Introduction
In the ever-evolving landscape of sports, traditional box score statistics like goals, points, or assists have long served as the benchmarks for evaluating player performance. However, in the quest for a deeper understanding of the game, analysts and teams are turning to advanced metrics that offer a more nuanced and comprehensive picture. These metrics are transforming how we view matches, evaluate athletes, and how the teams make strategic decisions. In this article, I will demonstrate how advanced metrics have changed over the years in different sports.
From Goals to xG and Beyond – Football
In football, the concept of Expected Goals (xG) has become a cornerstone of modern analytics. Unlike traditional metrics, xG assesses the quality of scoring chances, providing a probabilistic estimate of whether a shot should result in a goal. By considering factors such as the shot’s distance from goal, angle, and the number of defenders around, xG offers a more accurate measure of a player’s finishing ability and a team’s offensive efficiency than the traditional shots attempted and shots on target metrics.
Another advanced metric gaining traction in football is Expected Assists (xA). This statistic evaluates the quality of a pass that leads to a shot, shedding light on playmakers who create high-quality scoring opportunities that might not always be converted into goals. Together, xG and xA paint a deeper picture of a player’s contributions on the pitch, moving beyond simple goal and assist tallies.
Another innovative metric in football is Expected Threat (xT), which quantifies the likelihood of an action leading to a goal. Unlike xG and xA, which focus on shots and assists, xT evaluates all actions on the pitch – such as passes, dribbles, and carries – to determine their contribution to advancing the ball into more dangerous areas. By assessing the potential of each movement to create scoring opportunities, xT provides a comprehensive view of a player’s influence on the game.
These advanced metrics are just a glimpse into the evolving landscape of sports analytics. Every year, analysts and data scientists continue to innovate, developing new models and techniques to deepen our understanding of the game.
xG value of shots in certain areas (https://www.flashscore.com/news/expected-goals-or-xg-what-is-it-and-what-can-its-value-be-used-for/zkSPpS6F/)
Evolution in Player and Team Evaluations – Basketball
Basketball, with its fast-paced and high-scoring nature, has been fertile ground for advanced analytics. Player Efficiency Rating (PER) is one such metric that has revolutionized player evaluation. Developed by John Hollinger, PER condenses a player’s statistical accomplishments into a single number, adjusting for pace and playing time. This allows for a more equitable comparison of players across different teams and eras.
Another influential metric is Net Rating, a crucial advanced metric that measures a team’s point differential per 100 possessions. It calculates the difference between a team’s offensive rating (points scored per 100 possessions) and defensive rating (points allowed per 100 possessions). By providing a single number to represent a team’s overall efficiency on both ends of the court, Net Rating allows for a more nuanced comparison of team performance than simple win-loss records.
NBA Top 10 teams in Net Rating last season (https://www.nba.com/stats/teams/advanced?LastNGames=0&SeasonType=Regular%20Season&dir=A&sort=NET_RATING)
The Impact of DVOA and EPA – American Football
In American football, the complexity and variety of plays have led to the development of sophisticated metrics such as Defense-adjusted Value Over Average (DVOA) and Expected Points Added (EPA). DVOA evaluates team performance on a per-play basis, adjusting for the strength of the opposition. This provides a more accurate measure of a team’s efficiency than traditional stats like total yards or points scored.
EPA, on the other hand, measures the value of individual plays in terms of their contribution to the team’s expected points total. By accounting for down, distance, and field position, EPA offers a detailed understanding of a play’s impact, whether it’s a three-yard run on third-and-one or a 50-yard touchdown pass. This metric is invaluable for assessing quarterbacks, running backs, and receivers, highlighting their true contributions to the team’s success.
The NFL and AWS are collaborating in tracking Next Gen Stats since 2014 (https://operations.nfl.com/gameday/technology/nfl-next-gen-stats/)
The Future of Sports Analytics
The integration of advanced metrics into sports analytics represents a paradigm shift in how we understand and appreciate athletic performance. These metrics provide insights that go beyond the surface, uncovering the subtleties and intricacies of the game. For fans, this means a deeper appreciation of the sport and its players. For teams and analysts, it translates into more informed decision-making, from player acquisitions to in-game strategies.
As technology continues to advance, the potential for further innovation in sports analytics is immense. Machine learning and artificial intelligence are already being employed to develop even more sophisticated models, while wearable technology is providing unprecedented levels of data on player movements and physical conditions. The combination of these tools promises to push the boundaries of what we can measure and understand about sports performance.
Sources
https://www.nba.com/stats/help/glossary
https://www.pff.com/news/bet-nfl-bet-terms-metrics-game-script-handicapping-success-rate-epa