The Perceived Disconnect Between Soccer and Analytics

By | February 29, 2016

By Timothy Nyangacha

It’s an idea that I have spent considerable time pondering: What makes soccer so exceptional in that its community has not been swept up by the rise of analytics in other sports? Is there something unquantifiable about the game of soccer that allows it transcend statistical analysis and predictions? Post-match statistics include possession percentage, pass accuracy percentage, shot accuracy percentage, and other basic calculations that could be determined by anyone who actively watched the match. These simple quantifications of individual and team performance greatly contrast with, for example, the Player Efficiency Rating (PER) and Real Plus-Minus (RPM) metrics adopted by the NBA as well as the Quarterback Rating (QBR) statistic in the NFL. It seems as if American sports, in particular, have fallen in love with analytics as general managers are increasingly dependent upon them to evaluate talent and fans want the knowledge that they provide to understand what makes their favorite players so great. I’d like to offer two comments that may shed some light on why soccer has been mostly unaffected by the rise of analytics, though this may change in the coming years. The basic structure of how soccer is played and cultural differences linked to the origins of the sport compared to American sports are two possible explanations for the difficult questions concerning soccer analytics. Admittedly, this is an oversimplification, but here goes nothing.

The rules and structure of soccer make introducing analytics into the sport a challenging endeavor. First and quite obviously, every goal is worth a single point. Whether it is a long shot or a header inside the box, each goal has a value of one. This differs from basketball, where a basket can be worth one (free throw), two or three points depending on the location and context of the shot. The evolution of analytics in basketball has led to the conclusion that close-range shots and three-pointers are much more valuable and desirable than mid-range shots. In particular, long two-pointers are least valuable because they are converted at a low accuracy by most players and are worth the same number of points (2) as shots near the basket. Extending this idea even further, the concept of a ‘good shot or opportunity’ in soccer seems, in my opinion, to require less statistical evidence to prove compared to basketball and even American football. In theory, the best shot in soccer should be the closest one. Inherently, the skill set, footedness, and other characteristics of the player might dictate what their best shot is, but the closest shot should still yield the highest conversion percentage. In basketball, determining which shots are the best shots require more data aggregates and analysis to reach a conclusion. It seems like more factors are at play in determining the best shot in basketball compared to soccer. Statisticians and analytics must account for the value of the specific shot, the accuracy at which the player typically converts those shots, whether the shot is guarded or unguarded, and other situational factors. The conclusion eventually reached is that close-range shots and three pointers are the ‘best shots’, but the process seems much more complicated compared to soccer. The presence of goalkeepers in soccer also impacts the success at which ‘good shots’ are converted. When a basketball player is on a fast break and no one is in front of him, he has the opportunity to score on an easy layup/dunk and almost always converts. In soccer, however, a soccer player with no defenders in front of him must beat the goalkeeper in order to score the goal. These sorts of opportunities seem to be a bit less successful than uncontested scoring attempts on a fast break in basketball, though I could be wrong. All in all, the events that occur in soccer matches are much harder to analyze as a whole compared to basketball and other American sports.

Sports are rooted in the cultures from which they originated and often represent the principles and values of the societies in which they are played. The cultural differences reflected in in soccer and American sports can be a contributing factor in the pace at which analytics has been applied to each sport. Referencing the introduction of soccer in British public schools, it is evident that the sport of soccer, an extension of British society, emphasized teamwork and discipline. Success was ideally achieved by the collective effort. The performance of an individual, though noteworthy, was much less important than the success of the team. American sports, particularly baseball, football, and basketball, embody the values of American society both from the time periods in which they originated and contemporary America. Individual productivity, efficiency, and performance are characteristics that have been tracked in all major American sports for decades and are as important (if not more) than the success of the team. Fans are also increasingly more interested in the advanced statistics that arise from sports data aggregates. It is clear that culture has played a significant role in how the aforementioned sports are played and the rate at which they have adopted complex methods to quantify performance.

Recently, some metrics have been developed to provide us with numbers potentially indicating why particular players and teams excel above the rest. Total Shots Ratio (TSR) and expected goals (ExG) are concepts that were created over the last few years that have some value (Goodman). It is likely that as we move forward, more advanced statistical categories will emerge and their prominence will grow.

Ultimately, does it really matter that soccer has been slower to incorporate analytics into its framework than other sports? It is debatable whether analytics actually enhances the experience of the spectators or provides valuable information from which to make personnel decisions. Sports writer David Goldblatt notes that “for longer than most sports, football resisted the use of statistics for analyzing the game” due to “a persistent preference for artisanal knowledge” (70). Perhaps our understanding of the beauty of soccer is best enhanced through watching the game rather than ‘advanced’ statistics.


Works Cited

Goldblatt, David. “Keeping it Real? Match Day in the Society of the Spectacle.” In The Game of our Lives.

Goodman, Mike L. “The Adolescence of Soccer Stats.”, last modified September 11, accessed February 29, 2016,

2 thoughts on “The Perceived Disconnect Between Soccer and Analytics

  1. Breanna Atkinson

    I think one of the distinctive features of soccer is the lack of analytics. Sports like football, basketball and baseball have so many breaks, stops and timeouts during the game while soccer is continuous. This leaves more room for creativity and personal style for soccer players, which I believe is the reason soccer fans everywhere love the sport!

  2. Andrew Jordan


    This is a really fascinating post! I have also noticed the change in culture; however, I believe that there just simply isn’t enough structure to the game to have intense analytics. Soccer is all about improvisation, creativty, spontaneity, and movement, as opposed to the optimization of certain actions. It would be almost impossible to quantify the full effect that any player has on the ebb and flow of a soccer match. Additionally, I would hate to see such a beautiful sport be bogged down with made up parameters. Soccer should stay more of a qualitative game as opposed to a quantitative one!

    Great work!


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