As part of an effort to create a couple league-wide data visualizations before we start to take a more in-depth look a couple interesting teams, I wanted to look at NA LCS teams’ aggressiveness surrounding neutral objectives on the map in the spring split. In order to do this on a basic level, I looked at the number of kills that each team produced at the two major neutral objectives in LoL: the Baron and the Dragon. What I considered “kills at Baron” and “kills at Dragon” were kills that occurred within the vicinity of Baron or Dragon as a result of either a direct, or partial contest of the objective (e.g. a Baron-bait executed from the river brush when a team tried to ward in order to see another team’s progress at the objective). Broadly, I tried to count these types of kills whenever a team directly leveraged a neutral objective to produce a kill. This analysis includes the Baron, and all types of dragons, but not the Rift Herald. Of course, my definition is still subjective to personal opinion, so a more defined model may be adopted in the future. However, even my current standards produced some very interesting data:
In this chart, the NA LCS teams are ordered from left-to-right by the number of kills that they acquired from Baron skirmishes. It’s interesting to note that most of the top teams are located on the right half of the chart, and even more striking is the ratio of kills at Baron to kills at Dragon for top teams.
There is a lot to unpack with this data, and even with the simple chart above. The first basic insight is that the top teams tended to have more kills at Baron, but in some cases, fewer kills at Dragon. To be honest, I’m not sure if this result surprises me or not. Part of me believed that the top teams might have cleaner macro that leads to fewer skirmishes, or opt for fewer contested objectives, but this doesn’t appear to be the case completely. Some uncertainty may linger from how I’ve defined what constitutes the neutral objective kills, but its still fairly clear that top teams are more aggressive around Baron. However, in comparison, they seem to skirmish at Dragon less than some bottom-tier teams, which may indicate a comparatively lessened value of the objective, at least for last split. Do worse teams tend to value dragon more as a consolation prize for Baron, or are they simply misguiding their efforts?
To me, another interesting insight from the graph comes from the ratio of kills at Baron to kills at Dragon. Although top teams tend to have more kills at Baron than worse teams, the ratio of kills at Baron to kills at Dragon might provide some additional insight as to how teams value the two different objectives in comparison to one another. It is possible that I am searching for causation amongst a strong correlation, but the thought experiments surrounding the relationship between kills at neutral objectives and a team’s rank are fun, and certainly intriguing:
A table displaying each NA LCS team’s ratio of kills at Baron to kills at Dragon. The teams are ordered by their rank at the end of the regular season. The green section indicates the top five teams and the orange indicates the bottom five teams. While Golden Guardians seems to stand out the most on the table, the overall trends of kills at Baron vs. kills at Dragon are interesting.
Once again, Golden Guardians sticks out amongst the lower-ranking teams, and Clutch sticks out amongst the playoff teams. Golden Guardians seems to exhibit the tendencies of a top team despite having a roster that is perceived to be weaker. Was this the effect of good shot-calling from Hai? It’s possible. Likewise, Clutch Gaming exhibited a distinct play style from the other playoff teams. However, as mentioned in previous articles, teams can exhibit fairly different play styles and still succeed.
All-in-all, I do believe that this correlative data does have an underlying causation, and the purpose of this work is primarily to identify that this might be a subject worth investigating further by poring over actual game footage to identify potential causes. If anyone can identify concrete causes, altering play style to increase winning chances might be a possibility, which at base is the main goal of most analytics.
And if your brain is fried from overthinking about numbers, it might be time to sit back and watch some pros do what they do best (and worst):