Trends and patterns are the one constant thing in an ever-changing world. People, dogs, and birds all show trends. One of the primary jobs of data analysis and the use of statistics is to discern these trends and patterns. The MLS in the past few years has shown tremendous growth as a league, developing their players and managers. This all was seen rather beautifully with LAFC who played a possession-based football that even Manchester City’s Pep Guardiola would be jealous of.
It pays dividends to track trends and that is exactly what will occur in this article. In this data analysis, through the use of the data, the statistical trends in MLS will be shown. For this analysis, we’ll be peeling back to the year 2015 season to get a useful context for any patterns or outliers we will observe.
Setting the Reference Guide
Before we continue, we need to establish some basic guidelines and in addition to that, analyze the MLS 2019 season as it is the latest season and represents MLS’ evolution.
Advanced metrics used to analyze recent seasons are not available for the past years so I have done my best to gather as much data WyScout, FBref, and WhoScored. There will be some gaps in the data as looking back that many years is bound to turn up some inconsistencies. In light of this, I’ll be looking over a few areas, namely league styles and goal-shot trends.
Without further ado, let’s analyze the 2019 MLS season first. This analysis is particularly important because it is the one where we have the most data and is the successful evolution of a change in the MLS. By getting a defined picture here, we can easily pick up trends when we start showing the previous years.
For the following analysis, we’re going to conduct a PCA (Principal Component Analysis) of the midfielders in the league. The reason for this as the midfield is the one position where the players give a good indication of a league’s playing styles.
What a PCA allows us to do is look at different statistics and find different groups that correspond to different statistics. For example, Bayern Munich’s Thiago Alcantara will record more forward passes and be involved in buildup passing than Manchester City’s Riyad Mahrez who’ll get more dribbles and crosses. It’s a smart way of analyzing statistics and figuring out styles. The purpose of this PCA is to get an understanding of the style of the latest version, complete version of the MLS. This, then, allows us to see the change over the years.
A total of 17 statistics defensive, attacking, and passing were used. Here were the results.
Here we see the first category the midfielder who plays wider and is involved in the attacking phases. I’ve labelled this midfielder the Winger Midfielder as this midfielder shows traits of a winger. We see this with statistics such as successful attacking actions, dribbles, offensive duels, progressive runs, and deep completed crosses lighting up the most. In essence, these types of midfielders are much more active, are actively taking on their man, and getting many balls in the box through wide and narrow areas.
Conversely, we see other statistics such as final third passes per 90 and defensive statistics showing a negative correlation. This means that these types of players do not put up great defensive statistics or great buildup-play type of passes. This makes sense as they are playing much higher up the pitch and are involved in the ending actions of the team’s attacking play.
