This dataset contains data for last 10 seasons of Spanish La Liga including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.u...

La Liga Players Performance Dataset

This dataset provides a comprehensive overview of player performance in the La Liga capturing a wide array of metrics related to gameplay, scoring, passing, and defensive actions. With records detailing individual player statistics across different teams, this dataset is a valuable resource for analysts, data scientists, and fans who are interested in diving into player performance data from one of the world’s top soccer leagues.

Each entry represents a single player's profile, featuring data on expected goals (xG), expected assists (xAG), touches, dribbles, tackles, and more. This dataset is ideal for analyzing various aspects of player contribution, both offensively and defensively, and understanding their impact on team performance.

Dataset Columns

Player: Name of the player Team: Team the player belongs to '#' : Player's jersey number Nation: Nationality of the player Position: Primary playing position on the field Age: Age of the player Minutes: Total minutes played Goals: Number of goals scored Assists: Number of assists Penalty Shoot on Goal: Penalty shots taken on goal Penalty Shoot: Total penalty shots attempted Total Shoot: Total shots attempted Shoot on Target: Shots successfully on target Yellow Cards: Number of yellow cards received Red Cards: Number of red cards received Touches: Total ball touches Dribbles: Total dribbles attempted Tackles: Total tackles made Blocks: Total blocks Expected Goals (xG): Expected goals, calculated based on shooting positions and likelihood of scoring Non-Penalty xG (npxG): Expected goals excluding penalties Expected Assists (xAG): Expected assists, based on actions leading to an expected goal (xG) Shot-Creating Actions: Actions leading to a shot attempt Goal-Creating Actions: Actions leading to a goal Passes Completed: Successful passes completed Passes Attempted: Total passes attempted Pass Completion %: Pass completion rate, expressed as a percentage (some entries have missing values here) Progressive Passes: Passes advancing the ball significantly toward the opponent’s goal Carries: Total ball carries Progressive Carries: Carries advancing the ball significantly toward the opponent’s goal Dribble Attempts: Total dribbles attempted Successful Dribbles: Total successful dribbles Date: Date of record collection or game date

Potential Use Cases

Data Visualization: Explore relationships between various performance metrics to identify patterns.

Player Comparisons: Compare individual players based on goals, assists, xG, xAG, and other metrics.

Team Analysis: Evaluate contributions of players within the same team to gain insights into team dynamics.

Predictive Modeling: Use the dataset to build models for predicting game outcomes, goals, or assists based on player performance metrics.

La Liga 2023/24: Match, Player, and Team Performance Insights

Dataset Description

This dataset provides an in-depth look at the 2023/24 La Liga season, covering various aspects of team and player performances across all matchdays. With over 50 individual CSV files, the dataset includes statistics on passing accuracy, goal-scoring, defensive actions, possession metrics, and player ratings, among others. Whether you're interested in analyzing top scorers, understanding team strengths, or delving into player-specific contributions, this dataset offers a rich foundation for football analytics enthusiasts and professionals.

In addition to the core dataset, we have now added more files related to the league table, expanding the dataset with essential information on match outcomes, league standings, and advanced metrics.

Contents

The dataset contains the following types of data:

• Team Performance Metrics: Information on accurate passes, crosses, goals conceded, interceptions, and other team stats.
• Player Performance Metrics: Individual stats including expected goals (xG), assists, clearances, fouls committed, and tackles won.
• Match-Specific Insights: Detailed metrics on goals scored, scoring attempts, possession percentages, and cards issued per match.
• Match Details (New): Information about rounds, match IDs, teams, scores, and match statuses.
• League Tables (New):
  • Overall standings including matches played, wins, draws, losses, goals scored, goal differences, and points.
  • Separate breakdowns for home and away performances.
  • Advanced metrics including expected goals (xG), expected goals conceded, and expected points.

The file details provide an overview of each dataset, including a brief description of the data structure and potential uses for analysis. This helps users quickly navigate and understand the data available for analysis.

This dataset is ideal for statistical analysis, data visualization, and machine learning applications to uncover patterns in football performance.

Suggested Analysis

This dataset opens up multiple avenues for data analysis and visualization. Here are some ideas:

1. Team Performance Analysis

• Analyze team performance trends, such as comparing passing accuracy, possession, and expected goals (xG) across teams.
• Visualize which teams generate the most scoring opportunities and miss the most big chances.
• Identify the strongest and weakest defenses based on goals conceded, clean sheets, and clearances.

2. Player Performance Analysis

• Identify top-performing players by goals scored, assists, expected goals, and expected assists.
• Explore defensive contributions by analyzing tackles won, interceptions, and clearances per player.
• Assess attacking efficiency by comparing total attempts vs. on-target attempts for each player.

3. Goalkeeping and Defensive Analysis

• Compare goalkeepers on metrics like saves made, goals conceded, and clean sheets to highlight the top performers of the season.
• Evaluate defensive strength by analyzing interception rates and clearances by both teams and players.

4. League Table Insights (New)

• Analyze overall league standings to determine team performance trends.
• Explore home and away performance and identify strengths and weaknesses in different scenarios.
• Utilize advanced metrics to evaluate under- and overperforming teams.

5. Advanced Metrics Exploration

• Examine possession-based metrics, such as possession percentage and possessions won in the attacking third, to identify possession-dominant teams.
• Use expected goals and expected assists data to build profiles highlighting efficient playmaking and finishing among players and teams.

This dataset is a valuable resource for football enthusiasts, data scientists, and analysts interested in uncovering patterns, building predictive models, or generating insights for La Liga 2023/24.

License and Disclaimer

License

This dataset is shared for non-commercial, educational, and personal analysis purposes only. It is not intended for redistribution, commercial use, or integration into other public datasets.

Disclaimer

This dataset was sourced from FotMob, a proprietary provider of football statistics. All rights to the original data belong to FotMob. The dataset is a restructured collection of publicly viewable data and does not claim ownership over FotMob's data. Users should reference FotMob as the original source when using this dataset for research or analysis.

Terms of Use

By using this dataset, you agree to the following: - Non-commercial Use: This dataset is only for educational, analytical, and personal use. It may not be used for commercial purposes or integrated into other public datasets. - Proper Attribution: Please attribu...

This dataset consists in 22 JSON files representing a season of the Spanish Football League ("La Liga").

The dataset represents several hierarchically related elements, however, only the Match, Event and Player elements contain relevant information for analysis. The rest of the elements simply serve to keep the data structured, by seasons and matchdays. The dataset collects information from several seasons between the years 2000 and 2022. The attributes of each of the elements that make up the dataset are described below:

Season: JSON documents represent a season, their root contains the following information:

• competition: Name by which the competition is known
• country: Country where the competition is held
• season_id: Identifier of the season, example: Season 2021/22
• season_url: Relative URL of the season's web page
• rounds: List of Round elements, the days into which the championship is divided

Rounds: (or matchdays) Collection of matches:

• number: Name of the matchday, e.g.: Matchday 1.
• matches: List of Match elements, matches that are played on the same day/s of the championship.

Match: contains relevant match information.

• id: Match identifier used at BeSoccer.com
• status: Code representing the status of the match: Played (1), Not Played (0)
• home_team: Name of the home team
• away_team: Name of the away team
• result: List of two integers representing the match score
• date_time: Date and time at which the match started
• referee: First and last name of the referee of the match
• href: URL relative to the match page
• home_tactic: Tactical arrangement of the home team, e.g.: 4-3-3
• home_lineup: List of players in the starting lineup of the home team
• home_bench: List of the home team's substitute players
• away_tactic: Tactical arrangement of the away team, e.g. 4-3-3
• away_lineup: List of players in the home team's starting lineup
• away_bench: List of substitute players of the away team

Event: contains information that defines each of the relevant actions that occur during a soccer match. Events can be described by the following attributes:

• player: Player identifier. Relative URL
• team: Team of the player who participates in the event
• minute: Minute of the match in which the event occurs
• type: Event type (Enumeration)

Players: Player information:

• name: First name
• fullname: Player's full name
• dob: Date of birth
• country: Nationality
• position: Position the player usually occupies: GOA (GoalKeeper), DF (Defender), MID (Midfielder), STR (Striker)
• foot: Dominant Foot: Right-footed, Left-footed, Two-footed, Unknown
• weight: Weight of player in kilograms
• height: Player height in centimeters
• elo: Measurement of the player's skills on a scale of 1 to 100
• potential: Estimate of the maximum ELO that a player can reach on a scale of 1 to 100.
• href: Relative URL of the player's record

Analysis of ‘La Liga Match Data ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sanjeetsinghnaik/la-liga-match-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

La Liga is by far one of the world’s most entertaining leagues. They have some of the best managers, players and fans! But, what makes it truly entertaining is the sheer unpredictability. There are 6 equally amazing teams with a different team lifting the trophy every season. Not only that, the league has also witnessed victories from teams outside of the top 6. So, let us analyze some of these instances.

So far, the implementation of statistics into soccer has been positive. Teams can easily gather information about their opponents and their tactics. This convenience allows managers to create well-thought out game plans that suit their team, maximize opponents' weaknesses, and increase their chances of winning.

A goal is scored when the whole of the ball passes over the goal line, between the goalposts and under the crossbar, provided that no offence has been committed by the team scoring the goal. If the goalkeeper throws the ball directly into the opponents' goal, a goal kick is awarded.

THE TIME OF SEASON/MOTIVATION: While a club battling for a league title is going to be hungry for a win, as is a side that is fighting to stay up, a club that has already won the title or has already been relegated is unlikely to work as hard, and often rest players as well. THE REFEREE: Of course, when referee's send players off it make a massive impact on a match, but even if he is just awarding a yellow card then it can affect the outcome of the game as the player booked is less likely to go in as hard for the rest of the match.

SUBSTITUTES: The whole point of substitutes is for them to be able to come on and impact a match. Subs not only bring on a fresh pair of legs that are less tired than starters and more likely to track back and push forward, but can also play crucial roles in the formation of a team.

MIND GAMES/MANAGERS: Playing mind games has almost become a regular routine for top level managers, and rightly so. Just a simple mind game can do so much to impact a match, a good example coming from Sir Alex Ferguson.

Per his autobiography, when Manchester United were losing late on in a match at a certain point he would tap his watch and make sure to let the opposition know he is signalling this to his players. United's opposition already know that United have a tendency to come back from behind, and upon seeing this gesture they will think that United are going to come back. And because scientific studies prove that living creatures are more likely to accept things that have happened before than not - horses are more likely to lose to a horse they have already lost to in a race even if they are on an even playing field - they often succumb to a loss.

FORM/INJURIES/FIXTURES: A team on better form is more likely to win a match than if they have been on a poor run of form, while a team in the middle of a condensed run of fixtures is less likely to win than a well rested team. These are just some of the things that affect matches - if you have any other just mention them in the comment section below and I'll try to add them in!

--- Original source retains full ownership of the source dataset ---

Analysis of ‘LaLiga Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sdelquin/laliga-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

LaLiga is the trademark behind the Spanish Football Competitions and their data can be viewed on laliga.com but there is no way to download a csv file with the whole information.

To that end, I've built a Python scraper to retrieve and public these data. Data is updated weekly.

Content

So far, here you have the available contents:

• Player Data: You'll find all available player data from LaLiga with a huge amount of columns for these competitions: female first division, male first division and male second division. Each file is identified by SXX-YY at the beginning meaning the season XX-YY.

Acknowledgements

Thanks Python!

--- Original source retains full ownership of the source dataset ---

This dataset contains a set of statistics regarding the spanish first division teams.

Each field value has been computed as the mean for the last 30 games played, for the following statistics:

Possession

Passes

Tackles

Corners

Shots - Total

Shots - On target

Shots - Off target

Shots - Blocked

Shots - Outside Box

Shots - Inside Box

Fouls

Offsides

Yellow Card

Red Card

Penalties

Data has been obtained from https://playerstats.football

Dataset

This dataset was created by Alejandro Marcano Van Grieken

Released under Database: Open Database, Contents: Database Contents

Contents

It contains the following files:

Plug: Check out the Tableau Visualization I made using this data.

It's nothing groundbreaking, but it was satisfying and bit challenging to make. Oh, and it sometimes tends to break for seasons with number of clubs < 20. Shhhh.

Tip: Although you can use Python to scrape this data (as I did in first few attempts), a better and more feasible method in my opinion would be to use IMPORTHTMLtool in G-Sheets.

The dataset contains league table and match results of all seasons in the Spanish Football League, or LaLiga as it's known.

All seasons are included, starting from the inaugural 1929-30 to the recently wrapped 2019-20. Exceptions: 1936-37, 1937-38, 1938-39 due to the Spanish Civil War.

Data was scraped from Wikipedia, and is as error-proof as humanly possible.

La Liga, Spain’s top soccer competition, is widely considered the second biggest domestic soccer property in the world behind the Premier League, with its top sides Barcelona and Real Madrid the most followed and supported around the world. Having been able to ensure a completed season, a worst case scenario of a $1.16 billion loss has been avoided, but challenges still remain in ensuring La Liga’s product remains competitive and desirable to an international audience Read More

Context

I am fascinated by statistics and even more by Football, that's why I always wanted to have data available to analyze and draw conclusions. So I decided to put together a table with data obtained from different sources.

Content

The dataset contains statistical summary data by the end of each season from 2014 for the five big Leagues of Europe:

• La Liga
• English Premier League
• BundesLiga
• Serie A
• Ligue 1

The dataset is organized in 5 files:

Football_Data.csv contains data about each game. Transfer_team.csv - contains data with the information about transfers of each team. stats_per_game.csv - contains information for every team match. metric.txt - contains the textual description of each variable

Acknowledgements

Huge thanks for the team of understat.com for collecting this data and to Sergi Lehyki for scraped it!

Inspiration

Football data allows Data Scientist explore and learn by thousands of different questions. Let you feel free to analyse the data and try to discover new features or to confirm some hypothesis, like:

• First position teams tend to have more ball pressure.
• Last position teams always have less points than expected.
• Which League expend more money on transfers?.
• Who was the best champion since 2014 from all leagues?.
• Who was the team that had the most games in a row before the first draw?

And many more..

Contribute

If you notice a mistake or the results are being updated fast enough for your liking, you can fix that by submitting a pull request.

Context

The data set has been found from https://fbref.com/en/comps/12/La-Liga-Stats#stats_standard_squads::none . Due to COVID-19 all the Europe football game has been suspended so it will be benificary to predict the champion and who will go through to the Champions league and who will be relegate.

Content

In this Data Sets we have two different data sets of home and way. Now predict the champion of the remaining game.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Includes data for over 23000 matches and over 2 million events for those matches!

Content

This dataset contains information on 6 of the top European football/soccer leagues. I plan on updating this dataset weekly/biweekly with data for new matches played as well as gradually going backwards for game data as well.

(All data listed below is through roughly present during the current season.)

Data start years:

• English Premier League ** Game Data - 2001 ** Aggregate Stats - 2002 ** Tables - 2001

• Spanish La Liga ** Game Data - 2004 ** Aggregate Stats - 2002 ** Tables - 2000

• German Bundesliga ** Game Data - 2002 ** Aggregate Stats - 2002 ** Tables - 2000

• Italian Serie A ** Game Data - 2016 ** Aggregate Stats - 2001 ** Tables - 2000

• Dutch Eredivisie ** Game Data - 2018 ** Aggregate Stats - 2001 ** Tables - 2000

• French Ligue 1 ** Game Data - 2018 ** Aggregate Stats - 2002 ** Tables - 2002

Some notes: * Year as a column refers to the year a season started in. So if a match was played in January 2021, it's value for year would be 2020 because that season started in 2020. * Some older matches have no commentary, but they do have one row in events.csv to denote such

Acknowledgements

ESPN, as that's where this data is scraped from Image

Inspiration

• How do the leagues compare in things like goals per game and red cards per team per season?
• Which teams across the leagues foul/get fouled the most and the least per year?
• SkillCorner has some interesting data here that may be worth a bit of your time to check out.

ABSTRACT The aim of the paper is to elaborate an alternative measure to compare football leagues based on Accumulated Points Difference (APD). The sample includes eight seasons (2006-2007 to 2013-2014) from nine football leagues: German Bundesliga, Campeonato Brasileiro (Brazil), La Liga BBVA (Spain), French Ligue 1, Dutch Eredivise, English Premier League, Italian Serie A, Portuguese Primeira Liga and Russian Premier League. We have employed the ANOVA one way with Tukey post hoc to compare the results. We have confirmed the robustness of the model comparing it with two traditional measures: Herfindahl Index of Competitive Balance (HICB) and C4 Index of Competitive Balance (C4ICB). As a result, we have found that the Brazilian League was the most balanced tournament in this period and there are no statistical differences between European leagues.

Dataset

This dataset was created by Priyanti G

Contents

It contains the following files:

Snapshot img

Sports Analytics Market Size 2025-2029

The sports analytics market size is forecast to increase by USD 8.4 billion, at a CAGR of 28.5% between 2024 and 2029.

The market is witnessing significant growth, driven by the increasing adoption of cloud-based deployment solutions. This shift towards cloud-based technologies enables organizations to store and process large volumes of data more efficiently, facilitating real-time insights and informed decision-making. Additionally, the integration of wearable devices in sports is another key trend, providing teams and athletes with real-time performance data and analytics, leading to enhanced training and improved player safety. However, the market faces challenges, including the limited potential for returns on investment. The high cost of implementing and maintaining advanced analytics systems, as well as the need for specialized skills and resources, can deter smaller organizations from entering the market.
Furthermore, ensuring data privacy and security remains a significant challenge, particularly in light of the sensitive nature of sports data. To capitalize on market opportunities and navigate challenges effectively, companies must focus on offering cost-effective solutions, providing robust data security, and investing in talent development to meet the growing demand for sports analytics expertise.

What will be the Size of the Sports Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in technology and the increasing value placed on data-driven insights across various sectors. Game analytics and real-time data processing are revolutionizing team performance, enabling coaches to make informed decisions during games. Wearable sensors and biometric data are transforming athlete performance analysis, providing valuable insights into player conditioning and injury prevention. Sports sponsorship and marketing are leveraging data to optimize campaigns and enhance fan engagement. Data security and privacy are becoming paramount, with the growing use of sensitive biometric data. Sports ethics and regulation are also gaining importance, ensuring the ethical use of data and compliance with industry standards.

Sports broadcasting is being enhanced through data visualization and video analysis, providing viewers with a more immersive experience. Machine learning models and predictive analytics are being used to improve player scouting and talent identification. Sports research and education are benefiting from the wealth of data available, leading to new discoveries and advancements in sports science. Sports technology is driving innovation in sports equipment, sports medicine, and sports training. Data integration and processing are becoming more sophisticated, enabling more accurate performance metrics and coaching strategies. Sports law and governance are adapting to the changing landscape, ensuring fair play and compliance with regulations.

The market is a dynamic and ever-evolving ecosystem, with continuous innovation and applications across various sectors. The integration of data into sports is transforming the way teams and organizations operate, providing valuable insights and competitive advantages.

How is this Sports Analytics Industry segmented?

The sports analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Football
  Cricket
  Hockey
  Tennis
  Others


Solution

  Player analysis
  Team performance analysis
  Health assessment
  Fan engagement analysis
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By Type Insights

The football segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the increasing popularity of sports and the subsequent demand for in-depth analysis. Football, as the most widely followed sport, drives a substantial portion of this demand. Sports facilities, from domestic leagues such as the Champions League, English Premier League, and Spanish La Liga to international tournaments like the World Cup and European Championship, attract massive viewership. To cater to this demand, various companies and data suppliers have emerged, offering solutions in areas such as team performance, sports infrastructure, biometric data, player scouting, sports psychology, player tracking, sports equipment, sports medicine, sports management, game analyt

This dataset provides information about the number of properties, residents, and average property values for Three League Road cross streets in Natchitoches, LA.

I collected data from fifaindex.com for a thesis project. The dataset includes players and teams data from the EA Sports FIFA video game series, from FIFA12 to FIFA22. Some columns were dropped only from the team data (both teamyearly.csv and teamweekly.csv) due to inconsistent formatting used through the years.

The dataset only has data from the top five European leagues (Premier League, Serie A, Ligue 1, the Bundesliga, and La Liga), except for teamweekly.csv, which also has data from second divisions (of the same countries).

Scraper code: https://github.com/enricocattaneo/FIFA_WebScrapers

Sprint profile of professional football players and differences between playing positions.