Online Platforms Allow Access, Understanding of Sports Analytics
- Matthew Page
- Nov 10, 2025
- 6 min read
Updated: Dec 18, 2025
By: Matthew Page
As the analytics revolution takes hold of professional sports organizations, the next step is to enable fans to understand the data.
“I always questioned the stats they were showing on screen and was wondering, ‘Are these the right ones to show?’ Stats like average may not be the best ones to use,” said Naman Lakhani, president of the Fordham Sports Analytics Society. “When I got to Fordham, I found this club, which I thought was just amazing because I’d never had so many people that were into sports analytics like I was.”
Working with data allows for both students and people to gain understanding in not just sports, but all walks of the business world. An article from Teal notes, "Their strong understanding of statistical analysis and predictive modeling can be leveraged in finance, marketing, and operations," and skills gained from coding and practice can be applied to data-driven and strategic planning roles.
In addition to clubs at the academic level, fans and academic sources alike are gaining popularity. They are utilizing the available resources to make the data comprehensive and engage in dialogue.
Hockey Player Cards and Databases
The National Hockey League is making data available through in-game stats and insights. In 2024, the league launched NHL Edge, which showcases the location and quality of scoring opportunities, as well as sprint and shot speeds.
The conversation also extends onto social media. Personalities such as JFreshHockey, Evolving Hockey and MoneyPuck have created models that not only showcase a player’s value in all areas but also visualize it for publication online.
Another platform is CoreNHL, an Instagram and X page created by Michael Ostrower. Using online data, he creates his own visualizations to evaluate team performance, player value relative to the contracts they’re signed to, and probable growth or regression over the next three seasons. While hockey analytics are debated on how they show player value on a game-by-game basis, Ostrower says his posts work to get fans closer to and understand the sport in depth.
“Most people don’t scroll on Instagram and see anything about advanced analytics,” Ostrower said. “I thought that the model was great for me individually because I was able to compare what I was seeing versus what the data says.”
Baseball Contact and Results
In baseball, several high-profile platforms give fans access to data, such as Baseball Savant and Fangraphs. Baseball used to be a game of batting average, but today’s game now emphasizes qualitative statistics.
The popular example is On Base Plus Slugging (OPS), which is a combination of two kinds of data:
On base percentage (OBP): The rate at which a player reaches base through hits or walks
Slugging Percentage: A qualitative stat that values the result of base hits. Doubles triples and home runs lead to higher value.
As such, teams have placed a value on not just getting on base, but hitting the ball hard as well. If a team can hit the ball harder, this can lead to higher slugging percentages and more runs.
Top offenses like the New York Yankees, Boston Red Sox, Seattle Mariners and Philadelphia Phillies made the playoffs with hard-hit rates well over 40%. However, the expected outcomes from contact don't always lead to the true wins or losses, and analysts can explore how far above or below the mean teams are in performance.
Derek Grifka, a data scientist with the Chicago Fire, created the MLB Deserve-to-Win-O-Meter on X, visualizing 10,000 simulations of a game that factors in exit velocity, launch angle, walks and other outcomes. He enjoys the dialogue that comes with sharing the model but also finds interesting how teams’ outcomes change over the course of 162 games.
“The other end is teams where they’ve been getting unlucky and then start to progress back towards a mean in a positive way where they start to start to win a lot of games,” he said.
Football Play Data Packages
The National Football League is making analytics part of their television presentation. Broadcasts across the board use Next Gen Stats as a way to track the player’s position, sprint speed, and ball tracking to highlight player performance. This engagement continues online, as various packages exist that give greater insights to play-by-play data.
Examples of these sets are NFLVerse and NFLFastR, which scrape play-by-play data into an R package. Data points include expected points added and win probability added, expected yards after catch and rushing yards over expected on receiving and running plays, respectively.
“Even people I talk to from NFL teams tell me that the data is useful because it’s very clean and standardized,” said Ben Baldwin, an author of both platforms. “Even though they have access to all these other details for the data, they still use the play-by-play data that we provide.”
Basketball Player and Lineup Evaluations
As basketball continues to adapt to life with valuing shooting threats, platforms CraftedNBA and Viziball not only visualize, but also emphasize the true value players bring when on the court. They use stats such as effective field goal rates, true shooting percentages, and offensive rebound percentages, which prioritize the quality of a player's offense.
However, such developments can also be applied at the collegiate level. Evan Miyakawa, creator of EvanMiya.com, created his own model called “Bayesian Performance,” evaluating, “how many points per 100 possessions better a player’s team is expected to be than its opponent if that player were on the court with nine other average Division I players.” Miyakawa also offers lineup configuration models, that take a team’s roster and determine which combinations can yield the most ideal results, including five-man and subset on-court units, which are used by NCAA programs.
“When these particular two players play on the court, how does the team perform with them, or when these three players or four players,” Miyakawa said. “You can break it down into smaller combinations of players, and that can give you a lot of information too.”
Other Engagement Opportunities
As information and data becomes more openly available, fans can use it not just for understanding, but also to reach their own conclusions. Research Ground reports gambling is an industry that is impacted by data availability; deeper insights enhance how fans can make decisions on wagers. They note that probability analysis, regression analysis and machine learning models all come into play for experienced bettors.
RG's Sol Fayerman-Hansen writes, "Sports betting operators and oddsmakers analyze hundreds of factors—team performance, injuries, historical trends, and market behavior—to establish an opening sports betting point spread or moneyline. The published odds are not only about predicting the outcome; they are also designed to attract balanced action on both sides. There are opportunities for bettors to do research and find gaps in the odds." However, they also note that, "NO analytic method is foolproof," and that players must be responsible when betting based on the data.
Regardless of how fans use the data, the amounts of it available to the public is growing. From online platforms to in-game engagement, today's analytics allow for fans to form their own judgements, whether it's to evaluate players or predict outcomes of games. With more data available, there's hope that fans can use the methods available and find a way to make the data their own.
"The hope is that we do the annoying stuff that other people can get a clean version of the data and do the interesting stuff for themselves,” Baldwin added.
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