Data and soccer

This week I’m going to take influence from Soccernomics and take a look at a sabermetric approach to soccer. Specifically, why aren’t more clubs embracing the data revolution in soccer?

The use of statistics and data in sports originates from the Oakland A’s and their money-ball approach towards building a team. The managers Sandy Alderson and Billy Beane used a sabermetric approach to build a team by determine underutilized and undervalued players. By acquiring these players, Beane was able to build a highly competitive team on one of the lowest budgets in MLB. Beane used their statistics to determine “good” players that were not apparent to most baseball insiders. Due to Oakland’s success, multiple baseball teams began to employ statisticians to use this same sabermetric approach.

Now, baseball has a larger amount of statistics and a larger sample size to draw from than soccer, but that doesn’t mean that statistics can’t be used to analyze soccer. In some cases, sabermetrics have already taken influence on soccer. In the MLS, Ali Curtis was hired as the New York Red Bulls’s director of football prior to the 2015 season. As he described during a town-hall meeting with the fans, the club tracks everything from non-soccer specific stats – nutrition, heartbeat – to soccer specific stats – crosses per 90, assists, shots on goal – to help determine which players to utilize. His method was met with success: The New Red Bulls won the league in the 2015 season and topped their conference in the 2016 season.

This doesn’t mean that sabermetric approaches are used without controversy. Immediately after Curtis’s hire, he fired current head coach and club legend Mike Petke. As expected, supporters of the club were outraged. Curtis added to this by waiving multiple young, underperforming prospects. From the outside looking in, the club appeared to be mismanaged. It was only by the end of the season in which the Red Bulls locked up the supporter’s shield did the fans realize its success.

The Red Bulls are a small example of this data usage in soccer. Clubs in the Premier League such as Arsenal, Liverpool, and Manchester City have gone further and have hired dedicated data scientists – some even with PhD’s – to aid in the coaching of their team. These analysts use game data to help predict everything from which players to purchase to tactics for their next Premier League match. Clubs use this data: Arsenal signed Matheiu Flamini and Liverpool signed Jordan Henderson almost solely based on statistics. In a more recent example, Leicester embraced statistics to find and sign N’Golo Kante, Jamie Vardy, and Riyad Mahrez. Using these undervalued players, Leicester was able to pull of a shocking win of the premier league last season. Additionally, with the advent of the internet, data has become more accessible to everyone. Websites like Opta, Squawka and WhoScored provides thousands upon thousands piece of data to soccer fans online. Dedicated fans can try to do their own analysis to improve their “armchair coaching” of their club.

Like most things involving technology and soccer, using data analytics to help coach in soccer is controversial. Critics have claimed that statistics ruin the naturalness of the game and cannot track it effectively. Players have chemistry with other players that cannot be represented in statistics. These are the same principles held by the critics of the Oakland A’s implementation of Money Ball; yet, the Oakland A’s succeeded. Chemistry has an effect in baseball, but that was overcome via statistics; so why not in soccer also? Even those that have started using data analytics still aren’t 100% confident in it: Roberto Martinez, head coach of Everton, still believes that the psychological aspect of players are unaccounted for by data. When a skilled player does not perform, as possibly indicated by their “head game”, statistics like chances created or tackles won will go down. Statistics already takes this issue into account. Using data to help coach sports teams is a new frontier, thus the reaction is expected. Coaches, however, need to be ready to embrace this change to help better their team.

About the Author

Mark Krupinski
Sophomore Computational Science Business Manager