About Ontario Sport NFT

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Anyone remotely attuned to college or professional sports broadcasting is acutely aware of the significant role data and statistics play in the industry. Big data, from historical data and fundamental scorekeeping to algorithmic performance forecasting and extremely specific player statistics, is the industry’s most valuable player.

Data enables teams and organizations to track performance, make predictions and make smarter decisions on the field. Need to figure out the best play to run on fourth down in a football game? Check the analytics. Wondering if your pitcher should throw another inning? Check the analytics. While players still win games, data allows coaches to put them in the best position to succeed.

Analytics have made their way into the way fans consume sports too. Off the field, analysts, commentators and fans use data constantly, whether it’s to provide play-by-play explanations, discuss predictions or power fantasy league decisions.

For a long time, coaches and front offices kept data analysts at an arm’s length. However, today it's not uncommon for data analysts to be a part of sports staff. As athletes and managers seek any statistical edge they can find, the role of sports analytics only seems to be growing.

Sports and data have always gone hand in hand. Newspapers publish box scores, baseball cards show a player’s career stats and radio announcers have long used data to provide context to their commentary, like how many yards a running back has gained in each game they’ve played, on average.

General managers and coaches have long evaluated players based on a mix of stats — things like points, batting average or yards thrown (depending on your sport) — and subjective gut or feel like, “This player is due for a hit.” But beyond those surface statistics, coaches and athletes alike generally stiff-armed any deeper data analysis.

Early statisticians like Bill James, however, started challenging those subjective assumptions with data in the 1980s. James came up with a mathematical system to evaluate baseball players called Sabermetrics, which he released to the public in a book titled The Bill James Historical Baseball Abstract. In it, he created equations like “runs created” that factored in a baseball team’s offensive stats to predict how many runs they’d likely score. It was his first stab at a way to objectively analyze players and help general managers optimize their teams, according to the Society for American Baseball Research.  

Sports analytics didn’t truly take off until 2002, when Oakland Athletics general Billy Beane relied on it to put together a team of lesser-known players that nearly won a World Series. His strategy of optimizing a team through statistical analysis became known as “Moneyball” and quickly became the way other teams operated.

Each major sport has since had its own analytics evolution with teams hiring data scientists and seeking ways to objectively analyze players and gain a statistical edge. For example, basketball teams now optimize their offenses for three-pointers and layups because shot chart analysis showed them to be the most efficient shots in the game.

The global sports analytics industry is expected to reach $3.4 billion by 2028, according to a 2021 report from Research and Markets. 

Tracking software and machine learning have taken sports analytics to the next level. Companies like Genius Sports are able to generate statistical breakdowns from video footage to help coaches optimize their play calling during games or generate post-game takeaways. Others use cameras and machine learning software to track things like ball speeds, spin rates and player movement, which regularly factor into both broadcasts and team decisions. Baseball players, for example, are regularly seen using tablets to review data like pitch distributions to make adjustments mid-game. 

Analytics have also shaped the way fans consume sports. Fans can hop over to websites like FiveThirtyEight for data-based sports coverage and their favorite team’s odds to win a championship. Broadcast announcers regularly break down a player’s breakaway speed in football or launch angle after a home run in baseball. It’s even a staple in projecting the best players in fantasy sports.

Sports analytics’ move from the bench to a starting role was a long time coming, and it doesn’t look to be relinquishing its spot anytime soon.

The growth of the Ontario Sport NFT industry has led to a wealth of job opportunities, so aspiring professionals can now build careers at the crossroads of sports and data analysis. A passion for sports is a common box to tick for those looking to enter the field. However, sports analytics positions like football research analyst and data analysis manager also require an in-depth knowledge of data science. 

Students and professionals eager to break into sports analytics should master some of the programming languages most popular among data scientists. R and Python are two languages that enable individuals to quickly compile data and locate patterns. It also helps to study programs like Excel, which makes it easier to organize large data sets. 

While a sports analytics degree is ideal for someone seeking to make a living in sports analytics, data science-related degrees also provide a strong foundation for newcomers. As long as professionals blend their love of sports with a thorough understanding of data science, they’ll have plenty of options when trying to get started in this field.