Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

Key Benefits:

Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

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API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

Use Cases:

Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

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Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

NFL Football Stats

My family has always been serious about fantasy football. I've managed my own team since elementary school. It's a fun reason to talk with each other on a weekly basis for almost half the year.

Ever since I was in 8th grade I've dreamed of building an AI that could draft players and choose lineups for me. I started off in Excel and have since worked my way up to more sophisticated machine learning. The one thing that I've been lacking is really good data, which is why I decided to scrape pro-football-reference.com for all recorded NFL player data.

From what I've been able to determine researching, this is the most complete public source of NFL player stats available online. I scraped every NFL player in their database going back to the 1940s. That's over 25,000 players who have played over 1,000,000 football games.

The scraper code can be found here. Feel free to user, alter, or contribute to the repository.

The data was scraped 12/1/17-12/4/17

Shameless plug

When I uploaded this dataset back in 2017, I had two people reach out to me who shared my passion for fantasy football and data science. We quickly decided to band together to create machine-learning-generated fantasy football predictions. Our website is https://gridironai.com. Over the last several years, we've worked to add dozens of data sources to our data stream that's collected weekly. Feel free to use this scraper for basic stats, but if you'd like a more complete dataset that's updated every week, check out our site.

The data is broken into two parts. There is a players table where each player has been assigned an ID and a game stats table that has one entry per game played. These tables can be linked together using the player ID.

Player Profile Fields

• Player ID: The assigned ID for the player.
• Name: The player's full name.
• Position: The position the player played abbreviated to two characters. If the player played more than one position, the position field will be a comma-separated list of positions (i.e. "hb,qb").
• Height: The height of the player in feet and inches. The data format is

Upload from https://github.com/fantasydatapros/data

The global fantasy football market is experiencing robust growth, driven by increasing smartphone penetration, readily available internet access, and the ever-growing popularity of professional football leagues worldwide. The engagement fostered by fantasy football platforms, which blend competition with strategic decision-making, fuels user loyalty and continuous engagement, translating into significant revenue streams for operators. The market is segmented by application (individual vs. team competitions) and access method (mobile phone vs. computer), with mobile platforms witnessing faster adoption due to convenience and accessibility. Major players like FanDuel, DraftKings, and ESPN dominate the market, leveraging their established brand recognition and sophisticated platforms. However, smaller, niche platforms are also emerging, catering to specific user preferences or offering specialized features. The North American market currently holds the largest share, fueled by the immense popularity of American football, but significant growth is anticipated in other regions, particularly in Europe and Asia, as the sport’s global reach expands and digital infrastructure improves. The competitive landscape is dynamic, with ongoing innovation in platform features, scoring systems, and marketing strategies to attract and retain users. The forecast period from 2025 to 2033 projects substantial market expansion, driven by factors such as enhanced user experience, increased integration of data analytics, and the growing adoption of esports-like features within fantasy football platforms. This will likely lead to an increase in advertising revenue and potential partnerships with leagues and merchandise providers. However, regulatory hurdles surrounding gambling, data privacy concerns, and the potential for user fatigue represent potential restraints. Overcoming these challenges requires ongoing platform development focusing on security, transparency, and responsible gaming practices. The market's success hinges on continuing innovation and adapting to evolving user preferences while navigating the regulatory landscape to maintain sustainable growth. We estimate the CAGR (based on a reasonable assumption) to be in the range of 10-15% for the forecast period, leading to significant market expansion.

The Fantasy Football market has evolved into a thriving multi-billion-dollar industry, captivating millions of fans worldwide. As of 2023, the market size is estimated to be valued at over $8 billion, reflecting a consistent growth trajectory fueled by the increasing popularity of American football and the advanceme

As per Cognitive Market Research's latest published report, the Global Daily Fantasy Sports market size was USD 5,992.74 Million in 2017 and it is forecasted to reach USD 14,875.92 Million by 2029. Daily Fantasy Sports Industry's Compound Annual Growth Rate will be 8.95% from 2023 to 2030.

The North America Daily Fantasy Sports market size will be USD 5,022.11 Million by 2029.

Market Drivers For Daily Fantasy Sports

Growing adoption of smartphones coupled with rising internet connectivity

Over the last few years, there is a rapid increase in smart device adoption and internet penetration. Smart devices offer flexibility to customers on a smartphone, laptop, desktop, or tablet. With these devices, users are simply able to access several daily fantasy sports apps and websites.

According to a study, the number of unique mobile internet users in 2020 was 4.28 billion, and more than 90% of the worldwide internet population has access to the internet through a mobile device. Until March 2021, the internet penetration rate in Asia was at 62%, 88% in Europe, and 90% in North America.

Similarly, according to the study, the current global smartphone user population is 6.648 billion, which indicates that 83.72 percent of the world’s population possesses a smartphone. This statistic has increased significantly from 2016, when there were just 3.668 billion users, accounting for 49.40 percent of the world’s population at the time.

Furthermore, the usage of tablets, laptops, and desktops is also raising. According to the Australian Bureau of Statistics, desktop or laptop computers are used by 91% of connected households.

As technology advances, various organizations are releasing numerous daily fantasy games that can play online. This enables several people to entertain themselves as well as to earn money. In this app or website, users can play a wide range of fantasy games, including cricket, hockey, football, basketball, and kabaddi. This promotes market expansion.

According to data shared by Indian Super League (ISL) its fantasy football players hail from India, Nepal, Bangladesh, and the UK, with 86% of the traffic coming from mobile phones. As a result, the growing adoption of smart devices coupled with rising internet connectivity drives the growth of the daily fantasy sports market.

Rising number of daily fantasy sports players

As online gaming and associated activities gain traction, the online fantasy sports market is expected to see a considerable increase in income. The popularity of fantasy games has surged with the introduction of various applications and websites. Daily fantasy games, in particular, are gaining popularity, and many individuals are drawn to them. This increases the popularity of daily fantasy sports.

Daily fantasy games are a popular type of online fantasy gaming in which users pay an entrance fee to compete for cash rewards based on the performance of professional athletes whom users choose and then track in their respective professional sports events.

People are drawn to daily fantasy games for a variety of reasons. The primary reason for this is that players can win real money. According to data, one-third (34%) of urban Indians have played online fantasy sports for money in June 2021. There is real money on the line, and anybody has a chance to win. Some players even make a full-time career by joining competitions. It's difficult to say how much profit the top fantasy sports players make, but there's evidence that they might be earning at least $10,000 every week.

As a result, many players want to participate in daily fantasy games. According to the Fantasy Sports and Gaming Association, approximately 59 million individuals in the United States and Canada participate in fantasy sports. It also claims that 20 million individuals in India play fantasy games, with the amount expected to rise to 150 million by 2022. According to the Fantasy Sports Trade Association, the number of fantasy sports participants has increased from 500,000 in 1988 to 59.3 million in 2017, with the business being worth $7.22 billion (FSTA).

Similarly, key players are participating in a variety of measures to increase player numbers. For instance, to increase its appeal to women, Disney introduced a fantasy

league game for ABC's "Bachelorette" show in 2017. More than 700,000 individuals have playe...

Daily Fantasy Games Market Outlook

The global daily fantasy games market size was valued at approximately USD 20 billion in 2023 and is projected to reach around USD 70 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.2% during the forecast period. The growth of this market is primarily driven by the increasing penetration of the internet and smartphones, which has facilitated greater access to online gaming platforms.

One of the prominent growth factors in the daily fantasy games market is the rising adoption of smartphones and high-speed internet services. With improvements in network infrastructure, more users are able to access fantasy sports platforms anytime and anywhere, thereby boosting market growth. Additionally, the increasing popularity of major sports leagues and tournaments globally has driven fan engagement, which has consequently increased the user base for daily fantasy games. Enhanced fan experiences through fantasy sports have become a significant revenue stream for sports leagues and teams, further fueling market growth.

Another significant factor contributing to market expansion is the growing acceptance of daily fantasy sports as a legal and regulated form of gaming in several regions. Governments and regulatory bodies have started recognizing the potential revenue generation from this industry, leading to more favorable regulations. This regulatory support has attracted investments from major corporations and venture capitalists, encouraging innovation and development within the sector. Additionally, partnerships between daily fantasy sports platforms and traditional sports organizations have legitimized and popularized these platforms, allowing them to reach a broader audience.

Technological advancements, such as artificial intelligence (AI) and machine learning (ML), are also playing a pivotal role in the growth of the daily fantasy games market. These technologies are being used to enhance user experience by providing personalized recommendations, real-time data analytics, and accurate predictive models. The integration of blockchain technology to ensure transparency and security in transactions and data handling is another technological trend driving market growth. Furthermore, the increasing popularity of esports and virtual sports is expanding the horizon for fantasy sports, offering new avenues for user engagement and monetization.

From a regional perspective, North America currently holds the largest share in the daily fantasy games market, owing to the high popularity of sports like football and basketball, coupled with favorable legal frameworks. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period due to the burgeoning internet user base, rising disposable incomes, and increasing interest in sports and online gaming. Europe also shows significant potential, driven by the popularity of soccer and regulatory advancements that are gradually aligning with North American standards.

Online Football Games have become a significant component of the daily fantasy games market, particularly with the rise of digital platforms that allow fans to engage with their favorite sports in innovative ways. These games offer a dynamic and interactive experience, enabling users to create virtual teams and compete against others based on real-world football matches. The accessibility of online platforms has democratized participation, allowing fans from all over the world to engage in fantasy football. This has not only increased fan interaction but also provided a new revenue stream for sports organizations and fantasy sports platforms alike. The integration of real-time data and analytics in online football games enhances user engagement, making them a popular choice among fantasy sports enthusiasts.

Game Type Analysis

The daily fantasy games market can be segmented by game type into football, baseball, basketball, hockey, golf, soccer, and others. Football, particularly American football, holds a significant share in the market, primarily driven by the popularity of the National Football League (NFL) in North America. The NFL's massive fan base translates into a substantial user base for daily fantasy football games. The integration of fantasy football with real-time data and analytics enhances user engagement, making it the most popular game type in this segment.

Baseball is another key segment in the daily fa

📂 About This Dataset This dataset combines detailed player performance statistics from WhoScored with team and player meta-data from Transfermarkt. It covers over 1,500 players from top European leagues and includes metrics such as:

Expected Goals (xG) & xG per 90

Tackles, Interceptions, Key Passes, Assists

Pass Accuracy, Crosses, Long Balls

Total Minutes Played & Formations

Player Age, Height, Positioning

🧩 Use Cases Player Rating Prediction

Team Formation Impact Analysis

Identifying Underrated Players via xG vs. Goals

Clustering Players by Style or Efficiency

Fantasy Football Recommendations

🏗️ Data Sources WhoScored.com: Player match stats, tactical analysis.

Transfermarkt.com: Player bio, team formations.

📊 Features Snapshot 32 Columns

Over 20 numerical performance metrics

Cleaned, ready-to-analyze format

Small number of missing values (mostly in passing stats)

Snapshot img

Fantasy Sports Market Size 2025-2029

The fantasy sports market size is forecast to increase by USD 10.13 billion, at a CAGR of 7.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing popularity of dedicated apps and the utilization of these sports technology for brand promotion. The proliferation of user-friendly apps has made accessing and participating in fantasy sports more convenient than ever before, leading to a surge in demand. Brands recognize the potential of this trend, using fantasy sports as a marketing tool to engage consumers and build brand loyalty. However, the future of fantasy sports remains uncertain, with concerns surrounding the potential negative impacts on health and well-being. The addictive nature of fantasy sports gaming can lead to excessive time spent on digital platforms, negatively affecting productivity and overall well-being.
Companies must navigate these challenges by implementing responsible gaming practices and promoting a healthy balance between digital engagement and real-life activities. By addressing these concerns and continuing to innovate, fantasy sports providers can capitalize on the market's potential and maintain a strong competitive edge.

What will be the Size of the Fantasy Sports 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.
Request Free Sample

The market continues to evolve, with dynamic market activities unfolding across various sectors. Season-long fantasy sports platforms offer users customizable league settings, head-to-head matches, and real-time game data, enabling a more engaging user experience. Waiver wire transactions and player projections are integral components, requiring continuous analysis from fantasy sports analysts and machine learning algorithms. Fantasy sports communities foster interaction through player chat, news feeds, and podcasts, creating a vibrant ecosystem. Draft strategies and auction drafts vary, with salary cap leagues and private leagues offering unique challenges. Advertising revenue and affiliate marketing provide monetization opportunities, while privacy policies ensure user data security.

Daily fantasy sports (DFS) operators employ advanced statistical analysis and probability calculations to offer real-time contests, further fueling the market's growth. API integrations and data modeling enable seamless data access, while customer support and commissioner tools cater to league management needs. Freemium models, subscription models, and public leagues cater to diverse user preferences, with mobile applications and web applications ensuring accessibility. Risk assessment and lineup optimization strategies are essential for success, while injury reports and expert analysis inform user decision-making. Fantasy sports platforms continue to integrate social media, offering a more immersive experience. News feeds and custom leagues provide users with personalized content, further enhancing engagement.

How is this Fantasy Sports Industry segmented?

The fantasy sports 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.

Product

  Fantasy soccer
  Fantasy baseball
  Fantasy basketball
  Fantasy football
  Others


Application

  Individual Competition
  Team Competition


Demographic

  Under 25 Years
  Between 25 and 40 Years
  Above 40 Years


Dietary Preference

  Vegan
  Gluten-Free
  Keto


Target Audience

  Busy Professionals
  Health Enthusiasts


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Product Insights

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

In the realm of fantasy sports, soccer holds a significant position, allowing participants to build teams comprised of real-life soccer players and earn points based on their on-field statistics or perceived value. Soccer fantasy leagues, such as Draft Fantasy Football, McDonald FIFA World Cup Fantasy, Fantasy Premier League, and UEFA Champions League Fantasy Football, attract a massive following due to the universal appeal of soccer. These platforms offer users flexibility to manage their rosters, making unlimited transfers before the season's commencement. Fantasy sports communities thrive on player chat, league settings, and head-to-head matches, fostering a competitive and engaging environment.

Season-long fantasy sports and daily fantasy

NFL Rushing stats between 2000 and 2016. For regular season only. Extracted from FoxSports by Christopher Hayles for Fantasy Football purposes.

Sports Data Analytics Service Market Outlook

The global sports data analytics service market size was valued at approximately $2.3 billion in 2023 and is projected to reach around $6.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.6% during the forecast period. This robust growth is primarily driven by increased investments in sports technology, the rising importance of data-driven decision-making in sports, and the growing adoption of advanced analytics to enhance player performance and fan engagement.

The surge in the adoption of sports data analytics is attributed to the increasing competitive nature of sports, where teams and individual athletes are leveraging data to gain a strategic edge. Data analytics provides insights into player performance, injury risks, and optimal training regimens, which can significantly impact the outcomes of games and overall team performance. This, coupled with the rising investments in sports technology, is propelling the market growth. Additionally, the growing popularity of fantasy sports and sports betting has further fueled the demand for real-time data analytics to make informed decisions.

Another significant growth factor is the rising focus on enhancing fan engagement and experience. Sports organizations are increasingly using data analytics to understand fan preferences, behavior, and sentiment. This information is crucial for tailoring marketing strategies, improving fan interactions, and ultimately increasing revenue from ticket sales, merchandise, and digital platforms. The integration of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), is also enabling more sophisticated data analysis, driving further growth in the market.

Performance Analytics plays a crucial role in the sports data analytics service market, offering teams and athletes the ability to delve deeper into their performance metrics. By leveraging performance analytics, sports organizations can track and analyze various aspects of athletic performance, from speed and agility to endurance and skill execution. This data-driven approach not only aids in identifying areas for improvement but also helps in crafting tailored training programs that enhance overall performance. The integration of performance analytics into sports strategies allows for a more comprehensive understanding of both individual and team dynamics, ultimately leading to more informed decision-making and competitive advantages on the field.

Moreover, the healthcare and fitness tracking aspects of sports data analytics are gaining traction. With a growing emphasis on athlete health and well-being, sports teams are using data analytics to monitor players' physical conditions, predict injuries, and design personalized training programs. This proactive approach not only enhances performance but also extends players' careers and reduces healthcare costs. The increasing availability of wearable devices and IoT sensors is further supporting the collection and analysis of health-related data.

Regionally, North America is expected to dominate the sports data analytics service market, driven by the presence of major sports leagues, high technological adoption, and substantial investments in sports analytics. Europe is also anticipated to witness significant growth, supported by the increasing popularity of sports analytics among football clubs and other sports organizations. The Asia Pacific region is expected to emerge as a lucrative market due to the growing sports industry and rising investments in sports technology in countries like China, India, and Japan.

The rise of Fantasy Sports Service has significantly contributed to the growing demand for sports data analytics. Fantasy sports enthusiasts rely heavily on real-time data and analytics to make informed decisions about player selections and game strategies. This burgeoning interest in fantasy sports has prompted sports organizations and analytics firms to develop more sophisticated data solutions that cater to the unique needs of fantasy sports players. By providing detailed player statistics, performance forecasts, and injury updates, fantasy sports services enhance the user experience and engagement, driving further growth in the sports data analytics market. The intersection of fantasy sports and data analytics continues to open new avenues for innovation and fan in

The Market Research Report Covers Fantasy Sports Companies and is Segmented by Fantasy Sports Type (Traditional Fantasy Sports, Daily Fantasy Sports & Ancillaries), Sporting Type (Football, Baseball, Basketball, Others), and Country. The market size and forecasts are provided in terms of value (USD million) for all the above segments.

Dataset

This dataset was created by Plavak Das

Released under CC0: Public Domain

Contents

Analysis of ‘Winter Olympics Prediction - Fantasy Draft Picks’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ericsbrown/winter-olympics-prediction-fantasy-draft-picks on 28 January 2022.

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

Olympic Draft Predictive Model

Our family runs an Olympic Draft - similar to fantasy football or baseball - for each Olympic cycle. The purpose of this case study is to identify trends in medal count / point value to create a predictive analysis of which teams should be selected in which order.

There are a few assumptions that will impact the final analysis: Point Value - Each medal is worth the following: Gold - 6 points Silver - 4 points Bronze - 3 points For analysis reviewing the last 10 Olympic cycles. Winter Olympics only.

All GDP numbers are in USD

My initial hypothesis is that larger GDP per capita and size of contingency are correlated with better points values for the Olympic draft.

All Data pulled from the following Datasets:

Winter Olympics Medal Count - https://www.kaggle.com/ramontanoeiro/winter-olympic-medals-1924-2018 Worldwide GDP History - https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2020&start=1984&view=chart

GDP data was a wide format when downloaded from the World Bank. Opened file in Excel, removed irrelevant years, and saved as .csv.

Process

In RStudio utilized the following code to convert wide data to long:

install.packages("tidyverse") library(tidyverse) library(tidyr)

Converting to long data from wide

long <- newgdpdata %>% gather(year, value, -c("Country Name","Country Code"))

Completed these same steps for GDP per capita.

Primary Key Creation

Differing types of data between these two databases and there is not a good primary key to utilize. Used CONCAT to create a new key column in both combining the year and country code to create a unique identifier that matches between the datasets.

SELECT *, CONCAT(year,country_code) AS "Primary" FROM medal_count

Saved as new table "medals_w_primary"

Utilized Excel to concatenate the primary key for GDP and GDP per capita utilizing:

=CONCAT()

Saved as new csv files.

Uploaded all to SSMS.

Contingent Size

Next need to add contingent size.

No existing database had this information. Pulled data from Wikipedia.

2018 - No problem, pulled existing table. 2014 - Table was not created. Pulled information into excel, needed to convert the country NAMES into the country CODES.

Created excel document with all ISO Country Codes. Items were broken down between both formats, either 2 or 3 letters. Example:

AF/AFG

Used =RIGHT(C1,3) to extract only the country codes.

For the country participants list in 2014, copied source data from Wikipedia and pasted as plain text (not HTML).

Items then showed as: Albania (2)

Broke cells using "(" as the delimiter to separate country names and numbers, then find and replace to remove all parenthesis from this data.

We were left with: Albania 2

Used VLOOKUP to create correct country code: =VLOOKUP(A1,'Country Codes'!A:D,4,FALSE)

This worked for almost all items with a few exceptions that didn't match. Based on nature and size of items, manually checked on which items were incorrect.

Chinese Taipei 3 #N/A Great Britain 56 #N/A Virgin Islands 1 #N/A

This was relatively easy to fix by adding corresponding line items to the Country Codes sheet to account for future variability in the country code names.

Copied over to main sheet.

Repeated this process for additional years.

Once complete created sheet with all 10 cycles of data. In total there are 731 items.

Data Cleaning

Filtered by Country Code since this was an issue early on.

Found a number of N/A Country Codes:

Serbia and Montenegro FR Yugoslavia FR Yugoslavia Czechoslovakia Unified Team Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia

Appears to be issues with older codes, Soviet Union block countries especially. Referred to historical data and filled in these country codes manually. Codes found on iso.org.

Filled all in, one issue that was more difficult is the Unified Team of 1992 and Soviet Union. For simplicity used code for Russia - GDP data does not recognize the Soviet Union, breaks the union down to constituent countries. Using Russia is a reasonable figure for approximations and analysis to attempt to find trends.

From here created a filter and scanned through the country names to ensure there were no obvious outliers. Found the following:

Olympic Athletes from Russia[b] -- This is a one-off due to the recent PED controversy for Russia. Amended the Country Code to RUS to more accurately reflect the trends.

Korea[a] and South Korea -- both were listed in 2018. This is due to the unified Korean team that competed. This is an outlier and does not warrant standing on its own as the 2022 Olympics will not have this team (as of this writing on 01/14/2022). Removed the COR country code item.

Confirmed Primary Key was created for all entries.

Ran minimum and maximum years, no unexpected values. Ran minimum and maximum Athlete numbers, no unexpected values. Confirmed length of columns for Country Code and Primary Key.

No NULL values in any columns. Ready to import to SSMS.

SQL work

We now have 4 tables, joined together to create the master table:

SELECT [OlympicDraft].[dbo].[medals_w_primary].[year], host_country, host_city, [OlympicDraft].[dbo].[medals_w_primary].[country_name], [OlympicDraft].[dbo].[medals_w_primary].[country_code], Gold, Silver, Bronze, [OlympicDraft].[dbo].[gdp_w_primary].[value] AS GDP, [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita], Atheletes FROM medals_w_primary INNER JOIN gdp_w_primary ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[gdp_w_primary].[year_country] INNER JOIN contingency_cleaned ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[contingency_cleaned].[Year_Country] INNER JOIN convertedgdpdatapercapita ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[convertedgdpdatapercapita].[Year_Country] ORDER BY year DESC

This left us with the following table:

https://i.imgur.com/tpNhiNs.png" alt="Imgur">

Performed some basic cleaning tasks to ensure no outliers:

Checked GDP numbers: 1992 North Korea shows as null. Updated this row with information from countryeconomy.com - $12,458,000,000

Checked GDP per capita:

1992 North Korea again missing. Updated this to $595, utilized same source.

UPDATE [OlympicDraft].[dbo].[gdp_w_primary] SET [OlympicDraft].[dbo].[gdp_w_primary].[value] = 12458000000 WHERE [OlympicDraft].[dbo].[gdp_w_primary].[year_country] = '1992PRK'

UPDATE [OlympicDraft].[dbo].[convertedgdpdatapercapita] SET [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita] = 595 WHERE [OlympicDraft].[dbo].[convertedgdpdatapercapita].[year_country] = '1992PRK'

Liechtenstein showed as an outlier with GDP per capita at 180,366 in 2018. Confirmed this number is correct per the World Bank, appears Liechtenstein does not often have atheletes in the winter olympics. Performing a quick SQL search to verify this shows that they fielded 3 atheletes in 2018, with a Bronze medal being won. Initially this appears to be a good ratio for win/loss.

Finally, need to create a column that shows the total point value for each of these rows based on the above formula (6 points for Gold, 4 points for Silver, 3 points for Bronze).

Updated query as follows:

SELECT [OlympicDraft].[dbo].[medals_w_primary].[year], host_country, host_city, [OlympicDraft].[dbo].[medals_w_primary].[country_name], [OlympicDraft].[dbo].[medals_w_primary].[country_code], Gold, Silver, Bronze, [OlympicDraft].[dbo].[gdp_w_primary].[value] AS GDP, [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita], Atheletes, (Gold*6) + (Silver*4) + (Bronze*3) AS 'Total_Points' FROM [OlympicDraft].[dbo].[medals_w_primary] INNER JOIN gdp_w_primary ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[gdp_w_primary].[year_country] INNER JOIN contingency_cleaned ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[contingency_cleaned].[Year_Country] INNER JOIN convertedgdpdatapercapita ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[convertedgdpdatapercapita].[Year_Country] ORDER BY [OlympicDraft].[dbo].[convertedgdpdatapercapita].[year]

Spot checked, calculating correctly.

Saved result as winter_olympics_study.csv.

We can now see that all relevant information is in this table:

https://i.imgur.com/ceZvqCA.png" alt="Imgur">

RStudio Work

To continue our analysis, opened this CSV in RStudio.

install.packages("tidyverse") library(tidyverse) library(ggplot2) install.packages("forecast") library(forecast) install.packages("GGally") library(GGally) install.packages("modelr") library(modelr)

View(winter_olympic_study)

Finding correlation between gdp_per_capita and Total_Points

ggplot(data = winter_olympic_study) + geom_point(aes(x=gdp_per_capita,y=Total_Points,color=country_name)) + facet_wrap(~country_name)

cor(winter_olympic_study$gdp_per_capita, winter_olympic_study$Total_Points, method = c("pearson"))

Result is .347, showing a moderate correlation between these two figures.

Looked next at GDP vs. Total_Points ggplot(data = winter_olympic_study) + geom_point(aes(x=GDP,y=Total_Points,color=country_name))+ facet_wrap(~country_name)

cor(winter_olympic_study$GDP, winter_olympic_study$Total_Points, method = c("pearson")) This resulted in 0.35, statistically insignificant difference between this and GDP Per Capita

Next looked at contingent size vs. total points ggplot(data = winter_olympic_study) + geom_point(aes(x=Atheletes,y=Total_Points,color=country_name)) +

Global Fantasy Sports Market was valued at $31.73 B in 2023, and is projected to reach $USD 102.37 B by 2032, at a CAGR of 13.90% from 2023 to 2032.

Context

Fantasy basketball is a simple game. You select a team and fill out a roster. Each player has a price and you have a budget constraint that you should consider while building your team. You succeed or fail based on how well your players perform. Fantasy sport websites uses their own pricing algorithm and they mostly don’t tell people what their pricing algorithm looks like. In this case study, you will try to explore fantasy basketball data and the player pricing algorithm used for a fantasy basketball website.

Acknowledgements: Invent Analytics for providing data

About

Fantasy Premier League(FPL) is a global fantasy football game of English Premier League, played nearly by 7.5m managers all across the globe. An unfortunate outbreak of a pandemic has halted football and all other sporting events. In terms of FPL, it has provided us quite a time to ponder about our teams. And with Premier League set to resume on 17th June, its time to give a final push to our analysis of the season so far.

Content

Feature Description

Team - the club that the player belongs to Points - points scored by the player till date Cost - value of the player Position - position played by the player Goals_Scored - number of goals scored by the player Assists - number of assists made by the player Saves - number of saves made by the player Yellow_Card - number of yellow cards received by the player Red_Card - number of red cards received by the player Min_Played - number of minutes played by the player TSB - percentage of teams possessing the player CS - number of clean sheets kept by the player Shots_On_Target - number of shots on target by the player Goals_Conceded - number of goals conceded by the player BPS - total sum of bonus points scored by the players

Here are a few use cases for this project:

1. Sports Analytics and Performance Tracking: Use Vision stat v2.1 to analyze player performances, movements, and interactions in real-time or in post-game analysis, providing valuable insights for coaches to improve team strategies and individual player development.

2. Automated Game Highlights and Summaries: Vision stat v2.1 can quickly identify key moments in a game (goals, corners, saves, referee decisions) to automatically create game highlights or summaries, saving time for sports media and content creators.

3. Virtual and Augmented Reality Applications: Incorporate Vision stat v2.1 into VR and AR experiences to overlay real-time information about players, team positions, and game events onto live or recorded footage, enhancing the viewing experience for fans.

4. Smart Stadium Solutions: Integrate Vision stat v2.1 into the security and monitoring systems of sports venues to improve crowd management, detect unauthorized individuals on the field, and ensure a safe and enjoyable experience for attendees.

5. Betting and Fantasy Sports: Use the advanced statistics and live game data generated by Vision stat v2.1 to enhance betting platforms and fantasy sports apps, providing users a more comprehensive understanding for making informed decisions.

The dataset is aimed to gear up for a new season of fantasy epl football. Fantasy EPL is one the most widely played fantasy sports league in the world.

📈 Mejores jugadores de La Liga Fantasy 2023/2024. ⚽️ Mejores delanteros, centrocampistas, defensas y porteros en clave Fantasy Marca, Biwenger, Comunio, Mister y Futmondo