000 training steps—was a deep graph attention network 11 , 12 , leveraging geometric deep learning 10 through the use of D 2 group convolutions 13 . We supplement this result with a detailed ablation study, verifying that both our choice of base architecture and group convolution yielded significant improvements in the receiver prediction task (Supplementary Table 2 , see the subsection “Ablation study” in the “Methods” section). Considering that corner kick receiver prediction is a highly challenging task with many factors that are unseen by our model—including fatigue and fitness levels, and actual ball trajectory—we consider TacticAI’s top-3 accuracy to reflect a high level of predictive power, and keep the base TacticAI architecture fixed for subsequent studies. In addition to this quantitative evaluation with the evaluation dataset, we also evaluate the performance of TacticAI’s receiver prediction component in a case study with human raters. Please see the “Case study with expert raters” section for more details.

For shot prediction, we observe that reusing the base TacticAI architecture to directly predict shot events—i.e., directly modelling the probability \({\mathbb{P}}(\,{{\mbox{shot}}}| {{\mbox{corner}}}\,)\) —proved challenging, only yielding a test F 1 score of 0.52 ± 0.03, for a GATv2 base model. Note that here we use the F 1 score—the harmonic mean of precision and recall—as it is commonly used in binary classification problems over imbalanced datasets, such as shot prediction. However, given that we already have a potent receiver predictor, we decided to use its output to give us additional insight into whether or not a shot had been taken. Hence, we opted to decompose the probability of taking a shot as

where \({\mathbb{P}}(\,{{\mbox{receiver}}}| {{\mbox{corner}}}\,)\) are the probabilities computed by TacticAI’s receiver prediction system, and \({\mathbb{P}}(\,{{\mbox{shot}}}| {{\mbox{receiver}}},{{\mbox{corner}}}\,)\) models the conditional shot probability after a specific player makes first contact with the ball. This was implemented through providing an additional global feature to indicate the receiver in the corresponding corner kick (Table 1 ) while the architecture otherwise remained the same as that of receiver prediction (Supplementary Fig. 2 , see the “Methods” section). At training time, we feed the ground-truth receiver as input to the model—at inference time, we attempt every possible receiver, weighing their contributions using the probabilities given by TacticAI’s receiver predictor, as per Eq. ( 1 ). This two-phased approach yielded a final test F 1 score of 0.68 ± 0.04 for shot prediction, which encodes significantly more signal than the unconditional shot predictor, especially considering the many unobservables associated with predicting shot events. Just as for receiver prediction, this performance can be further improved using geometric deep learning; a conditional GATv2 shot predictor with D 2 group convolutions achieves an F 1 score of 0.71 ± 0.01.

Moreover, we also observe that, even just through predicting the receivers, without explicitly classifying any other salient features of corners, TacticAI learned generalisable representations of the data. Specifically, team setups with similar tactical patterns tend to cluster together in TacticAI’s latent space (Fig. 2 ). However, no clear clusters are observed in the raw input space (Supplementary Fig. 1 ). This indicates that TacticAI can be leveraged as a useful corner kick retrieval system, and we will present our evaluation of this hypothesis in the “Case study with expert raters” section.

We visualise the latent representations of attacking and defending teams in 1024 corner kicks using t -SNE. A latent team embedding in one corner kick sample is the mean of the latent player representations on the same attacking ( A – C ) or defending ( D ) team. Given the reference corner kick sample ( A ), we retrieve another corner kick sample ( B ) with respect to the closest distance of their representations in the latent space. We observe that ( A ) and ( B ) are both out-swing corner kicks and share similar patterns of their attacking tactics, which are highlighted with rectangles having the same colours, although they bear differences with respect to the absolute positions and velocities of the players. All the while, the latent representation of an in-swing attack ( C ) is distant from both ( A ) and ( B ) in the latent space. The red arrows are only used to demonstrate the difference between in- and out-swing corner kicks, not the actual ball trajectories.

Lastly, it is worth emphasising that the utility of the shot predictor likely does not come from forecasting whether a shot event will occur—a challenging problem with many imponderables—but from analysing the difference in predicted shot probability across multiple corners. Indeed, in the following section, we will show how TacticAI’s generative tactic refinements can directly influence the predicted shot probabilities, which will then corresponds to highly favourable evaluation by our expert raters in the “Case study with expert raters” section.

Controlled tactic refinement using class-conditional generative models

Equipped with components that are able to potently relate corner kicks with their various outcomes (e.g. receivers and shot events), we can explore the use of TacticAI to suggest adjustments of tactics, in order to amplify or reduce the likelihood of certain outcomes.

Specifically, we aim to produce adjustments to the movements of players on one of the two teams, including their positions and velocities, which would maximise or minimise the probability of a shot event, conditioned on the initial corner setup, consisting of the movements of players on both teams and their heights and weights. In particular, although in real-world scenarios both teams may react simultaneously to the movements of each other, in our study, we focus on moderate adjustments to player movements, which help to detect players that are not responding to a tactic properly. Due to this reason, we simplify the process of tactic refinement through generating the adjustments for only one team while keeping the other fixed. The way we train a model for this task is through an auto-encoding objective: we feed the ground-truth shot outcome (a binary indicator) as an additional graph-level feature to TacticAI’s model (Table 1 ), and then have it learn to reconstruct a probability distribution of the input player coordinates (Fig. 1 B, also see the “Methods” section). As a consequence, our tactic adjustment system does not depend on the previously discussed shot predictor—although we can use the shot predictor to evaluate whether the adjustments make a measurable difference in shot probability.

This autoencoder-based generative model is an individual component that separates from TacticAI’s predictive systems. All three systems share the encoder architecture (without sharing parameters), but use different decoders (see the “Methods” section). At inference time, we can instead feed in a desired shot outcome for the given corner setup, and then sample new positions and velocities for players on one team using this probability distribution. This setup, in principle, allows for flexible downstream use, as human coaches can optimise corner kick setups through generating adjustments conditioned on the specific outcomes of their interest—e.g., increasing shot probability for the attacking team, decreasing it for the defending team (Fig. 3 ) or amplifying the chance that a particular striker receives the ball.

TacticAI makes it possible for human coaches to redesign corner kick tactics in ways that help maximise the probability of a positive outcome for either the attacking or the defending team by identifying key players, as well as by providing temporally coordinated tactic recommendations that take all players into consideration. As demonstrated in the present example ( A ), for a corner kick in which there was a shot attempt in reality ( B ), TacticAI can generate a tactically-adjusted setting in which the shot probability has been reduced, by adjusting the positioning of the defenders ( D ). The suggested defender positions result in reduced receiver probability for attacking players 2–5 (see bottom row), while the receiver probability of Attacker 1, who is distant from the goalpost, has been increased ( C ). The model is capable of generating multiple such scenarios. Coaches can inspect the different options visually and additionally consult TacticAI’s quantitative analysis of the presented tactics.

We first evaluate the generated adjustments quantitatively, by verifying that they are indistinguishable from the original corner kick distribution using a classifier. To do this, we synthesised a dataset consisting of 200 corner kick samples and their corresponding conditionally generated adjustments. Specifically, for corners without a shot event, we generated adjustments for the attacking team by setting the shot event feature to 1, and vice-versa for the defending team when a shot event did happen. We found that the real and generated samples were not distinguishable by an MLP classifier, with an F 1 score of 0.53 ± 0.05, indicating random chance level accuracy. This result indicates that the adjustments produced by TacticAI are likely similar enough to real corner kicks that the MLP is unable to tell them apart. Note that, in spite of this similarity, TacticAI recommends player-level adjustments that are not negligible—in the following section we will illustrate several salient examples of this. To more realistically validate the practical indistinguishability of TacticAI’s adjustments from realistic corners, we also evaluated the realism of the adjustments in a case study with human experts, which we will present in the following section.

In addition, we leveraged our TacticAI shot predictor to estimate whether the proposed adjustments were effective. We did this by analysing 100 corner kick samples in which threatening shots occurred, and then, for each sample, generated one defensive refinement through setting the shot event feature to 0. We observed that the average shot probability significantly decreased, from 0.75 ± 0.14 for ground-truth corners to 0.69 ± 0.16 for adjustments ( z = 2.62, p < 0.001). This observation was consistent when testing for attacking team refinements (shot probability increased from 0.18 ± 0.16 to 0.31 ± 0.26 ( z = −4.46, p < 0.001)). Moving beyond this result, we also asked human raters to assess the utility of TacticAI’s proposed adjustments within our case study, which we detail next.

Case study with expert raters

Although quantitative evaluation with well-defined benchmark datasets was critical for the technical development of TacticAI, the ultimate test of TacticAI as a football tactic assistant is its practical downstream utility being recognised by professionals in the industry. To this end, we evaluated TacticAI through a case study with our partners at Liverpool FC (LFC). Specifically, we invited a group of five football experts: three data scientists, one video analyst, and one coaching assistant. Each of them completed four tasks in the case study, which evaluated the utility of TacticAI’s components from several perspectives; these include (1) the realism of TacticAI’s generated adjustments, (2) the plausibility of TacticAI’s receiver predictions, (3) effectiveness of TacticAI’s embeddings for retrieving similar corners, and (4) usefulness of TacticAI’s recommended adjustments. We provide an overview of our study’s results here and refer the interested reader to Supplementary Figs. 3 – 5 and the Supplementary Methods for additional details.

We first simultaneously evaluated the realism of the adjusted corner kicks generated by TacticAI, and the plausibility of its receiver predictions. Going through a collection of 50 corner kick samples, we first asked the raters to classify whether a given sample was real or generated by TacticAI, and then they were asked to identify the most likely receivers in the corner kick sample (Supplementary Fig. 3 ).

On the task of classifying real and generated samples, first, we found that the raters’ average F 1 score of classifying the real vs. generated samples was only 0.60 ± 0.04, with individual F 1 scores ( \({F}_{1}^{A}=0.54,{F}_{1}^{B}=0.64,{F}_{1}^{C}=0.65,{F}_{1}^{D}=0.62,{F}_{1}^{E}=0.56\) ), indicating that the raters were, in many situations, unable to distinguish TacticAI’s adjustments from real corners.

The previous evaluation focused on analysing realism detection performance across raters. We also conduct a study that analyses realism detection across samples. Specifically, we assigned ratings for each sample—assigning +1 to a sample if it was identified as real by a human rater, and 0 otherwise—and computed the average rating for each sample across the five raters. Importantly, by studying the distribution of ratings, we found that there was no significant difference between the average ratings assigned to real and generated corners ( z = −0.34, p > 0.05) (Fig. 4 A). Hence, the real and generated samples were assigned statistically indistinguishable average ratings by human raters.

In task 1, we tested the statistical difference between the real corner kick samples and the synthetic ones generated by TacticAI from two aspects: ( A.1 ) the distributions of their assigned ratings, and ( A.2 ) the corresponding histograms of the rating values. Analogously, in task 2 (receiver prediction), ( B.1 ) we track the distributions of the top-3 accuracy of receiver prediction using those samples, and ( B.2 ) the corresponding histogram of the mean rating per sample. No statistical difference in the mean was observed in either cases (( A.1 ) ( z = −0.34, p > 0.05), and ( B.1 ) ( z = 0.97, p > 0.05)). Additionally, we observed a statistically significant difference between the ratings of different raters on receiver prediction, with three clear clusters emerging ( C ). Specifically, Raters A and E had similar ratings ( z = 0.66, p > 0.05), and Raters B and D also rated in similar ways ( z = −1.84, p > 0.05), while Rater C responded differently from all other raters. This suggests a good level of variety of the human raters with respect to their perceptions of corner kicks. In task 3—identifying similar corners retrieved in terms of salient strategic setups—there were no significant differences among the distributions of the ratings by different raters ( D ), suggesting a high level of agreement on the usefulness of TacticAI’s capability of retrieving similar corners ( F 1,4 = 1.01, p > 0.1). Finally, in task 4, we compared the ratings of TacticAI’s strategic refinements across the human raters ( E ) and found that the raters also agreed on the general effectiveness of the refinements recommended by TacticAI ( F 1,4 = 0.45, p > 0.05). Note that the violin plots used in B.1 and C – E model a continuous probability distribution and hence assign nonzero probabilities to values outside of the allowed ranges. We only label y -axis ticks for the possible set of ratings.

For the task of identifying receivers, we rated TacticAI’s predictions with respect to a rater as +1 if at least one of the receivers identified by the rater appeared in TacticAI’s top-3 predictions, and 0 otherwise. The average top-3 accuracy among the human raters was 0.79 ± 0.18; specifically, 0.81 ± 0.17 for the real samples, and 0.77 ± 0.21 for the generated ones. These scores closely line up with the accuracy of TacticAI in predicting receivers for held-out test corners, validating our quantitative study. Further, after averaging the ratings for receiver prediction sample-wise, we found no statistically significant difference between the average ratings of predicting receivers over the real and generated samples ( z = 0.97, p > 0.05) (Fig. 4 B). This indicates that TacticAI was equally performant in predicting the receivers of real corners and TacticAI-generated adjustments, and hence may be leveraged for this purpose even in simulated scenarios.

There is a notably high variance in the average receiver prediction rating of TacticAI. We hypothesise that this is due to the fact that different raters may choose to focus on different salient features when evaluating the likely receivers (or even the amount of likely receivers). We set out to validate this hypothesis by testing the pair-wise similarity of the predictions by the human raters through running a one-away analysis of variance (ANOVA), followed by a Tukey test. We found that the distributions of the five raters’ predictions were significantly different ( F 1,4 = 14.46, p < 0.001) forming three clusters (Fig. 4 C). This result indicates that different human raters—as suggested by their various titles at LFC—may often use very different leads when suggesting plausible receivers. The fact that TacticAI manages to retain a high top-3 accuracy in such a setting suggests that it was able to capture the salient patterns of corner kick strategies, which broadly align with human raters’ preferences. We will further test this hypothesis in the third task—identifying similar corners.

For the third task, we asked the human raters to judge 50 pairs of corners for their similarity. Each pair consisted of a reference corner and a retrieved corner, where the retrieved corner was chosen either as the nearest-neighbour of the reference in terms of their TacticAI latent space representations, or—as a feature-level heuristic—the cosine similarities of their raw features (Supplementary Fig. 4 ) in our corner kick dataset. We score the raters’ judgement of a pair as +1 if they considered the corners presented in the case to be usefully similar, otherwise, the pair is scored with 0. We first computed, for each rater, the recall with which they have judged a baseline- or TacticAI-retrieved pair as usefully similar—see description of Task 3 in the Supplementary Methods . For TacticAI retrievals, the average recall across all raters was 0.59 ± 0.09, and for the baseline system, the recall was 0.36 ± 0.10. Secondly, we assess the statistical difference between the results of the two methods by averaging the ratings for each reference–retrieval pair, finding that the average rating of TacticAI retrievals is significantly higher than the average rating of baseline method retrievals ( z = 2.34, p < 0.05). These two results suggest that TacticAI significantly outperforms the feature-space baseline as a method for mining similar corners. This indicates that TacticAI is able to extract salient features from corners that are not trivial to extract from the input data alone, reinforcing it as a potent tool for discovering opposing team tactics from available data. Finally, we observed that this task exhibited a high level of inter-rater agreement for TacticAI-retrieved pairs ( F 1,4 = 1.01, p > 0.1) (Fig. 4 D), suggesting that human raters were largely in agreement with respect to their assessment of TacticAI’s performance.

Finally, we evaluated TacticAI’s player adjustment recommendations for their practical utility. Specifically, each rater was given 50 tactical refinements together with the corresponding real corner kick setups—see Supplementary Fig. 5 , and the “Case study design” section in the Supplementary Methods . The raters were then asked to rate each refinement as saliently improving the tactics (+1), saliently making them worse (−1), or offering no salient differences (0). We calculated the average rating assigned by each of the raters (giving us a value in the range [− 1, 1] for each rater). The average of these values across all five raters was 0.7 ± 0.1. Further, for 45 of the 50 situations (90%), the human raters found TacticAI’s suggestion to be favourable on average (by majority voting). Both of these results indicate that TacticAI’s recommendations are salient and useful to a downstream football club practitioner, and we set out to validate this with statistical tests.

We performed statistical significance testing of the observed positive ratings. First, for each of the 50 situations, we averaged its ratings across all five raters and then ran a t -test to assess whether the mean rating was significantly larger than zero. Indeed, the statistical test indicated that the tactical adjustments recommended by TacticAI were constructive overall ( \({t}_{49}^{{{{{{{{\rm{avg}}}}}}}}}=9.20,\, p \, < \, 0.001\) ). Secondly, we verified that each of the five raters individually found TacticAI’s recommendations to be constructive, running a t -test on each of their ratings individually. For all of the five raters, their average ratings were found to be above zero with statistical significance ( \({t}_{49}^{A}=5.84,\, {p}^{A} \, < \, 0.001;{t}_{49}^{B}=7.88,\; {p}^{B} \, < \, 0.001;{t}_{49}^{C}=7.00,\; {p}^{C} \, < \, 0.001;{t}_{49}^{D}=6.04,\; {p}^{D} \, < \, 0.001;{t}_{49}^{E}=7.30,\, {p}^{E} \, < \, 0.001\) ). In addition, their ratings also shared a high level of inter-agreement ( F 1,4 = 0.45, p > 0.05) (Fig. 4 E), suggesting a level of practical usefulness that is generally recognised by human experts, even though they represent different backgrounds.

Taking all of these results together, we find TacticAI to possess strong components for prediction, retrieval, and tactical adjustments on corner kicks. To illustrate the kinds of salient recommendations by TacticAI, in Fig. 5 we present four examples with a high degree of inter-rater agreement.

These examples are selected from our case study with human experts, to illustrate the breadth of tactical adjustments that TacticAI suggests to teams defending a corner. The density of the yellow circles coincides with the number of times that the corresponding change is recognised as constructive by human experts. Instead of optimising the movement of one specific player, TacticAI can recommend improvements for multiple players in one generation step through suggesting better positions to block the opposing players, or better orientations to track them more efficiently. Some specific comments from expert raters follow. In A , according to raters, TacticAI suggests more favourable positions for several defenders, and improved tracking runs for several others—further, the goalkeeper is positioned more deeply, which is also beneficial. In B , TacticAI suggests that the defenders furthest away from the corner make improved covering runs, which was unanimously deemed useful, with several other defenders also positioned more favourably. In C , TacticAI recommends improved covering runs for a central group of defenders in the penalty box, which was unanimously considered salient by our raters. And in D , TacticAI suggests substantially better tracking runs for two central defenders, along with a better positioning for two other defenders in the goal area.

We have demonstrated an AI assistant for football tactics and provided statistical evidence of its efficacy through a comprehensive case study with expert human raters from Liverpool FC. First, TacticAI is able to accurately predict the first receiver after a corner kick is taken as well as the probability of a shot as the direct result of the corner. Second, TacticAI has been shown to produce plausible tactical variations that improve outcomes in a salient way, while being indistinguishable from real scenarios by domain experts. And finally, the system’s latent player representations are a powerful means to retrieve similar set-piece tactics, allowing coaches to analyse relevant tactics and counter-tactics that have been successful in the past.

The broader scope of strategy modelling in football has previously been addressed from various individual angles, such as pass prediction 14 , 15 , 16 , shot prediction 3 or corner kick tactical classification 7 . However, to the best of our knowledge, our work stands out by combining and evaluating predictive and generative modelling of corner kicks for tactic development. It also stands out in its method of applying geometric deep learning, allowing for efficiently incorporating various symmetries of the football pitch for improved data efficiency. Our method incorporates minimal domain knowledge and does not rely on intricate feature engineering—though its factorised design naturally allows for more intricate feature engineering approaches when such features are available.

Our methodology requires the position and velocity estimates of all players at the time of execution of the corner and subsequent events. Here, we derive these from high-quality tracking and event data, with data availability from tracking providers limited to top leagues. Player tracking based on broadcast video would increase the reach and training data substantially, but would also likely result in noisier model inputs. While the attention mechanism of GATs would allow us to perform introspection of the most salient factors contributing to the model outcome, our method does not explicitly model exogenous (aleatoric) uncertainty, which would be valuable context for the football analyst.

While the empirical study of our method’s efficacy has been focused on corner kicks in association football, it readily generalises to other set pieces (such as throw-ins, which similarly benefit from similarity retrieval, pass and/or shot prediction) and other team sports with suspended play situations. The learned representations and overall framing of TacticAI also lay the ground for future research to integrate a natural language interface that enables domain-grounded conversations with the assistant, with the aim to retrieve particular situations of interest, make predictions for a given tactical variant, compare and contrast, and guide through an interactive process to derive tactical suggestions. It is thus our belief that TacticAI lays the groundwork for the next-generation AI assistant for football.

We devised TacticAI as a geometric deep learning pipeline, further expanded in this section. We process labelled spatio-temporal football data into graph representations, and train and evaluate on benchmarking tasks cast as classification or regression. These steps are presented in sequence, followed by details on the employed computational architecture.

Raw corner kick data

The raw dataset consisted of 9693 corner kicks collected from the 2020–21, 2021–22, and 2022–23 (up to January 2023) Premier League seasons. The dataset was provided by Liverpool FC and comprises four separate data sources, described below.

Our primary data source is spatio-temporal trajectory frames (tracking data), which tracked all on-pitch players and the ball, for each match, at 25 frames per second. In addition to player positions, their velocities are derived from position data through filtering. For each corner kick, we only used the frame in which the kick is being taken as input information.

Secondly, we also leverage event stream data, which annotated the events or actions (e.g., passes, shots and goals) that have occurred in the corresponding tracking frames.

Thirdly, the line-up data for the corresponding games, which recorded the players’ profiles, including their heights, weights and roles, is also used.

Lastly, we have access to miscellaneous game data, which contains the game days, stadium information, and pitch length and width in meters.

Graph representation and construction

We assumed that we were provided with an input graph \({{{{{{{\mathcal{G}}}}}}}}=({{{{{{{\mathcal{V}}}}}}}},\,{{{{{{{\mathcal{E}}}}}}}})\) with a set of nodes \({{{{{{{\mathcal{V}}}}}}}}\) and edges \({{{{{{{\mathcal{E}}}}}}}}\subseteq {{{{{{{\mathcal{V}}}}}}}}\times {{{{{{{\mathcal{V}}}}}}}}\) . Within the context of football games, we took \({{{{{{{\mathcal{V}}}}}}}}\) to be the set of 22 players currently on the pitch for both teams, and we set \({{{{{{{\mathcal{E}}}}}}}}={{{{{{{\mathcal{V}}}}}}}}\times {{{{{{{\mathcal{V}}}}}}}}\) ; that is, we assumed all pairs of players have the potential to interact. Further analyses, leveraging more specific choices of \({{{{{{{\mathcal{E}}}}}}}}\) , would be an interesting avenue for future work.

Additionally, we assume that the graph is appropriately featurised. Specifically, we provide a node feature matrix, \({{{{{{{\bf{X}}}}}}}}\in {{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times k}\) , an edge feature tensor, \({{{{{{{\bf{E}}}}}}}}\in {{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times | {{{{{{{\mathcal{V}}}}}}}}| \times l}\) , and a graph feature vector, \({{{{{{{\bf{g}}}}}}}}\in {{\mathbb{R}}}^{m}\) . The appropriate entries of these objects provide us with the input features for each node, edge, and graph. For example, \({{{{{{{{\bf{x}}}}}}}}}_{u}\in {{\mathbb{R}}}^{k}\) would provide attributes of an individual player \(u\in {{{{{{{\mathcal{V}}}}}}}}\) , such as position, height and weight, and \({{{{{{{{\bf{e}}}}}}}}}_{uv}\in {{\mathbb{R}}}^{l}\) would provide the attributes of a particular pair of players \((u,\, v)\in {{{{{{{\mathcal{E}}}}}}}}\) , such as their distance, and whether they belong to the same team. The graph feature vector, g , can be used to store global attributes of interest to the corner kick, such as the game time, current score, or ball position. For a simplified visualisation of how a graph neural network would process such an input, refer to Fig. 1 A.

To construct the input graphs, we first aligned the four data sources with respect to their game IDs and timestamps and filtered out 2517 invalid corner kicks, for which the alignment failed due to missing data, e.g., missing tracking frames or event labels. This filtering yielded 7176 valid corner kicks for training and evaluation. We summarised the exact information that was used to construct the input graphs in Table 2 . In particular, other than player heights (measured in centimeters (cm)) and weights (measured in kilograms (kg)), the players were anonymous in the model. For the cases in which the player profiles were missing, we set their heights and weights to 180 cm and 75 kg, respectively, as defaults. In total, we had 385 such occurrences out of a total of 213,246( = 22 × 9693) during data preprocessing. We downscaled the heights and weights by a factor of 100. Moreover, for each corner kick, we zero-centred the positions of on-pitch players and normalised them onto a 10 m × 10 m pitch, and their velocities were re-scaled accordingly. For the cases in which the pitch dimensions were missing, we used a standard pitch dimension of 110 m × 63 m as default.

We summarised the grouping of the features in Table 1 . The actual features used in different benchmark tasks may differ, and we will describe this in more detail in the next section. To focus on modelling the high-level tactics played by the attacking and defending teams, other than a binary indicator for ball possession—which is 1 for the corner kick taker and 0 for all other players—no information of ball movement, neither positions nor velocities, was used to construct the input graphs. Additionally, we do not have access to the player’s vertical movement, therefore only information on the two-dimensional movements of each player is provided in the data. We do however acknowledge that such information, when available, would be interesting to consider in a corner kick outcome predictor, considering the prevalence of aerial battles in corners.

Benchmark tasks construction

TacticAI consists of three predictive and generative models, which also correspond to three benchmark tasks implemented in this study. Specifically, (1) Receiver prediction, (2) Threatening shot prediction, and (3) Guided generation of team positions and velocities (Table 1 ). The graphs of all the benchmark tasks used the same feature space of nodes and edges, differing only in the global features.

For all three tasks, our models first transform the node features to a latent node feature matrix, \({{{{{{{\bf{H}}}}}}}}={f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) , from which we could answer queries: either about individual players—in which case we learned a relevant classifier or regressor over the h u vectors (the rows of H )—or about the occurrence of a global event (e.g. shot taken)—in which case we classified or regressed over the aggregated player vectors, ∑ u h u . In both cases, the classifiers were trained using stochastic gradient descent over an appropriately chosen loss function, such as categorical cross-entropy for classifiers, and mean squared error for regressors.

For different tasks, we extracted the corresponding ground-truth labels from either the event stream data or the tracking data. Specifically, (1) We modelled receiver prediction as a node classification task and labelled the first player to touch the ball after the corner was taken as the target node. This player could be either an attacking or defensive player. (2) Shot prediction was modelled as graph classification. In particular, we considered a next-ball-touch action by the attacking team as a shot if it was a direct corner, a goal, an aerial, hit on the goalposts, a shot attempt saved by the goalkeeper, or missing target. This yielded 1736 corners labelled as a shot being taken, and 5440 corners labelled as a shot not being taken. (3) For guided generation of player position and velocities, no additional label was needed, as this model relied on a self-supervised reconstruction objective.

The entire dataset was split into training and evaluation sets with an 80:20 ratio through random sampling, and the same splits were used for all tasks.

Graph neural networks

The central model of TacticAI is the graph neural network (GNN) 9 , which computes latent representations on a graph by repeatedly combining them within each node’s neighbourhood. Here we define a node’s neighbourhood, \({{{{{{{{\mathcal{N}}}}}}}}}_{u}\) , as the set of all first-order neighbours of node u , that is, \({{{{{{{{\mathcal{N}}}}}}}}}_{u}=\{v\,| \,(v,\, u)\in {{{{{{{\mathcal{E}}}}}}}}\}\) . A single GNN layer then transforms the node features by passing messages between neighbouring nodes 17 , following the notation of related work 10 , and the implementation of the CLRS-30 benchmark baselines 18 :

where \(\psi :{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{l}\times {{\mathbb{R}}}^{m}\to {{\mathbb{R}}}^{{k}^{{\prime} }}\) and \(\phi :{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{{k}^{{\prime} }}\to {{\mathbb{R}}}^{{k}^{{\prime} }}\) are two learnable functions (e.g. multilayer perceptrons), \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(t)}\) are the features of node u after t GNN layers, and ⨁ is any permutation-invariant aggregator, such as sum, max, or average. By definition, we set \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(0)}={{{{{{{{\bf{x}}}}}}}}}_{u}\) , and iterate Eq. ( 2 ) for T steps, where T is a hyperparameter. Then, we let \({{{{{{{\bf{H}}}}}}}}={f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})={{{{{{{{\bf{H}}}}}}}}}^{(T)}\) be the final node embeddings coming out of the GNN.

It is well known that Eq. ( 2 ) is remarkably general; it can be used to express popular models such as Transformers 19 as a special case, and it has been argued that all discrete deep learning models can be expressed in this form 20 , 21 . This makes GNNs a perfect framework for benchmarking various approaches to modelling player–player interactions in the context of football.

Different choices of ψ , ϕ and ⨁ yield different architectures. In our case, we utilise a message function that factorises into an attentional mechanism, \(a:{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{l}\times {{\mathbb{R}}}^{m}\to {\mathbb{R}}\) :

yielding the graph attention network (GAT) architecture 12 . In our work, specifically, we use a two-layer multilayer perceptron for the attentional mechanism, as proposed by GATv2 11 :

where \({{{{{{{{\bf{W}}}}}}}}}_{1},\, {{{{{{{{\bf{W}}}}}}}}}_{2}\in {{\mathbb{R}}}^{k\times h}\) , \({{{{{{{{\bf{W}}}}}}}}}_{e}\in {{\mathbb{R}}}^{l\times h}\) , \({{{{{{{{\bf{W}}}}}}}}}_{g}\in {{\mathbb{R}}}^{m\times h}\) and \({{{{{{{\bf{a}}}}}}}}\in {{\mathbb{R}}}^{h}\) are the learnable parameters of the attentional mechanism, and LeakyReLU is the leaky rectified linear activation function. This mechanism computes coefficients of interaction (a single scalar value) for each pair of connected nodes ( u , v ), which are then normalised across all neighbours of u using the \({{{{{{{\rm{softmax}}}}}}}}\) function.

Through early-stage experimentation, we have ascertained that GATs are capable of matching the performance of more generic choices of ψ (such as the MPNN 17 ) while being more scalable. Hence, we focus our study on the GAT model in this work. More details can be found in the subsection “Ablation study” section.

Geometric deep learning

In spite of the power of Eq. ( 2 ), using it in its full generality is often prone to overfitting, given the large number of parameters contained in ψ and ϕ . This problem is exacerbated in the football analytics domain, where gold-standard data is generally very scarce—for example, in the English Premier League, only a few hundred games are played every season.

In order to tackle this issue, we can exploit the immense regularity of data arising from football games. Strategically equivalent game states are also called transpositions, and symmetries such as arriving at the same chess position through different move sequences have been exploited computationally since the 1960s 22 . Similarly, game rotations and reflections may yield equivalent strategic situations 23 . Using the blueprint of geometric deep learning (GDL) 10 , we can design specialised GNN architectures that exploit this regularity.

That is, geometric deep learning is a generic methodology for deriving mathematical constraints on neural networks, such that they will behave predictably when inputs are transformed in certain ways. In several important cases, these constraints can be directly resolved, directly informing neural network architecture design. For a comprehensive example of point clouds under 3D rotational symmetry, see Fuchs et al. 24 .

To elucidate several aspects of the GDL framework on a high level, let us assume that there exists a group of input data transformations (symmetries), \({\mathfrak{G}}\) under which the ground-truth label remains unchanged. Specifically, if we let y ( X , E , g ) be the label given to the graph featurised with X , E , g , then for every transformation \({\mathfrak{g}}\in {\mathfrak{G}}\) , the following property holds:

This condition is also referred to as \({\mathfrak{G}}\) -invariance. Here, by \({\mathfrak{g}}({{{{{{{\bf{X}}}}}}}})\) we denote the result of transforming X by \({\mathfrak{g}}\) —a concept also known as a group action. More generally, it is a function of the form \({\mathfrak{G}}\times {{{{{{{\mathcal{S}}}}}}}}\to {{{{{{{\mathcal{S}}}}}}}}\) for some state set \({{{{{{{\mathcal{S}}}}}}}}\) . Note that a single group element, \({\mathfrak{g}}\in {\mathfrak{G}}\) can easily produce different actions on different \({{{{{{{\mathcal{S}}}}}}}}\) —in this case, \({{{{{{{\mathcal{S}}}}}}}}\) could be \({{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times k}\) ( X ), \({{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times | {{{{{{{\mathcal{V}}}}}}}}| \times l}\) ( E ) and \({{\mathbb{R}}}^{m}\) ( g ).

It is worth noting that GNNs may also be derived using a GDL perspective if we set the symmetry group \({\mathfrak{G}}\) to \({S}_{| {{{{{{{\mathcal{V}}}}}}}}}|\) , the permutation group of \(| {{{{{{{\mathcal{V}}}}}}}}|\) objects. Owing to the design of Eq. ( 2 ), its outputs will not be dependent on the exact permutation of nodes in the input graph.

Frame averaging

A simple mechanism to enforce \({\mathfrak{G}}\) -invariance, given any predictor \({f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) , performs frame averaging across all \({\mathfrak{G}}\) -transformed inputs:

This ensures that all \({\mathfrak{G}}\) -transformed versions of a particular input (also known as that input’s orbit) will have exactly the same output, satisfying Eq. ( 5 ). A variant of this approach has also been applied in the AlphaGo architecture 25 to encode symmetries of a Go board.

In our specific implementation, we set \({\mathfrak{G}}={D}_{2}=\{{{{{{{{\rm{id}}}}}}}},\leftrightarrow,\updownarrow,\leftrightarrow \updownarrow \}\) , the dihedral group. Exploiting D 2 -invariance allows us to encode quadrant symmetries. Each element of the D 2 group encodes the presence of vertical or horizontal reflections of the input football pitch. Under these transformations, the pitch is assumed completely symmetric, and hence many predictions, such as which player receives the corner kick, or takes a shot from it, can be safely assumed unchanged. As an example of how to compute transformed features in Eq. ( 6 ), ↔( X ) horizontally reflects all positional features of players in X (e.g. the coordinates of the player), and negates the x -axis component of their velocity.

Group convolutions

While the frame averaging approach of Eq. ( 6 ) is a powerful way to restrict GNNs to respect input symmetries, it arguably misses an opportunity for the different \({\mathfrak{G}}\) -transformed views to interact while their computations are being performed. For small groups such as D 2 , a more fine-grained approach can be assumed, operating over a single GNN layer in Eq. ( 2 ), which we will write shortly as \({{{{{{{{\bf{H}}}}}}}}}^{(t)}={g}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{{\bf{H}}}}}}}}}^{(t-1)},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) . The condition that we need a symmetry-respecting GNN layer to satisfy is as follows, for all transformations \({\mathfrak{g}}\in {\mathfrak{G}}\) :

that is, it does not matter if we apply \({\mathfrak{g}}\) it to the input or the output of the function \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) —the final answer is the same. This condition is also referred to as \({\mathfrak{G}}\) -equivariance, and it has recently proved to be a potent paradigm for developing powerful GNNs over biochemical data 24 , 26 .

To satisfy D 2 -equivariance, we apply the group convolution approach 13 . Therein, views of the input are allowed to directly interact with their \({\mathfrak{G}}\) -transformed variants, in a manner very similar to grid convolutions (which is, indeed, a special case of group convolutions, setting \({\mathfrak{G}}\) to be the translation group). We use \({{{{{{{{\bf{H}}}}}}}}}_{{\mathfrak{g}}}^{(t)}\) to denote the \({\mathfrak{g}}\) -transformed view of the latent node features at layer t . Omitting E and g inputs for brevity, and using our previously designed layer \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) as a building block, we can perform a group convolution as follows:

Here, ∥ is the concatenation operation, joining the two node feature matrices column-wise; \({{\mathfrak{g}}}^{-1}\) is the inverse transformation to \({\mathfrak{g}}\) (which must exist as \({\mathfrak{G}}\) is a group); and \({{\mathfrak{g}}}^{-1}{\mathfrak{h}}\) is the composition of the two transformations.

Effectively, Eq. ( 8 ) implies our D 2 -equivariant GNN needs to maintain a node feature matrix \({{{{{{{{\bf{H}}}}}}}}}_{{\mathfrak{g}}}^{(t)}\) for every \({\mathfrak{G}}\) -transformation of the current input, and these views are recombined by invoking \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) on all pairs related together by applying a transformation \({\mathfrak{h}}\) . Note that all reflections are self-inverses, hence, in D 2 , \({\mathfrak{g}}={{\mathfrak{g}}}^{-1}\) .

It is worth noting that both the frame averaging in Eq. ( 6 ) and group convolution in Eq. ( 8 ) are similar in spirit to data augmentation. However, whereas standard data augmentation would only show one view at a time to the model, a frame averaging/group convolution architecture exhaustively generates all views and feeds them to the model all at once. Further, group convolutions allow these views to explicitly interact in a way that does not break symmetries. Here lies the key difference between the two approaches: frame averaging and group convolutions rigorously enforce the symmetries in \({\mathfrak{G}}\) , whereas data augmentation only provides implicit hints to the model about satisfying them. As a consequence of the exhaustive generation, Eqs. ( 6 ) and ( 8 ) are only feasible for small groups like D 2 . For larger groups, approaches like Steerable CNNs 27 may be employed.

Network architectures

While the three benchmark tasks we are performing have minor differences in the global features available to the model, the neural network models designed for them all have the same encoder–decoder architecture. The encoder has the same structure in all tasks, while the decoder model is tailored to produce appropriately shaped outputs for each benchmark task.

Given an input graph, TacticAI’s model first generates all relevant D 2 -transformed versions of it, by appropriately reflecting the player coordinates and velocities. We refer to the original input graph as the identity view, and the remaining three D 2 -transformed graphs as reflected views.

Once the views are prepared, we apply four group convolutional layers (Eq. ( 8 )) with a GATv2 base model (Eqs. ( 3 ) and ( 4 )) as the \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) function. Specifically, this means that, in Eqs. ( 3 ) and ( 4 ), every instance of \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(t-1)}\) is replaced by the concatenation of \({({{{{{{{{\bf{h}}}}}}}}}_{{\mathfrak{h}}}^{(t-1)})}_{u}\parallel {({{{{{{{{\bf{h}}}}}}}}}_{{{\mathfrak{g}}}^{-1}{\mathfrak{h}}}^{(t-1)})}_{u}\) . Each GATv2 layer has eight attention heads and computes four latent features overall per player. Accordingly, once the four group convolutions are performed, we have a representation of \({{{{{{{\bf{H}}}}}}}}\in {{\mathbb{R}}}^{4\times 22\times 4}\) , where the first dimension corresponds to the four views ( \({{{{{{{{\bf{H}}}}}}}}}_{{{{{{{{\rm{id}}}}}}}}},\, {{{{{{{{\bf{H}}}}}}}}}_{\leftrightarrow },\, {{{{{{{{\bf{H}}}}}}}}}_{\updownarrow },\, {{{{{{{{\bf{H}}}}}}}}}_{\leftrightarrow \updownarrow }\in {{\mathbb{R}}}^{22\times 4}\) ), the second dimension corresponds to the players (eleven on each team), and the third corresponds to the 4-dimensional latent vector for each player node in this particular view. How this representation is used by the decoder depends on the specific downstream task, as we detail below.

For receiver prediction, which is a fully invariant function (i.e. reflections do not change the receiver), we perform simple frame averaging across all views, arriving at

and then learn a node-wise classifier over the rows of \({{{{{{{{\bf{H}}}}}}}}}^{{{{{{{{\rm{node}}}}}}}}}\in {{\mathbb{R}}}^{22\times 4}\) . We further decode H node into a logit vector \({{{{{{{\bf{O}}}}}}}}\in {{\mathbb{R}}}^{22}\) with a linear layer before computing the corresponding softmax cross entropy loss.

For shot prediction, which is once again fully invariant (i.e. reflections do not change the probability of a shot), we can further average the frame-averaged features across all players to get a global graph representation:

and then learn a binary classifier over \({{{{{{{{\bf{h}}}}}}}}}^{{{{{{{{\rm{graph}}}}}}}}}\in {{\mathbb{R}}}^{4}\) . Specifically, we decode the hidden vector into a single logit with a linear layer and compute the sigmoid binary cross-entropy loss with the corresponding label.

For guided generation (position/velocity adjustments), we generate the player positions and velocities with respect to a particular outcome of interest for the human coaches, predicted over the rows of the hidden feature matrix. For example, the model may adjust the defensive setup to decrease the shot probability by the attacking team. The model output is now equivariant rather than invariant—reflecting the pitch appropriately reflects the predicted positions and velocity vectors. As such, we cannot perform frame averaging, and take only the identity view’s features, \({{{{{{{{\bf{H}}}}}}}}}_{{{{{{{{\rm{id}}}}}}}}}\in {{\mathbb{R}}}^{22\times 4}\) . From this latent feature matrix, we can then learn a conditional distribution from each row, which models the positions or velocities of the corresponding player. To do this, we extend the backbone encoder with conditional variational autoencoder (CVAE 28 , 29 ). Specifically, for the u -th row of H id , h u , we first map its latent embedding to the parameters of a two-dimensional Gaussian distribution \({{{{{{{\mathcal{N}}}}}}}}({\mu }_{u}| {\sigma }_{u})\) , and then sample the coordinates and velocities from this distribution. At training time, we can efficiently propagate gradients through this sampling operation using the reparameterisation trick 28 : sample a random value \({\epsilon }_{u} \sim {{{{{{{\mathcal{N}}}}}}}}(0,1)\) for each player from the unit Gaussian distribution, and then treat μ u + σ u ϵ u as the sample for this player. In what follows, we omit edge features for brevity. For each corner kick sample X with the corresponding outcome o (e.g. a binary value indicating a shot event), we extend the standard VAE loss 28 , 29 to our case of outcome-conditional guided generation as

where h u is the player embedding corresponding to the u th row of H id , and \({\mathbb{KL}}\) is Kullback–Leibler (KL) divergence. Specifically, the first term is the generation loss between the real player input x u and the reconstructed sample decoded from h u with the decoder p ϕ . Using the KL term, the distribution of the latent embedding h u is regularised towards p ( h u ∣ o ), which is a multivariate Gaussian in our case.

A complete high-level summary of the generic encoder–decoder equivariant architecture employed by TacticAI can be summarised in Supplementary Fig. 2 . In the following section, we will provide empirical evidence for justifying these architectural decisions. This will be done through targeted ablation studies on our predictive benchmarks (receiver prediction and shot prediction).

Ablation study

We leveraged the receiver prediction task as a way to evaluate various base model architectures, and directly quantitatively assess the contributions of geometric deep learning in this context. We already see that the raw corner kick data can be better represented through geometric deep learning, yielding separable clusters in the latent space that could correspond to different attacking or defending tactics (Fig. 2 ). In addition, we hypothesise that these representations can also yield better performance on the task of receiver prediction. Accordingly, we ablate several design choices using deep learning on this task, as illustrated by the following four questions:

Does a factorised graph representation help? To assess this, we compare it against a convolutional neural network (CNN 30 ) baseline, which does not leverage a graph representation.

Does a graph structure help? To assess this, we compare against a Deep Sets 31 baseline, which only models each node in isolation without considering adjacency information—equivalently, setting each neighbourhood \({{{{{{{{\mathcal{N}}}}}}}}}_{u}\) to a singleton set { u }.

Are attentional GNNs a good strategy? To assess this, we compare against a message passing neural network 32 , MPNN baseline, which uses the fully potent GNN layer from Eq. ( 2 ) instead of the GATv2.

Does accounting for symmetries help? To assess this, we compare our geometric GATv2 baseline against one which does not utilise D 2 group convolutions but utilises D 2 frame averaging, and one which does not explicitly utilise any aspect of D 2 symmetries at all.

Each of these models has been trained for a fixed budget of 50,000 training steps. The test top- k receiver prediction accuracies of the trained models are provided in Supplementary Table 2 . As already discussed in the section “Results”, there is a clear advantage to using a full graph structure, as well as directly accounting for reflection symmetry. Further, the usage of the MPNN layer leads to slight overfitting compared to the GATv2, illustrating how attentional GNNs strike a good balance of expressivity and data efficiency for this task. Our analysis highlights the quantitative benefits of both graph representation learning and geometric deep learning for football analytics from tracking data. We also provide a brief ablation study for the shot prediction task in Supplementary Table 3 .

Training details

We train each of TacticAI’s models in isolation, using NVIDIA Tesla P100 GPUs. To minimise overfitting, each model’s learning objective is regularised with an L 2 norm penalty with respect to the network parameters. During training, we use the Adam stochastic gradient descent optimiser 33 over the regularised loss.

All models, including baselines, have been given an equal hyperparameter tuning budget, spanning the number of message passing steps ({1, 2, 4}), initial learning rate ({0.0001, 0.00005}), batch size ({128, 256}) and L 2 regularisation coefficient ({0.01, 0.005, 0.001, 0.0001, 0}). We summarise the chosen hyperparameters of each TacticAI model in Supplementary Table 1 .

Data availability

The data collected in the human experiments in this study have been deposited in the Zenodo database under accession code https://zenodo.org/records/10557063 , and the processed data which is used in the statistical analysis and to generate the relevant figures in the main text are available under the same accession code. The input and output data generated and/or analysed during the current study are protected and are not available due to data privacy laws and licensing restrictions. However, contact details of the input data providers are available from the corresponding authors on reasonable request.

Code availability

All the core models described in this research were built with the Graph Neural Network processors provided by the CLRS Algorithmic Reasoning Benchmark 18 , and their source code is available at https://github.com/google-deepmind/clrs . We are unable to release our code for this work as it was developed in a proprietary context; however, the corresponding authors are open to answer specific questions concerning re-implementations on request. For general data analysis, we used the following freely available packages: numpy v1.25.2 , pandas v1.5.3 , matplotlib v3.6.1 , seaborn v0.12.2 and scipy v1.9.3 . Specifically, the code of the statistical analysis conducted in this study is available at https://zenodo.org/records/10557063 .

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Acknowledgements

We gratefully acknowledge the support of James French, Timothy Waskett, Hans Leitert and Benjamin Hervey for their extensive efforts in analysing TacticAI’s outputs. Further, we are thankful to Kevin McKee, Sherjil Ozair and Beatrice Bevilacqua for useful technical discussions, and Marc Lanctôt and Satinder Singh for reviewing the paper prior to submission.

Author information

These authors contributed equally: Zhe Wang, Petar Veličković, Daniel Hennes.

Authors and Affiliations

Google DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK

Zhe Wang, Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, Jerome Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess & Demis Hassabis

Liverpool FC, AXA Training Centre, Simonswood Lane, Kirkby, Liverpool, L33 5XB, UK

William Spearman

Liverpool FC, Kirkby, UK

University of Alberta, Amii, Edmonton, AB, T6G 2E8, Canada

Michael Bowling

Google DeepMind, London, UK

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Contributions

Z.W., D. Hennes, L.P. and K.T. coordinated and organised the research effort leading to this paper. P.V. and Z.W. developed the core TacticAI models. Z.W., W.S. and I.G. prepared the Premier League corner kick dataset used for training and evaluating these models. P.V., Z.W., D. Hennes and N.T. designed the case study with human experts and Z.W. and P.V. performed the qualitative evaluation and statistical analysis of its outcomes. Z.W., P.V., D. Hennes, N.T., L.P., M. Kaisers, Y.B., R.E., L.K.W., F.P., W.S., I.G., N.H., M.B., D. Hassabis and K.T. contributed to writing the paper and providing feedback on the final manuscript. J.C., Y.Y., A.R., M. Khan, N.B., P.S. and P.M. contributed valuable technical and implementation discussions throughout the work’s development.

Corresponding authors

Correspondence to Zhe Wang , Petar Veličković or Karl Tuyls .

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Competing interests.

The authors declare no competing interests but the following competing interests: TacticAI was developed during the course of the Authors’ employment at Google DeepMind and Liverpool Football Club, as applicable to each Author.

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Wang, Z., Veličković, P., Hennes, D. et al. TacticAI: an AI assistant for football tactics. Nat Commun 15 , 1906 (2024). https://doi.org/10.1038/s41467-024-45965-x

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