This is the first article of Season Two and eighth overall article in a series of articles and videos in the Where Goals Come From project from Jamon Moore and Carl Carpenter.

Season Two Introduction

In the eight articles of Season One of the Where Goals Come From project we demonstrated how “progressive pass” goals make up 40% of the goals scored in professional soccer.

2015-2020 Goal Categories from Europe and US Leagues.png

We broke these down into various types of progressive passes, demonstrated each type, and also demonstrated how teams can take advantage of each. We also demonstrated that the perceived or real quality differences league-to-league does not change these numbers much at all.

In Season Two, we are going to go a good bit deeper into the various types of goals, and situations which lead to them being scored. We’ll still look at improving progressive passing because, generally speaking, that is how top-performing teams create a gap from mid-table and lower teams. But we’ll also look at other types of goals and what works more often than what doesn’t.

In Season One, we used percentages to show methods that work better than others. We will continue that in Season Two, but we’ll introduce other techniques such as probabilities and Expected Goals (xG). If you are tired of the xG dialogue, or it is confusing to you, don’t worry -- we will only use it in ways that benefit front offices and coaches, and we’ll help keep it easy to understand or help it be understandable for the first time for you.

What we want to do is separate shot opportunities into poor, good, better, and best, so we can see what situations work more often than others. Percentages, probabilities, and xG (all inter-related) will help us do that. One of the great things about this sport is that all this math doesn’t ruin The Beautiful Game -- the teams that do these things the best are the ones we universally consider the most visually-appealing ones in the game. Goals are beautiful!

Boosting Your Chances

Keep in mind that soccer is a funny sport, and scoring is tough for any team in any league. Virtually nothing in the game is true over 50% of the time. Almost everything that happens, happens from a low percentage chance.

At the risk of being a bit obvious, let’s start at a basic level and build out from there.

Goals happen on about 10% of all shots.
Shots happen on about 11% of all possessions.
Teams get an average of 140 possessions per game.
Teams roughly score about 1.23 goals per game from these 140 possessions.

*Average Goal Conversion Rates (white lines) don’t vary much league-by-league (9.5% to 10.5%). In Major League Soccer, the salary cap parity pushes more teams into the middle.*

Top-performing teams manipulate the numbers in those four bullet points higher, and suppress them against their opponents. For example, since the 2014-15 season, Barcelona has scored on almost 17% of their shots (without Lionel Messi, they still score on 16% of their shots). Barca’s opponents take only two-thirds of the shots they do and score on only 8% of them.

Across our six profiled leagues, of the top 25% of teams (teams that score over 1.41 goals per game), 94% of them beat the league average in Goal Conversion Rate.

*Filtering to the top 25% of teams in goals per game, 94% of them outpaced the league average in Goal Conversion Rate.*

Higher shooting percentage is not the only way to score more goals, both shots and possessions can be manipulated as well. Since 2014-15, Manchester City has scored on 13% of their shots, compared to Barcelona’s 17%, but they’ve also taken 12% more shots than Barcelona.

This visualization shows which top clubs are better than their opponents at conversion rate or shots per possession.

I’ve highlighted Manchester City and Barcelona teams on the graph to better illustrate the point.

Here is a zoomed-in image of the upper right corner of the chart.

As you can see from this visualization, teams with a poor Goal Differential are in the bottom left quadrant clustered together, while teams with a higher Goal Differential are in the upper right quadrant, but much more spread out. The cause of failure is the same, but the factors that create success can vary a bit more.

Statistics side note: The correlation between the Shots per Game Difference and the Conversion Rate Difference is very weak, which means that most teams tend to do more of one than the other. In fact, this spread becomes huge when looking only at elite teams, which we define as those with a 1.2 or greater goal differential per game. Once you filter for just those elite teams, we get a moderate negative correlation (-0.55). In essence, they almost go in the opposite direction, reinforcing the point that success factors vary more than failure factors.

Every team over a 1.2 Goal Differential per Game has a positive Conversion Rate Difference and a positive Shots per Game Difference telling us elite teams tend to focus on one or the other (or a little of both) but both are important. The trend line tells us that more successful teams are found on the Conversion Rate Difference side, however.

The Red Bull organization teams are famous for their pressing and frenetic play. They manipulate total possessions higher to overcome some deficits to more talented teams. For example, the New York Red Bulls have 10% more total possessions in the 2021 season than the next highest team in MLS.

One of the drawbacks of total possessions is each team gets nearly the same number of possessions per game. To be elite with this category, you need to consistently do better in shorter possessions than your opponent, and very few teams can reach an elite status doing this. More statistics speak: there is a weak negative correlation between having more possessions and goal differential per game. That tells us that teams that have more possessions are also slightly more likely to be out-scored.

Any effective season-long strategy then needs to come from a net positive difference of three factors: shooting percentage, total shots, and total possessions, and probably at least two of them if not all three of them.

The Reality

The bigger the positive difference of these three factors vs. the opponent’s, the more likely a team will perform at an elite level. There are no guarantees, but the team almost always outperforms most or all of the league over the course of a season.

For example, let’s assume that every team in the league shoots 500 times in a season. The average team converts 10%, the top team shoots 14% and the bottom team shoots 6%. The top team scores 70 goals, while the bottom team scores 30 goals. That 40-goal difference, over the course of a season, is absolutely huge.

Conversely, if every team shoots 10%--but the top team shoots 700 times, the average team shoots 500 times, and the bottom team shoots 300 times--the same number of goals will be scored as in the prior example, and the same 40-goal difference separates the top team from the bottom team.

But in reality, it is a lot more complicated: teams have varying numbers of possessions and shots, some come from set pieces, crosses, cutbacks, rebounds, outside the box, and more variations. Shots are not all scored at the same rates, as our Season One articles demonstrated when discussing key pass types and balls into the box.

A quick refresher visualization from our Zone 17 passing article is below.

Some situations score at higher, occasionally much higher rates, than others. Some players may excel at shooting in particular situations in a season or over a career. Some may be better with their head and others with their feet.

What we are not advocating for here is not necessarily SHOOT CLOSER!, although that probably will help low shot conversion rates. The real key to this is to go back to our 11 non-penalty shot types from Season One.

You can see which types of shots are the most valuable above, starting with the very effective Through ball shot. But not all through balls are created equal. Each one does not have a 30% chance of being scored.

The focus of our Season Two articles in the Where Goals Come From series is to identify bad, good, better, and best patterns and situations that lead to higher Goal Conversion Rates and help players understand when and where they should look for a shot, and when they should keep looking. It can help coaches train for specific situations and outcomes to fine-tune game performance with better informed players. This information should also lead to new ideas on how to defend the most dangerous situations and force opponents to take fewer and lower-quality shots.

In our next article we will look at tools that can help us identify these. This will include the perhaps-dreaded Expected Goals (xG) discussion, but as we said in the introduction, we will not cover this topic the way it is usually covered. We want you to turn this information into action, and we will guide you through that and do our best to simplify it and take out the mystique and confusion.

Conclusion

Regardless of the level of play or style of play, the factors that determine a team’s performance are based on the ability to either score at a higher efficiency than the opponent or shoot much more than the opponent:

These factors are measured using the Goal Conversion Rate Difference and Shots per Game Difference over the course of a season.
Some elite performing teams are able to do both well, but usually do one better than the other. That said, underperforming your opponents in either one is not an option to be at the elite level.
On occasion, a team may accomplish either or both by out-possessing the opponent, however this is likely to be influenced by other stylistic factors.
The key to unlocking a higher Goal Conversion Rate is found largely in shot location and shot type as we covered in Season One.

We will explore other factors that influence Goal Conversion Rate, and how to separate better shots and good shots from bad ones starting in our next article. Over the course of Season Two we will be exploring how to create more and better shots than your opponents.

About Jamon Moore

Jamon is a high-technology industry executive overseeing business agility transformations. In addition to a contributor for American Soccer Analysis, he is a credentialed media member covering the San Jose Earthquakes from mostly an analytical perspective. Jamon can be contacted via Twitter, and club analysts and executives can connect with Jamon on LinkedIn.

About Carlon Carpenter

Carlon is the current Tactical & Video Analyst for StatsBomb, one of the largest soccer data companies in Europe. Carlon also works as a contract employee for the U.S. Soccer youth national teams, working as a performance analyst for the U-17 men’s national team. Carlon can be contacted through his LinkedIn account, or via Twitter.