You can see that the 2016 Montreal Impact created a lot more chaos in front of the box than the 2017 Impact do. Either they’ve become more clinical, or less goal-dangerous. Perhaps a similar analysis on passes and shots would shed light on that question. New England last year also employed a higher line of confrontation than they do this year. If one simply watched the games without looking at the data, these kinds of differences might actually go unnoticed.

There are other potential applications for these kinds of results too. There’s so much detail in these heatmaps, that you might imagine analyzing how a particular team performs against a type of team with a very different style. Perhaps there are patterns within matchups that seem like unpredictable wins and losses when they pan out in real life, but would have been entirely predictable if the coaches (or betting lines!) had looked carefully at the history of one style vs another.

We might also imagine examining these maps with and without a key player, to see how someone’s presence and absence impacts a team (like a more advanced version of this analysis I did a little while back). Or perhaps we could identify a turning point in a coach’s tactics, and use these to assess what it led to.

We could even make player-specific maps and repeat any of the above potential analyses.

There are, of course limitations to this. For one, it requires a huge amount of data to be able to pick up a reliable signal. Trying to estimate these heatmaps from one game to the next, useful as that would be, is kind of infeasible. These maps also don’t say anything about uncertainty. For some teams, the predicted heatmap might be a highly-accurate representation of their style, but for others, it might just be the midpoint of very blurry patterns. In that sense, they give the reader a false sense of certainty about team style. There’s also a multitude of statistical flaws (or at least weaknesses) to the model I’ve chosen. The most important consequence I've noticed is that this model tends to exaggerate when there are "islands" of action (like the Jozy Altidore/Sebastian Giovinco island for Toronto, or a number of the teams with hot spots around the corner flags). Those teams really do have tendencies to defend in those areas, but the heatmaps are way too dark there to be realistic. A better model, something like integrated, nested Laplace approximation, would probably avoid those exaggerations, but it’s kind of overkill for the first analysis of this kind. Finally, there’s the matter of whether the defensive action was actually successful. The maps above just show where attempts are made, but I could make completely separate maps (and I actually have already) about the probability of an attempt leading to a turn-over. Those maps tell a different side of the story that I may save for another article.

Whether it’s used to predict the results of future games, analyze the impact of a major change, or just summarize a team’s style at a glance, spatial analysis like this is a natural fit for a fluid, tactical game. Really, I think the potential analytical uses for this kind of analysis are enormous. They’re also just kind of mesmerizing.

Enjoy!