Reddit calls itself the front page of the internet. There are subreddits for any topic you might want to explore, today there are 1,209,754 different subreddits. In the r/sportsanalytics and r/dataisbeautiful, many amateurs are creating original analyses. The visualizations from their work show that sports often intersect with other important factors besides game performance. Geography, economics, and even the discussion around sports analytics itself create interesting data to be visualized for fans. Here are 5 visualizations of sports data that show the breadth of analysis by amateurs and cover the different topics and methods of visualization and some insight into the different aspects of sports analysis on Reddit.
Sports analytics grew out of simple sports statistics that were kept like the baseball box score. Bill James is widely considered the first important analyst who, while working in a bean canning factory, put together the Baseball Abstract that tried to identify ways to understand which baseball teams were better with statistics. This process would become more sophisticated through the next 30 years as a community formed around sabermetrics. As technology became more sophisticated and data sets became available on the web, more amateurs were able to participate in looking through data for insight on how teams played. Nate Silver of FiveThirtyeight first became famous when he sold his PECOTA algorithm to Baseball Prospectus before his notoriety in election predictions.
World Highest Paid Athletes
u/datashown visualizes the income of athletes by their sport salary and endorsement deal incomes. The athletes have different color bars to show which sport they play. This shows how even highly paid athletes differ based on how popular they are for endorsements.
Sports data visualizations use many different methods of visualizations. The visualizations created on Reddit use many types of display to show interesting information. Pie charts are often used to display what portion of the whole a particular statistic makes up. Line graphs are common to show performance over time like games per season or team performance trends over many seasons. Sports are geographically situated and maps explore how their trends effect the country or world differently. Bar graphs are simple ways to show the overall count of statistics and are common in single statistic measures like which team has the most passing yards. Scatterplots are used to show how different teams or players compare to each other with two different statistics. They allow the reader to understand how those two statistics relate to each other
Closest D1 Hockey Team to Each US County
u/JohnDoeMonopoly displays the closest D1 hockey team for each county and indirectly shows the regionality of D1 hockey. This map displays the density of interest in D1 hockey in the northeast and the surprising existence of teams in the west coast.
The sports visualizations from Reddit sports analysts cover a wide variety of topics. Team performance is the most salient but often comparing team performance is just one aspect of sports. Since these Redditors are fans, the fan experience is often a topic of measurement itself. Players are measured based on their on and off the field attributes. Fandom as a geographic or time measurement are great ways to understand how the sport interacts with its viewers in space and time. The economics of sports is also a major object of study, how players are paid, how teams earn money and the revenue from endorsement deals are all subjects of interest to analysts on Reddit.
MLB Number of Wins by Payroll
u/osmannoah graphs the MLB teams, the higher up they indicates more wins and the farther to the right shows the teams with largest payrolls. This visualization gives the reader and idea of how large the difference in payrolls can be between teams with similar performance.
The community of Reddit sports analysts come from a wide variety of backgrounds. From students, to academics, to professionals and the engaged amateur, they find common tools to get access to sports data and turn that into engaging analysis and visualizations. The three most common technologies are the most accessible business and open source data analysis tools on the market. None of them are expressly designed for sports data, rather the analysts use the tools they are most familiar with from their studies or day jobs. The world of data analysis technology is fast moving and ever more accessible. This explosion in computing power for the average laptop has created an ecosystem of technologies empowering a generation of amateur analysts to create original work on their own.
Super Bowl Mascots Comparisons
u/Blatb00m proves that even the pie chart can be made interesting. This graph shows how often the Super Bowl outcome has different mascot types winning. Humor and surprising results in sports analysis are popular and make simple visualizations interesting.
As amateur data analysis has grown popular, sports as a topic has grown right with it. Reddit as a data savvy and tech forward platform has a large audience of users that enjoy interacting with data. As innovative data visualizations techniques have become important to digital media generally, the sports analytics community has embraced it as a medium to communicate the insight of the numbers to large sports fan audiences. Sports fans are not hungry just for written or video analysis but also looking into the numbers more and more. This hunger for analysis has created a large audience for sports analysts to reach, and the widespread availability of analysis tools democratizes the access to the new mediums. A stunning visualization that brings the data to life and tells a story is compelling to digital media consumers and Reddit sports analytics provides new content to an ever growing audience.
Major Sports Championships by City
u/sirvizalot gives the reader a comparison about which cities won the most championships and which sports championships have a different geography and fan base. Visualization touches the fan aspect of sports by rooting to the location of teams.
The community on Reddit of amateur sports analysts is vibrant and uses a variety of methods to talk about many different aspects of sports. The use of visualizations transforms sports data analytics from the realm of arcane math wizards to fan engaging insight. These communities give a creative outlet to the amateur analysts and entertaining knowledge to sports fans. These communities that have large amateur contributor populations are increasingly visible in the content consumption of sports fans.