Presentation about Role of Data Analytics & AI in Football
AI & Data Analytics have enabled faster & better decision making in the world of sports such as football. AI-powered algorithms can derive actionable insights that add even greater value to the players & coaching staff. They have unlocked many use cases that will be paramount for everyone involved in this beautiful game.
Use machine learning techniques to predict sporting events. Learn about how sports betting works and how to apply predictive analytics to gain a potential edge.
What would professional sports look like with AI referees and other smart tec...Entefy
Does the idea of watching sporting events with AI referees sound futuristic? It certainly might. But when you take a look around the world of professional sports—football, soccer, fencing, basketball—advanced technologies are already having an impact on the roles of referees, coaches, players, and fans.
In fact, “precursor” technologies that provide the sensory input data for yet-to-be-invented AI algorithms are already in use. In some sports, athletes’ uniforms feature wearable devices and refs are using smart technologies to call plays. Technology looks likely to have a serious impact on how games are played and watched.
This presentation highlights key points from our article about how AI and other smart technologies might impact the future of professional sports. These slides provide an overview of the systems in use today, the rapid implementation of new smart technologies, and what fully automated refereeing might look like.
For additional analysis and links to our background sources, read “What would the Super Bowl look like with AI referees and other smart technologies?" on our blog at https://blog.entefy.com/view/304/What-would-the-Super-Bowl-look-like-with-AI-referees-and-other-smart-technologies.
Use machine learning techniques to predict sporting events. Learn about how sports betting works and how to apply predictive analytics to gain a potential edge.
What would professional sports look like with AI referees and other smart tec...Entefy
Does the idea of watching sporting events with AI referees sound futuristic? It certainly might. But when you take a look around the world of professional sports—football, soccer, fencing, basketball—advanced technologies are already having an impact on the roles of referees, coaches, players, and fans.
In fact, “precursor” technologies that provide the sensory input data for yet-to-be-invented AI algorithms are already in use. In some sports, athletes’ uniforms feature wearable devices and refs are using smart technologies to call plays. Technology looks likely to have a serious impact on how games are played and watched.
This presentation highlights key points from our article about how AI and other smart technologies might impact the future of professional sports. These slides provide an overview of the systems in use today, the rapid implementation of new smart technologies, and what fully automated refereeing might look like.
For additional analysis and links to our background sources, read “What would the Super Bowl look like with AI referees and other smart technologies?" on our blog at https://blog.entefy.com/view/304/What-would-the-Super-Bowl-look-like-with-AI-referees-and-other-smart-technologies.
Using Data Science to grow games / Robert Magyar (SuperScale)DevGAMM Conference
- How did we double the profits of a game played by 160 million players with Machine Learning?
- What are the challenges of everyday use of ML in mobile games?
- We'll show you examples of how we've doubled the profits of the Hill Climb Racing 2 mobile game with cloud ML solutions.
- We will also show how we predict the long-term success of mobile gaming marketing campaigns in our portfolio
- We will take a closer look at prediction methods, ML cloud pipeline and other Data Science points of interest
Artificial Intelligence (AI) technologies are evolving fast and growing increasingly critical for a sporting organisation’s ability to:
- win games;
- improve coaches and players;
- manage internal operations; and
- grow, serve and retain their fans.
The imperative exists for sporting teams not to just adopt a singular AI technology but rather to have access to an arsenal of AI technologies that will improve their ability to generate and act on critical insights; whether it’s fan engagement, talent identification, pre-game preparation or in-game real-time facilitation.
This paper explores some of the specific AI applications being experimented across sporting codes, learnings from other sports and the current AI market. I also pose some ethical questions the sporting industry need to consider before introducing AI technologies into their codes.
FC Barcelona Development Presentation (Notes)Paul Cammarata
Here are notes from a fantastic presentation given by Joan Villa, Head of Methodology with FC Barcelona. The notes are prepared by Coach Alejandro Perez and go over "A New Idea" detailing Barcelona's comprehensive development model.
Be sure to follow Alejandro on Twitter: @JandroPerez95
-The Coaching Journey (www.TheCoachingJourney.org)
Big Data and Analytics are playing an important role to improve the performance of game and players on the field, off the field through predictive decisions.
Joyixir is an indie game studio. This is our pitch deck to investors and we present ourselves with this file. Take a look and don't hesitate to ask anything. Maybe it can help you too.
Good Gaming Inc is a development stage company. It owns and operates the e-Sports tournament and social networking platform. It also provides videos, blogs, and articles authored by world-renowned professional gamers. The company focuses on hosting multiple games online that subscribers can play for free or for fees depending on player level. It also offers social networking functionality, which helps gamers to interact, track each other and communicate.
In the last few years, we have witnessed a true revolution in the video-game industry, as both traditional video-game platforms and emerging mobile games have become always connected to the Internet. This has contributed to widen the audience for video games (casual gamers) and to the appearance of new economic models (in-app purchases, free-to-play) that are gaining more and more importance in a sector traditionally monetized by expensive one-time purchases or subscriptions.
More importantly, this recent paradigm shift allows game developers to collect a huge amount of data in real time while maintaining an active relationship with the players. This has created a broad range of new challenges and opportunities for both data science research and business applications, as demonstrated by the quickly growing number of job openings for data scientists in game companies. To fully take advantage of this new scenario, it is paramount to develop adequate statistical and learning methods that model and predict player behavior, scale to large datasets and allow an intuitive visualization of the results.
In this talk, I will survey the state-of-the-art of Data Science in the mobile game industry. First, I will present a general summary of the main techniques to predict player behavior, concentrating on those learning methods that help to reduce user attrition, i.e. churn, which is decisive to increase player retention and raise revenues.
Then, I will discuss these techniques from the viewpoint of Game Data Science as a Service. The goal of Silicon Studio is to democratize Game Data Science. Hence, I will show how the proposed methods can make predictions in an operational business environment and easily adapt to different kinds of games and players—namely, to different data distributions. I will focus on flexible techniques that do not need previous manipulation of the data and are able to deal efficiently with the temporal dimension of the churn-prediction problem.
Sports Analytics: Market Shares, Strategy, and Forecasts, Worldwide, 2015 to ...Shrikant Mandlik
The 2015 study has 472 pages, 177 tables and figures. Worldwide markets are poised to achieve significant growth as the cloud computing for utility infrastructure and the tablets and smart phone communications systems make training information more cogent and more available, remaking all sporting everywhere.
Information services will leverage automated process to leverage cloud computing: services The value of sports analytics is the predictive capabilities provided. The best sports teams are the ones using the power of real-time information to their advantage. What to measure? What real time information is the best? Can the players game the analytics systems?
Lets start with the story of Babe Ruth. The “Babe” used to come to every at bat with the desire to win the game. So early in the game, aware that at the end of the game it would fall on him to win the game, the “Babe” would deliberately strike out on pitches that he really could hit. Later in the game, the pitcher would remember the pitches that had gotten the “Babe” out and “Babe Ruth” could hit with ease, winning the game defying the statisticians.
So, Babe Ruth used sports analytics in the 1930’s in reverse, hoping to entice the pitcher to throw that very pitch he could hit in a tight situation later in the game. His very success illustrates that in sports analytics sophistication is needed. For sports analytics to track Babe Ruth, it would have been necessary to look at the pitches he could hit at the end of the game, not just everything that came at him. How sophisticated is that? You have to know your players to do good sports analytics.
Babe Ruth is at the center of one of the sad stories of sporting in Boston. The Boston Red Sox baseball team, in 2003, had not won a world series since Babe Ruth was sold to New York, the so called “Curse of the Bambino.” John Henry, a financial analytics wizard came along and purchased the Boston Red Sox along with other partners and he took the team to three world series using sports analytics as the dominant force for running the team and building fan enthusiasm. Sports become the model for predictive business decision making. Business has been reorganized among teams, inspired by sports. Analytics, developed by businesses are finding innovative use in sports, leading to models for
business to organize and manage teams.
Sports analytics market driving forces relate to the ability to improve winning percentages and decrease the cost of paying players. By implementing metrics functions that describe how to put together a winning team without a very high payroll, sports analytics provide a winning edge to team management. Analytics are used to figure out how a team can improve fan appeal.
Sports analytics are used for creating fantasy leagues, giving sports fantasy players access to statistics that enhances their play of the game. It is used to improve scouting, to detect new player unusual talent and evaluate
Using Data Science to grow games / Robert Magyar (SuperScale)DevGAMM Conference
- How did we double the profits of a game played by 160 million players with Machine Learning?
- What are the challenges of everyday use of ML in mobile games?
- We'll show you examples of how we've doubled the profits of the Hill Climb Racing 2 mobile game with cloud ML solutions.
- We will also show how we predict the long-term success of mobile gaming marketing campaigns in our portfolio
- We will take a closer look at prediction methods, ML cloud pipeline and other Data Science points of interest
Artificial Intelligence (AI) technologies are evolving fast and growing increasingly critical for a sporting organisation’s ability to:
- win games;
- improve coaches and players;
- manage internal operations; and
- grow, serve and retain their fans.
The imperative exists for sporting teams not to just adopt a singular AI technology but rather to have access to an arsenal of AI technologies that will improve their ability to generate and act on critical insights; whether it’s fan engagement, talent identification, pre-game preparation or in-game real-time facilitation.
This paper explores some of the specific AI applications being experimented across sporting codes, learnings from other sports and the current AI market. I also pose some ethical questions the sporting industry need to consider before introducing AI technologies into their codes.
FC Barcelona Development Presentation (Notes)Paul Cammarata
Here are notes from a fantastic presentation given by Joan Villa, Head of Methodology with FC Barcelona. The notes are prepared by Coach Alejandro Perez and go over "A New Idea" detailing Barcelona's comprehensive development model.
Be sure to follow Alejandro on Twitter: @JandroPerez95
-The Coaching Journey (www.TheCoachingJourney.org)
Big Data and Analytics are playing an important role to improve the performance of game and players on the field, off the field through predictive decisions.
Joyixir is an indie game studio. This is our pitch deck to investors and we present ourselves with this file. Take a look and don't hesitate to ask anything. Maybe it can help you too.
Good Gaming Inc is a development stage company. It owns and operates the e-Sports tournament and social networking platform. It also provides videos, blogs, and articles authored by world-renowned professional gamers. The company focuses on hosting multiple games online that subscribers can play for free or for fees depending on player level. It also offers social networking functionality, which helps gamers to interact, track each other and communicate.
In the last few years, we have witnessed a true revolution in the video-game industry, as both traditional video-game platforms and emerging mobile games have become always connected to the Internet. This has contributed to widen the audience for video games (casual gamers) and to the appearance of new economic models (in-app purchases, free-to-play) that are gaining more and more importance in a sector traditionally monetized by expensive one-time purchases or subscriptions.
More importantly, this recent paradigm shift allows game developers to collect a huge amount of data in real time while maintaining an active relationship with the players. This has created a broad range of new challenges and opportunities for both data science research and business applications, as demonstrated by the quickly growing number of job openings for data scientists in game companies. To fully take advantage of this new scenario, it is paramount to develop adequate statistical and learning methods that model and predict player behavior, scale to large datasets and allow an intuitive visualization of the results.
In this talk, I will survey the state-of-the-art of Data Science in the mobile game industry. First, I will present a general summary of the main techniques to predict player behavior, concentrating on those learning methods that help to reduce user attrition, i.e. churn, which is decisive to increase player retention and raise revenues.
Then, I will discuss these techniques from the viewpoint of Game Data Science as a Service. The goal of Silicon Studio is to democratize Game Data Science. Hence, I will show how the proposed methods can make predictions in an operational business environment and easily adapt to different kinds of games and players—namely, to different data distributions. I will focus on flexible techniques that do not need previous manipulation of the data and are able to deal efficiently with the temporal dimension of the churn-prediction problem.
Sports Analytics: Market Shares, Strategy, and Forecasts, Worldwide, 2015 to ...Shrikant Mandlik
The 2015 study has 472 pages, 177 tables and figures. Worldwide markets are poised to achieve significant growth as the cloud computing for utility infrastructure and the tablets and smart phone communications systems make training information more cogent and more available, remaking all sporting everywhere.
Information services will leverage automated process to leverage cloud computing: services The value of sports analytics is the predictive capabilities provided. The best sports teams are the ones using the power of real-time information to their advantage. What to measure? What real time information is the best? Can the players game the analytics systems?
Lets start with the story of Babe Ruth. The “Babe” used to come to every at bat with the desire to win the game. So early in the game, aware that at the end of the game it would fall on him to win the game, the “Babe” would deliberately strike out on pitches that he really could hit. Later in the game, the pitcher would remember the pitches that had gotten the “Babe” out and “Babe Ruth” could hit with ease, winning the game defying the statisticians.
So, Babe Ruth used sports analytics in the 1930’s in reverse, hoping to entice the pitcher to throw that very pitch he could hit in a tight situation later in the game. His very success illustrates that in sports analytics sophistication is needed. For sports analytics to track Babe Ruth, it would have been necessary to look at the pitches he could hit at the end of the game, not just everything that came at him. How sophisticated is that? You have to know your players to do good sports analytics.
Babe Ruth is at the center of one of the sad stories of sporting in Boston. The Boston Red Sox baseball team, in 2003, had not won a world series since Babe Ruth was sold to New York, the so called “Curse of the Bambino.” John Henry, a financial analytics wizard came along and purchased the Boston Red Sox along with other partners and he took the team to three world series using sports analytics as the dominant force for running the team and building fan enthusiasm. Sports become the model for predictive business decision making. Business has been reorganized among teams, inspired by sports. Analytics, developed by businesses are finding innovative use in sports, leading to models for
business to organize and manage teams.
Sports analytics market driving forces relate to the ability to improve winning percentages and decrease the cost of paying players. By implementing metrics functions that describe how to put together a winning team without a very high payroll, sports analytics provide a winning edge to team management. Analytics are used to figure out how a team can improve fan appeal.
Sports analytics are used for creating fantasy leagues, giving sports fantasy players access to statistics that enhances their play of the game. It is used to improve scouting, to detect new player unusual talent and evaluate
Discovering The Best Free Football Scouting Software360 Scouting
Professional football clubs frequently lean on paid scouting software, but these tools can be financially out of reach for smaller clubs and scouts (or fantasy football enthusiasts). Fortunately, there are outstanding free alternatives. In this presentation, we share six of the best options for free football scouting software.
End-to-End Digital Transformation Consulting Services. We help businesses build technology, Shape Ideas, Scale Faster. We are engineer who create scalable, high performance digital solution to solve business challenges for
startups, entrepreneurs and enterprise.
Our vision is to create value for our clients, people, shareholders and partners. Our core value of shared success will guide us to become a leading service provider in the information technology space.
Digital transformation, the use of Data, and AI have been transforming businesses in every industry. Sports are no exception. Football has been implementing new technology in order to guarantee succes and better engage with fans.
Scoring with Data: The Role of Football APIs in the Modern GameDataSportsGroup
Explore the transformative influence of data analytics in the world of football. It delves into the essential role played by football data APIs and feeds, highlighting their impact on various aspects of the sport. From enhancing fan engagement to providing valuable insights for teams and coaches, data analytics is reshaping football and even powering fantasy football experiences. Discover how this data-driven revolution is changing the game we love.
The Evolution and Power of Football Data Feeds.pdfDataSportsGroup
Discover how innovations in football data collection and delivery have transformed the way we experience the game. Explore the journey from basic stats to live play-level data feeds, and learn how developers and analysts are leveraging this wealth of information through APIs. Uncover the myriad applications, from predictive injury models to augmented reality experiences, that have become possible with access to real-time, granular football data. Dive into the future, where data quality, speed, and new technologies promise even richer insights for fans, teams, and analysts. Whether you're a stats enthusiast, bettor, fantasy player, or developer, this blog unveils the immense potential of modern football data.
In this project, we will be analyzing the ways to increase the fan satisfaction by making up a strong offensive team of soccer players without having much impact on the revenue. By looking at the dataset, it is conspicuous that acquiring excellent players and winning games with them have an impact on the fan loyalty and the increase in revenue. For better results, the data sets need to be integrated, fed to the data warehouse for processing to extract information that will help in making a physical model to be presented for further knowledge. To achieve this goal, we have planned to start with making dimension tables and fact tables that will provide some insight on the parameters affecting the fan satisfaction without largely affecting the revenue.
Small budget coaches get big leagues capabilities. Innovative basketball analytics available for every coach in the world:
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The Essential Role of Data Feeds in Modern FootballDataSportsGroup
The beautiful game of football has come a long way from its early days of leather balls and sparse statistics. Today, football is a data-driven sport. Teams utilize advanced analytics and technology to gain every possible advantage, while fans crave up-to-the-second stats and insights into their favorite players and clubs. This increasing data dependence has created a huge demand for football data feeds packed with real-time information.
Data analytics mostly involves studying data trends over a given period, and then extracting useful information from these trends.
Why Is Data Analytics Important?
More precise decision making process: Data analytics helps organizations make more accurate decisions based on the insights gotten from data trends over time.
For example, a company selling different products can figure out what time of the year different products sell higher. This will enable them boost production of such products at the required time.
A better decision making process will eliminate the need for guess work, and minimize losses and avoidable risks.
Improved customer satisfaction: When you're able to serve customers, you retain them and keep business going. Insights gotten from data analytics can help you understand exactly what your customers want and when to act.
Data analytics also enables businesses to identify their target audience easily.
Improved business strategy: Data analytics helps organizations channel their resources towards the most efficient strategies.
Performance evaluation: Data analytics can help organizations evaluate how well or badly they've performed over a specified period. This will enable them make important decisions for the future of the organization.
Although the points listed above seem to be from the business point of view, that's not the only industry where data analytics is important.
You can see data analytics being used in healthcare, education, agriculture, and so on.
Types of Data Analytics
There are mainly four different types of data analytics:
Descriptive analytics: This type of analytics has to do with what happened with analyzed data over a specified period of time.
Diagnostic analytics: Diagnostic data analytics shows the "why" in a data trend. This involves having a deeper look into why certain patterns were present in the data.
Predictive analytics: The goal here is to foretell what is expected to happen in the future based on the outcomes of analyzed data over time.
Prescriptive analytics: In prescriptive analytics, the results from data analysis is used to make recommendations on what to do next.
What Is the Difference Between Data Analysis and Data Analytics?
You'll come across different definitions of data analytics and data analysis.
Some sources would define data analytics and data analysis as the same. Others would use them interchangeably.
Although, they are closely related, these terms have slightly different meanings. They are similar because they aid in the decision making process.
What Is Data Analysis?
Data analysis is the process of studying what has happened in the past in a dataset. There is no need to extend this definition.
Data analysis studies the why and how of data trends. Yes, it involves data collection, organization, and "analysis".
"How did the users respond to a new feature?".
"Why did the rate of purchase of a product fall during a particular period?".
Data analysts can make use o
The world of sports has been transformed by the digital revolution. Real-time data and statistics are now an integral part of how fans, teams, leagues, and media outlets experience and analyze sports. As a professional sports writer with over 10 years of experience, I've witnessed firsthand how sports data has evolved and enabled new possibilities for enhancing the sports experience.
The Top 10 Cutting-Edge Sports Technology Solution Providers, 2019Mirror Review
In the sports industry, a well-analyzed data is a great resource that can be used to increase revenue and enhance audience engagement. Moreover, data has capabilities to enhance the experience of professional sports for all parties involved. Rather than relying on past scenarios and intuition, sports professionals can scrutinize the data that tells the real story to help with every aspect of the game—from player recruitment to fan engagement.
https://www.mirrorreview.com/the-top-10-cutting-edge-sports-technology-solution-providers-2019/
The world of football is undergoing a tech-driven revolution, transforming how fans engage with the game. Football Data Feeds, APIs, and Widgets are at the forefront of this evolution, offering a treasure trove of real-time information, insights, and interactive experiences. These innovations are deepening the connection between fans and football, fueling informed fandom, and enriching the viewing experience. Behind it all are Football Data Feed Providers, ensuring data accuracy and up-to-date information. As technology continues to unite football enthusiasts worldwide, it's clear that the beautiful game's bond with its fans is stronger than ever, promising even more exciting possibilities in the future.
Public "Deck" about MYagonism project, a 360° software/hardware service for basketball coaches.
With MYagonism small budget coach gets big leagues capabilities.
Thanks to our innovative algorithms and analytics sports world can find real hidden-value of every player.
MYagonism has been selected by the NCAA for season 2016/17 and presented at the MIT Sloan Sports Analytics conference in 2016.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. ROLE OF DATA ANALYTICS
& AI IN FOOTBALL
Under The Guidance Of:
Dr. Bam Bahadur Sinha &
Dr. Uma Sheshadri
Submitted By:
TERMINALX 1.0
2. MINIMIZES INJURY
& PREDICTING &
IMPROVING
RECOVERY TIME
01
IMPROVING &
BOOSTING
PLAYING SKILLS OF
PLAYER BY GAME
AWARENESS
02
BETTERVIEWING
EXPERIENCE FOR
FANS
ETC.
03
3. DECIDING THE PRESSURE INSIDE THE FOOTBALL &
ALL IT’S MANUFACTURING TO IMPROVE PLAYER’S
GAME
4. MOVEMENT OF BODY IS SUCH A WAY TO
REDUCE AIR RESISTANCE AND MINIMIZE
LEVEL OF INJURY
5. THE ANGLE WITH WHICH BALL SHOULD BE
KICKED IN ORDER TO PROVIDE MAX. SPEED
AND CORRECT DIRECTION
10. RISE OF AI & DATA ANALYTICS
•In recent years,AI & Machine Learning have seen a lot of
traction in the footballing world.
•Both of them came to be associated with football for predicting
outcomes of matches
•By analyzing big data, machine learning & algorithms have the
capability to predict the success & failure of football games
11. SELECTION OF RIGHT TEAM
• Let us go with an example---
• A German based AI company help a Bundesliga team win matches by
selecting right players.The company achieved this by building an AI tool
that extracted insights from unstructured data & putting them in a single
dashboard.IBM’s WATSON AI was leveraged by the company for
providing a deep perspective on players.Watson was trained to
understand scouting reports & draw out the most relevant details.
12. CONCLUSION
• AI & Data Analytics have enabled faster & better decision making in the
world of sports such as football.AI-powered algorithms can derive
actionable insights that add even greater value to the players & coaching
staff.They have unlocked many use cases that will be paramount for
everyone involved in this beautiful game.
• The future seems bright for both of them along with machine learning in
football. It will be exciting to see the extent to which it is leveraged by
teams to devise match winning scenarios.
