Introduction

Sports Analytics, as it is referred to these days, is rapidly becoming a booming domain. The basic idea is to collect huge chunks of relevant historical data, analyze the same, from a more statistical point of view, in order to gain insights which effectively can provide a competitive edge to a team or individual. But hold on. There are way to many number of sports where-in such kind of analysis can be performed! (Remember Moneyball?)

This website will be primarily be focusing on soccer (European football). The reason - well lets just say that there is a lot of potential for the field of analytics to be successful here. Below are some of the aspects (but not limited to), which can be achieved using soccer analytics :

➢ Tracking Match Events
➢ Winner Prediction
➢ Match Performance Evaluation
➢ Set Piece Optimization
➢ Player Recruitment
➢ Game planning and Strategy

Sports Analytics is not a new term. It has existed for a while and remarkable work has been done in the field. Lets take a look with the following two news articles, about what has been successfully achieved:

The article mentioned above talks about Injuries in sports - how Data Science techniques are able to predict injuries in players and how Machine Learning is able to tell athletes when to train and when to stop.

It talks about Alessio Rossi - an aspiring footballer who could never make it due to his career ending injury. Rossi now is a researcher, who analyses chunks and chunks of data to help prevent players at top teams getting injuries of their own.

Click here to read the article

This article talks about Data Science being used in Soccer and is primarily focused on busting the myth that players are begun to shed/let go once they reach the age of 30. It states that such presumptions rely and are based on outdated logic - which is proved by statistical methods.

Click here to read the article

Soccer being the most popular and most watched sport in the world, evidently, there is huge chunks of data across different league hierarchies, countries, continents - where-in encouraging insights can be found, which may lead to improved performance of teams / players, and might be of favorable results on the business front as well.

Machine learning (ML) and data science are being increasingly used in soccer analytics due to their ability to analyze large and complex data sets, extract meaningful insights, and provide predictive models that can improve decision-making in soccer. By analyzing data from various sources, such as player performance data, match statistics, and social media data, ML and data science can help coaches, scouts, and analysts gain a deeper understanding of player performance, team dynamics, and match strategies. For example, ML algorithms can be used to analyze player data to identify patterns in performance, such as strengths and weaknesses, player tendencies, and optimal positions for each player. This can help coaches make better decisions about player selection, substitutions, and training programs. Data science can also be used to analyze match data to identify patterns in team performance, such as successful plays, opponent weaknesses, and optimal tactics. This can help coaches and analysts develop better match strategies, adjust tactics during matches, and identify areas for improvement. In addition, ML and data science can be used to predict match outcomes, player performance, and even future player value. This can help clubs make more informed decisions about player transfers, contracts, and overall team strategy.

Lets talk about some prominent methods and techniques in the field of Machine Learning, AI and Data Science and how they can be applied in the field of Soccer Analytics.

First, Clustering. Clustering methods can be used in soccer analytics, lets say, to group players based on similar playing styles to begin with. By clustering players, coaches and analysts can identify potential player combinations for improved team performance. Clustering can also be used to group teams based on their overall performance, such as identifying teams with similar playing styles or strengths and weaknesses. Cluster analysis can be effectively used to identify playing patterns, such as identifying areas on the pitch where a team is most effective or areas where they struggle.By analyzing player clusters, coaches can create targeted training plans to improve specific skills and styles. Clustering can also be used to analyze specific events during a game, such as corner kicks or free kicks, to identify optimal strategies. Cluster analysis can help identify trends in player performance, such as determining which players are most effective in certain positions or against certain types of opponents. By clustering fans based on their behavior, preferences, and demographics, teams can tailor marketing campaigns to specific groups of fans.Finally, clustering can be used to analyze player social media behavior and identify players with strong influence or potential for brand endorsements.

Next, Decision Trees. Decision tree methods are an invaluable tool for soccer analytics, providing insights into a wide range of factors that can impact game outcomes and team performance. By analyzing game statistics and player performance, decision trees can be used to identify the most important features that contribute to success on the field, such as goals scored, assists, and turnovers. Coaches can use decision tree analysis to develop targeted strategies for improving player performance and team tactics, addressing areas of weakness and optimizing team strengths. Furthermore, decision trees can be used to predict the likelihood of injuries based on player history and playing conditions, allowing teams to develop injury prevention strategies and ensure player safety. Decision trees can also be used to analyze fan behavior and identify factors that impact attendance, such as weather conditions or game timing. Teams can use this information to develop targeted marketing strategies, improve fan engagement, and increase ticket sales.

Moving on, we have Support Vector Machines. Support Vector Machines (SVMs) are another powerful tool in soccer analytics. SVMs can be used to classify and predict player positions, as well as to evaluate player performance based on a variety of metrics. For example, one could use SVMs to analyze player movement patterns and classify players into different positions, such as striker, midfielder, or defender. SVMs can also be used to evaluate the effectiveness of different playing styles and strategies, by analyzing player data and identifying the most successful patterns of play. This information can be used by coaches to improve team performance and optimize player selection. Furthermore, SVMs can be used to predict game outcomes, such as which team is more likely to win based on a variety of factors such as team composition, past performance, and player statistics. SVMs can also be used to analyze the performance of individual players, such as how many goals or assists they are likely to score in a game. By using SVMs to analyze game data and identify patterns, coaches and analysts can gain insights into the strengths and weaknesses of their team and develop strategies to improve performance. SVMs can also be used to analyze player injuries and predict the likelihood of injury based on various factors such as age, position, and playing style. This information can be used to design training programs that reduce the risk of injury and improve overall player health. In addition, SVMs can be used to evaluate player potential, by analyzing player performance data and predicting future success based on various factors such as age, experience, and skill level.

With the recent boom and advancements in technology and the way, different kinds of data can be gathered, Soccer analytics can help teams to better understand their strengths and weaknesses, allowing them to develop targeted strategies and game plans. Not only this, it allows teams to track player performance, evaluate tactics, and identify areas for improvement. By analyzing performance data, teams can also identify areas where they need to make adjustments to their training programs. It can also provide valuable insights into injury prevention, helping teams to reduce the risk of injury to their players. Soccer analytics can also be used to evaluate and compare players, helping teams to make informed decisions when signing new players or making roster changes. Data visualization tools can be used to help coaches and analysts understand and communicate complex data. Ultimately, the use of soccer analytics can give teams a competitive advantage, leading to improved performance and increased success on the field.

Q/A

What team dominates each of the Europe's top 5 leagues ?

The dominance of each team in the top 5 European soccer leagues can vary from season to season. However, as of the 2022-2023 season, the following teams can be considered dominant in their respective leagues, over the course of past 10 years:

English Premier League (EPL): Manchester City has been the dominant team in the EPL in recent years, winning the title 5 times in the last 10 seasons.
La Liga (Spain): FC Barcelona and Real Madrid are traditionally the dominant teams in La Liga. However, in recent years, Atlético Madrid has emerged as a strong contender, winning the title in the 2020-2021 season.
Bundesliga (Germany): Bayern Munich is the dominant team in the Bundesliga, having won the title in each of the last 9 seasons (as of the 2021-2022 season). Second to Bayern, comes Borussia Dortmund.
Serie A (Italy): Juventus has been the dominant team in Serie A in recent years, winning the title in 9 of the last 10 seasons. However, Inter Milan won the title in the 2020-2021 season.
Ligue 1 (France): Paris Saint-Germain is currently the dominant team in Ligue 1, having won the title in 8 of the last 10 seasons.

What is the past 10 years trend across the big 5 leagues?

Over the past 10 years, there has been a trend of dominance by a few teams in each of the big 5 European soccer leagues. In the English Premier League, Manchester City. In Spain's La Liga, Barcelona and Real Madrid continue to be the dominant teams. In Italy's Serie A, Juventus has been the most dominant team. In the German Bundesliga, Bayern Munich and finally, in France's Ligue 1, Paris Saint-Germain, has been the dominant team. Overall, the past 10 years have seen a trend of a few teams dominating their respective leagues, with little variation in the top teams in each league.

Does age affect performance ?

Age can affect performance in soccer, as players generally experience a decline in physical abilities as they get older. This decline can include decreases in speed, agility, strength, and endurance, which can affect a player's ability to perform at the same level they did when they were younger. However, the impact of age on performance can vary depending on the player's position, playing style, and other factors. Some players may be able to continue performing at a high level into their 30s or even 40s, while others may experience a decline in performance earlier. Additionally, factors such as injuries, playing time, and training can also play a role in how age affects a player's performance. Players like Karim Benzema, Leo Messi, Cristiano Ronaldo, Robert Lewandowski and many more, continue to perform at optimum levels, despite the fact that all of them are over 34 years old. Overall, age can be an important factor to consider when analyzing soccer performance, but it should be considered in conjunction with other factors that may impact a player's abilities.

Who were the top players ?

Identifying the top players in soccer over the past 10 years is a subjective matter and can vary depending on personal opinions and criteria used for evaluation. However, some players who have consistently performed at a high level and achieved significant success in their respective teams during this period. The below list is purely based on statistics and facts which were deduced by the data at hand. Apart from the but obvious players - Leo Messi and Cristiano Ronaldo, the below players can be considered top :

Neymar Jr. - Brazil/Paris Saint-Germain & Barcelona
Robert Lewandowski - Poland/Bayern Munich
Mohamed Salah - Egypt/Liverpool
Kevin De Bruyne - Belgium/Manchester City
Sergio Ramos - Spain/Real Madrid
Luka Modric - Croatia/Real Madrid
Luis Suaraz - Uruguay/Ajax, Liverpool & Barcelona

This list is by no means exhaustive and other players could also be considered based on their performances and achievements.

Which team was the most successful in Europe?

Over the past 10 years, the most successful team in Europe's top competition - the UEFA Champions League (UCL) is Real Madrid. They have won the UCL 4 times in the last 10 years, in 2014, 2016, 2017, and 2018. Other teams that have won the UCL during this time period include Barcelona in 2015, Bayern Munich in 2013 and Chelsea in 2012. Real Madrid's recent dominance in the UCL has made them one of the most successful teams in the history of the competition, with a total of 13 UCL titles.

What makes a team successful ?

Apart from the obvious attributes like scoring goals and keeping possession, we attempted to identify, uncommon attributes which contribute to the success of a team. The below numbers and statistics are for the top teams (mentioned above) across the big 5 leagues in Europe:

To be successful, it's not just about attempting shots but rather making the opposition goalkeeper work by hitting shots on target. Our analysis indicates that a team must maintain their shots on target between 34-39% in each game to achieve consistency in their performance over time.
Based on our statistical analysis, the data and results suggest that a team must play on the front foot and be aggressive in order to win games and be successful. On average, the team finishing on the top end of the table, tend to move the ball between 1077-1232 yards per game, towards the opponent's goal, indicating intent and desire to score. The same numbers drop to 748-912 yards for teams finishing on the lower ends of the table.

Besides these, some other attributes which contribute to a team's success include:
> Team Chemistry
> Youth Development
> Stadium Atmosphere
> Corners and Set Piece Optimization

Defensive and offensive stats analysis

Analyzing the defensive and offensive stats over the past 10 seasons of soccer in Europe's top 5 leagues reveals some interesting trends.

In terms of defense, teams in the top 5 leagues have been improving over the past decade, with an average of 60-64% tackles won per game.
However, the number of errors leading to opposition shots has been increasing, with an average of 12-16.5 errors per season.
On the offensive front, teams have been consistent in terms of shots attempted, with the conversion rate of shots on target to goals ranging between 30-35% per game.

Below are the clean sheet statistics for the top teams across europe:

La Liga: Real Madrid - 0.86, Barcelona - 0.79, Atletico Madrid - 1.17
Premier League: Manchester City - 0.79, Manchester United - 0.71, Liverpool - 0.70, Chelsea - 0.73, Arsenal - 0.63
Serie A: Juventus - 1.05, Inter Milan - 0.99, AC Milan - 0.89, Roma - 0.84, Napoli - 0.93
Bundesliga: Bayern Munich - 1.10, Borussia Dortmund - 0.92
Ligue 1: Paris Saint-Germain - 1.50

Who were the top scorers and what attributes made them the best?

Over the past 10 seasons in soccer, several players have been top scorers in their respective leagues. Here are some of the top scorers and the attributes that make them the best:

Lionel Messi - Messi has been the top scorer in La Liga for several seasons. His main attributes are his exceptional dribbling skills, vision, and ability to score goals from anywhere on the pitch.
Cristiano Ronaldo - Ronaldo has been the top scorer in Serie A for the past two seasons. His main attributes are his speed, strength, and his ability to score goals with both feet and his head.
Robert Lewandowski - Lewandowski has been the top scorer in the Bundesliga for the past few seasons. His main attributes are his positioning, finishing, and his ability to score goals from difficult angles.
Mohamed Salah - Salah has been the top scorer in the Premier League for two seasons. His main attributes are his pace, dribbling, and his ability to score goals from long range.
Kylian Mbappe - Mbappe has been the top scorer in Ligue 1 for the past few seasons. His main attributes are his speed, dribbling, and his ability to score goals with both feet.

The top scorers in soccer have a combination of exceptional technical skills, physical attributes, and an eye for goal. They are often the most creative and skillful players on the pitch and are able to create and score goals in a variety of ways.

Various co-relations across different attributes

Again, to answer this question, we attempted to identify hidden and uncommon correlations, and avoid the obvious ones. Some insights and conclusions we could draw include:

A team's success rate is negatively correlated with the number of yellow cards they receive. In other words, teams that receive fewer yellow cards tend to have a higher success rate.
A goalkeeper's height is negatively correlated with their diving ability. Taller goalkeepers may struggle to dive as quickly and efficiently as comparatively shorter goalkeepers.
The number of fouls committed by a team is positively correlated with their possession percentage. In other words, teams that commit more fouls tend to have a higher possession percentage.
The number of passes completed by a team is negatively correlated with the number of goals they score. In other words, teams that complete more passes may struggle to score as many goals as teams that complete fewer passes.
The distance a team covers during a match is positively correlated with their possession percentage.

Who will win the current season ?

Predicting the winner of a sports league involves analyzing various factors, including team performance, player form, injuries, and team dynamics, among others. Additionally, there are numerous unforeseeable events and variables that could influence the outcome of the league, making it difficult to provide an accurate prediction. It is important to analyze and consider all relevant data, including past performance, team dynamics, and individual player statistics, when making predictions. However, even with extensive analysis and data, it is very difficult to predict the future with complete accuracy. Also, bear in mind that sports are unpredictable, and any predictions should be taken with a grain of salt.

INTRODUCTION

Q/A

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