If you’re a sports fan, chances are you’ve noticed these terms being used more and more frequently, but what do they mean? Where did they come from? Most importantly, why should YOU care?
The labels may vary from sport to sport, but the idea of advanced stats involves the data that isn’t found inside each sport’s traditional box score. Think about it as the science behind sports stats that allows you to make decisions based on specific circumstances.
Let’s look at a few examples of advanced stats across the world’s most popular sports, how coaches interpret this data to explain what’s happening on the field, and what solutions are out there if you’re interested in diving into the world of analytics.
‘Moneyball’ & the Rise of Advanced Stats
Thanks to Oakland Athletics general manager Billy Beane’s adoration for Bill James’ scientific analysis on why baseball teams win and lose, the term Moneyball is now synonymous with all things advanced stats and probably the first thing you think of when someone brings up analytics.
Beane’s analytical, evidenced-based approach to assembling a baseball team has been well-documented (see: the movie Moneyball) and is now commonplace in elite baseball. Why is that? What stats did Beane analyze and how did he use that information to field winning teams?
At its basic level, Moneyball can be boiled down to tracking advanced statistics used by baseball teams to build an effective roster and make in-game decisions. Here are a few popular examples:
On their own, each example may not mean much to you, but the entire objective should.
The goal of Moneyball and advanced stats in general is to answer objective questions about a given sport through the use of data.
Which player contributes most to the team’s offense?
Which infielder has the most range?
Finding answers to these questions helped coaches put players in situations where they had the best chance to succeed, eventually leading to more wins. Over the course of decades, this approach proved to be so successful that elite organizations such as the New York Yankees, St. Louis Cardinals, and Boston Red Sox decided to hire their own full-time data analysts.
Taking a lead from baseball, coaches and organizations in other sports eventually developed similar statistical approaches to make better tactical decisions.
Advanced Stats on the Hardwood
In basketball, there are two layers of advanced stats – more commonly referred to as analytics – that prove useful for coaches and players.
- Basic - The more accessible analytics revolve around shot charts and run graphs. Coaches at all levels use this information to identify their players’ strongest shooting areas and visualize shifts in momentum.
- In-Depth - Teams at the highest levels of competition are always looking for a competitive advantage, no matter how small it may seem, so they track more detailed analytics like advanced shooting stats, data related to the pace of play, win shares and ball-screen defense. Key stats vary from team to team, but the main goal for each elite level coach is to have the most efficient and effective group of players on the floor at all times.
Regardless of level of competition, the driver of most basketball analytics is the shot chart. In fact, most credit Kirk Goldsberry’s obsession with charting every shot to the rise of analytics in the sport of basketball. Unlike the static nature of baseball, basketball is more unpredictable, making it harder to confidently determine the odds of a given outcome.
Goldsberry quickly realized that if you analyzed all the spaces on the court and how players performed in those spaces, on both offense and defense, you could decipher strengths and weaknesses of any given player or team.
For example, by analyzing shot chart data from across the world, Goldsberry realized most players aren’t very effective from mid-range. If you were a coach with this information, you would make your defensive sets conducive to allowing mid-range shots instead of shots by the basket or out by the three-point line.
Much like baseball, the insights found in analytics has led to many elite basketball teams hiring full-time data analysts. The study of sports analytics has even found its way into college curriculums.
Football, Soccer & Beyond
Analyzing advanced stats is now an integral part of winning strategies in basketball and baseball, but the analytics movement has also taken hold of other major sports across the globe.
For football coaches, advanced stats have proven valuable for scouting opponents and finding areas of improvement for their teams. Formation and down and distance reports, as well as hit charts, are just a few of the more common advanced stats reports coaches have used to develop nuanced strategies that take advantage of opponent tendencies.
Advanced stats are used at all levels of competition, but in the NFL, analytics are mostly considered an extension of what quality control and scouting personnel have done for decades – they identify inefficiencies that can be improved through coaching and help calculate the probability of success for certain playcalls.
Although the use of advanced stats is in its infancy throughout most levels of soccer, front offices across Major League Soccer have dedicated countless resources to determine how to best utilize analytics data. A big reason for this is because the most respected soccer league in the world, the English Premier League, has so fully embraced the analytics movement that they can predict the likelihood of muscle injuries occurring if a player doesn’t get rest, and release entire data collections showcasing their analytics for the season.
Soccer teams at the elite level have access to both tactical and physical data, but not unlike their NFL counterparts, managers aren’t using these advanced stats to replace what they see on the pitch. Managers are using this information to justify assumptions.
As you can see, advanced stats can be a valuable asset in all levels of competition across all sports. However, the quality of insights gathered heavily depends on the amount of work each coach and analyst puts into compiling and analyzing the data.