By now, it should be a known fact that the integration of data analytics has been a positive for football. Data plays a crucial role in analyzing team performances, scouting opponents, and, most importantly, seeking out potential transfer targets.
Basically, everything that you see happen on a football pitch can be ticked off and recorded as data. From pass locations and directions, to progressive carries and passes, to the amount of times a player applied pressure on an opponent, everything is jotted down by a set of humans watching a game.
The manipulation and presentation of these statistics have come a long way. More analytics work is being done than ever before. Scroll through the infamous Football Twitter for about 8 seconds and you’ll come across some sort of graph denoting a player or team’s performance in a specific facet of the game. These graphs, whether they come in the form of scatter plots or pizza charts, tell a story. Their creators present the data in such a way that appeals to the human eye. They draw us in.
What is most important, though, is what we do with the information. Many statistics get taken out of context, or are too shallow in that they only scratch the surface of a larger trend. Take the possession statistic, which was once tokenized as the ultimate indicator of an attacking team. Once you dig a little deeper, however, you can see how much a team is actually doing with that possession. (Shoutout to Klopp, Rangnick, and German football as a whole.)
Furthermore, even though a given statistic tells a story worth listening to, interpretations can and will differ.
For example, last season, there was much talk about Heung-Min Son’s drastic xG (expected goals) over-performance. He ended up scoring 17 goals in the Premier League despite only accumulating 10.3 expected goals. The discrepancy between the expected value and the true value caused much controversy.
The fact that he was scoring more than he was “supposed to” was being understood in two very different ways: Son’s ability to score shots that the xG model perceives as low percentage chances can be accredited to the fact that Son is simply better than most attackers. If he’s outperforming his xG that much, he is just better at scoring more difficult chances than the average forward is.
On the other hand, this level of over-production can be perceived as unsustainable, as lucky, even. He isn’t “supposed to” be scoring these chances. Theoretically, at some point, he’ll stop scoring at such a high rate.
The fact of the matter is both are probably correct analyses.
Recently, the Athletic’s Ali Maxwell and famed football data guru Tom Worville (who we had the pleasure of having on our podcast) went to visit MK Dons’ sporting Director Liam Sweeting. MK Dons have been making waves in the English lower leagues, pairing an innovative brand of football with astute data-driven transfer business.
In the interview, Sweeting made a point to discuss one of his player’s xG over-performance. Scott Twine has scored far more goals than his xG tally suggests he should have, potentially worrying the coaches of an impending dip in output. Sweeting made it clear that Twine has historically always outperformed his xG numbers, leading him to remain unconcerned. He went on to say this is the sort of player a club wants in the side, because they can guarantee a certain level of quality.
The same goes for Heung-Min Son. This season, despite Spurs’ early struggles, he is already outperforming his xG (below). In fact, since xG data has been recorded, Heung-Min Son has NEVER underperformed his xG. You can be wary of regression to the mean, naturally, but you can also sit back and admire the South Korean’s talents. You’re allowed to.
You see, stats tell a story because they are a true reflection of what’s happening on the pitch. When watching a player that catches your eye, look up some of his stats on Fbref — a public data site that automatically generates a percentile graph comparing a player to his peers. Delve deeper to decide whether a certain stat is a result of the player’s tendencies and skill or a coached pattern.
By combining your football viewing with some form of statistical education, you’re making yourself a better spectator. If you like the way someone plays, research their stats a bit to create a well-rounded opinion. It isn’t because player X has 10 key passes per game that you have to like them. Remember that all people view football differently.
Stats don’t have to intimidate you. They are just there to keep track of everything that is happening. Pair them with what you actually see, and you’ll be better at watching football.