A Proper Analysis

Barring a string of freak losses, Liverpool likely will finish the season with the best goal differential.

On the other hand, Southampton continues to flirt with one of the worst differential records in recent memory.

Outside of the outliers, most clubs tend to fall within a 20-or-so goal difference, asking: What does goal differential really mean?

At the top of the table, Liverpool, Arsenal, and surprisingly Nottingham, rest as the kings of the goal differential charts. For almost every other club, besides the bottom of the table, a healthy range separates them.

Without any further analysis, goal differential helps identify the balance between the attack and defense. A pattern exists where the more positive the difference, the better the club’s performance, and vice versa. Since the inception of the sport, goal differential stats offered an easy way to measure and understand a team’s performance across a season.

However, take, for example, Aston Villa, a pretty decent club this season that currently has a negative goal differential. Conversely, Tottenham holds a better goal differential than Newcastle despite being closer to the relegation zone than European spots. What these discrepancies suggest is that the goal differential is entirely dependent on context, like any stat.

Similar to other discussions on statistics in football, examining goal differentials requires a different approach.

While other sports do present a good path towards analysis, it must be molded for football.

Goal differential presents itself as the most digestible of footballing statistics. Like possession or shots, they lack the further context required for other stats, such as expected goals.

It allows for anyone observing to, at face value, assess how balanced a club is. Looking at Arsenal shows a club that should be successful, while Leicester’s struggles are obvious. When examining the outliers, this stat is easy to understand.  

As you move between the top and bottom ends of the table, however, you run into issues applying the same system to other teams. How can, for example, the base notion of goal differential explain the middle of the table?

This exposes the issue of limited statistics, a type that allows for initial inferences but lacks complexity. That issue allows for the rise and implementation of more complex ideas. Expected goals, goals allowed, and differential all give you the ability to better understand the real character of a team.

Expected statistics take into consideration more than just the physical act of the ball going into the net on either side. Utilizing passes and movement allows for a better evaluation of a team. Yes, the outliers still remain at the top or bottom, but the rest of the table becomes clearer. 

For example, take Crystal Palace, who currently find themselves underperforming their expected goal differential by almost six. When you compare their expected numbers, they clearly have an offense that is struggling to score more efficiently. This makes sense, as outside of Mateta, they lack any scoring threat that can capitalize on the chances created.

Like dipping a toe in the water to check the temperature, goal differential is the surface layer of any deeper statistical inference.

It also highlights the sport’s slow grasp of data analytics.

Another key component of understanding goal differential and its shortcomings is by looking at the context a team finds itself in. After all, statistics inherently lack any meaning without the proper context.

Both teams mentioned earlier find themselves in differing spots. For Tottenhan, Postecoglou’s project is in trouble, while Villa are in a dog fight for Europe.

PHOTO: r/coys/Reddit

Tottenham’s goal differential suggests a club that is firmly near the top six, not one struggling to stay behind United. To say that the Spurs’ season from hell boils down to bad luck is not entirely wrong. Postecoglou’s side dominated the likes of City and Villa in the early part of the season, only to lose to Leicester in January. 

Outside of the occasional draw or one-goal-difference match, the Spurs’ rather elite attacking record is buoyed by a number of lopsided wins. Several 3 or 2-goal difference wins litter their season, but those are the majority of wins they have. While their numbers aren’t totally misleading, they hint at a team that lacks consistency.

Villa, on the other hand, still have plenty to play for down this stretch. Even though they possess a negative goal differential, with their defense conceding one more goal than scored, they aren’t bad. Unlike the Spurs, their results show a consistent team that can challenge the best.

Outside of drubbings at the hands of Palace and Spurs, most of their games tend to end with a one or two-goal differential. Emery clearly sets his teams up to play like this with relatively little risk on either side. This ensures that the only blemishes are those two losses, but they contribute to the team’s rather poor defensive record.

When evaluating goal differential, context is necessary.

Without that, it becomes impossible to properly evaluate teams that fall outside of the top or bottom of the table.

As football continues to embrace data analytics, the methods used to understand teams and their performances will change.

Goal differential, a long-time component of the sport, will mutate into a better measurement of a team’s makeup.

Football still lags behind other sports in terms of its adoption of data, but there’s so much waiting to be discovered.

PHOTO: r/coys/Reddit