Sports

Stats in soccer

Historically, people who follow soccer have not paid much attention to statistics. Some may argue that talent and skill cannot be quantified easily, or they might suggest that numbers and mathematical models fail to acknowledge the beauty and elegance of the game. For the most part, they are right. Soccer is a low-scoring game with few other standard statistical markers. The game also involves 22 different players whose actions — for the duration of ninety minutes — influence the final result. These players make the outcome, which is simultaneously influenced by external, uncontrollable factors like weather conditions, hard to predict and blurs individual talent in numbers.

This unpredictability stands in contrast with other sports, which have been implementing mathematical models and statistical data in analysis of their respective games. Baseball, for example, had its Moneyball movement in the early 2000’s. This movement saw teams beginning to use data collected on players to drive recruitment and devise responsive on-field strategies to increase their likelihood of winning. The best example of this analysis in baseball is its increased use of advanced statistics, like Wins Above Replacement (WAR), and teams continue to develop more advanced statistics that relate the average launch speed of a batted ball to a player’s BABIP (Batting Average on Balls In Play).

Part of the reason that statistics have advanced far more in sports like baseball and basketball in comparison to sports like soccer is the collection of data. Aside from goals, assists, and clean sheets, individual actions in a soccer game are hard to quantify. This statistical roadblock is why systemic tracking of players and match data only began around ten years ago. Before this, keeping up with multiple matches occurring in different locations across different leagues made the reliable collection of numbers difficult, if not impossible. Social media and the Internet and better computer processing power, which only really became widespread around a decade ago, made it easier for fans to compile objective data in a sport not classically known for its statistical side. This relatively new practice means that soccer analytics are less advanced. It also means that the average fan lacks a nuanced understanding of some of the controversial statistics being used to interpret the game, like expected goals, often referred to as xG.

So far, the implementation of statistics into soccer has been positive. Teams can easily gather information about their opponents and their tactics. This convenience allows managers to create well-thought out game plans that suit their team, maximize opponents’ weaknesses, and increase their chances of winning. However, the most important change soccer analytics has produced is in the player recruitment process. While not being known as a sport for the people, soccer has a reputation of being a game that favors the wealthy. Considering Real Madrid and their galacticos plan to purchase some of the most well-renowned footballers to teams like Paris Saint-Germain and Manchester City, both of which spend billions of dollars on the transfer market for the biggest names, fans know that teams with more money are likely to have better players and, consequently, dominate both domestic and international competitions.

Refreshingly, statistics and computational models are helping to level the playing field. Clubs with less resources have taken advantage of the accessibility of player data to facilitate player recruitment and minimize the financial risk that comes with team transfers. Some teams have even gone as far as creating football analytics departments that lead the development and the maintenance of data collection, as well as the interpretation of this data to help their teams progress. Other teams have created their own models that use numbers collected on players to evaluate whether the player would be a good fit into their team and whether the financial risk associated with the player purchase would be worth it. Famously, Leicester City signed Riyad Mahrez from French second division side Le Havre for less than half a million pounds. He went on to be a crucial part of their title-winning team in 2015-2016 and was later sold to Manchester City for upwards of sixty million pounds.

Though fairly recent, the incorporation of statistics into soccer has brought new life into the game. Soccer is the most popular sport in the world, perhaps because of its unpredictability. However, there were only ever a handful of teams that would win the top-flight leagues, like La Liga or Bundesliga, killing the odds of surprises towards the end of competitions. Leicester City’s performance was more than just a fluke. It was a sign of the times to come. Despite its flaws and its recency, analytics are helping smaller clubs catch up with the bigger ones. Those hesitant to embrace and exploit the benefits soccer analytics can produce should look forward to soccer’s Moneyball revolution, if not for the advancement of the sport, for the sake of more competitive leagues and more exhilarating matches.