Soccer analytics has come a long way in recent years along with the rise of sports analytics. Today teams and management recognize the impact that data can have on the game and use the modern tools available to them to make that impact. There are even advanced metrics making their way into the media such as expected goals – the number of goals a team is expected to score based off the probability of scoring a goal given the location of the shot. The stats that have been used to describe games in the last decade don’t account for the fluid nature of the games they try to describe. In soccer specifically, goals do not account for luck. It is not uncommon for a team to generate goal scoring chances but have an off day in front of the keeper. Expected goals thus was introduced to account for this and plot a team’s chance generation regardless of the final goal, giving a true picture of a team’s scoring potential across games. Possession, as another example, does very little in the context of telling you the value of the possession of a team. In soccer, many teams play counter attacking football, purposefully letting their opponent control possession and catching them off-guard very high up the pitch. Traditional possession stats do not capture this, so to provide greater insight into the value of the time spent on the ball by teams, statistics such as expected threat were developed. Expected threat (xT), calculates the aggregate danger a team generates over the course of a game or on an average possession, to provide insight into the threat generated by teams each time they have the ball.

Below are some of the most useful advanced metrics seen in soccer today from a game analysis point of view:

xT (Expected Threat) : Each area on the field is given a numerical rating of potential threat – or danger that can be caused from a position – based off of historical data and proximity to the goal. Based on the positions that a team manages to get into over the course of a game, they are given an expected threat rating.

xG (Expected Goals) : Each shot taken on the field is given a probability rating for the likelihood of scoring from these positions. A number of variables are used to determine this probability, such as angle, distance, foot of the player (dominant or non-dominant), type of shot (header etc.) and the more advanced calculations even take into account the positioning of the opposition.

xA (Expected Assists) : Expected assists is used as a player metric and is the number of assists a player was expected to generate based on the passes they made that led directly to shots. The xG (see above) of the shot taken is the xA for the player. This metric can be used to gauge the danger the player generated and the scoring potential of their passes.

Where are advanced stats going:

Currently advanced statistics are sold to professional teams and guarded from the general public. This is done because the companies that sell this data do not want to make it publicly available for teams to a point where they would not have to purchase the data themselves. As time progresses the more useful and high level of these statistics will continue to make their way into the media and become more available for fans. Companies such as Sports Loci – a soccer statistics company and content creator – are working to get more of these stats available to the public.

Believe it or not, there are still circles and philosophies of management that do not believe that data is as useful as it is made out to be. Of course, with time, this sentiment too will fade, and modern management of professional teams will be inseparable from data analytics.

In the coming years expect to see more journalism that is statistically based and heavy with advanced statistics. This trend is already present in some circles, but it will grow, and younger fans will be the most aware and informed of these stats.

Statistics and Gambling: uses their comprehensive database to compile historical, seasonal, and recent data to predict the likelihood of each result for hundreds of soccer games a day. Betting and algorithms have a complicated history, much like the stock market and computers that attempt to predict how to make money on the market. At Sports Loci it is widely accepted that a computer alone cannot make accurate predictions at a consistent enough basis to make a significant portion of profit. Rather statistics and algorithms are used at the company to give fans and players a picture of the form and strength of each result coming into the match. These insights used in pair with the analysis of a player are the recipe for success.

This article was written by Lisbel Martinez, founder of Sports Loci.