Week 4 NPL Analysis: Comparing Aggressive and Passive Play Styles

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Why do some NPL teams seem to be able to keep a full squad alive until the late game, while others hang on with only one or two players? Which teams are better defensively, and which teams are better offensively? How do these strengths stack up relative to other teams?

This week I took a stab at answering these questions with a new plot that, well, looks remarkably like my old plot.

Let’s take a look at the old plot first.

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WEEK 4 “LUCK” PLOT

Figure 1: Total points per match for each NPL team based on circle favor, as of Phase 2, Week 4. Teams that are above the black line performed better than expected, and teams below the line, worse. The gray lines represent one standard deviation from the mean in points per match. The dotted trace represents the change in performance from Week 3 to Week 4.

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If you’ve seen my posts before, you know what this plot shows. A breakdown by kill points and placement points can be found here.

Shoutout to Rumblers, Spacestation, and Endemic, who are really outperforming their luck. Wildcard and Denial seem to be trending in the wrong direction this week.

I tweaked this plot a bit, based on my work last week. The proportion of favorable circles on the x-axis here is still the fraction of circles that landed on a team out of all the circles the team was alive for, but what’s new is that I calculated this solely based on circles 3, 4, 5, 6, and 9, which are the only circles that have a significant impact on the game. This new measure of circle favor explains 18% of variance in match results (R2 = 0.1771, p < 1x10-15).

Moving forward, my goal is to try to create models of team performance that incorporate more than just circle favor, because it’s really not the be-all and end-all of team evaluation, as many people have mentioned. But in order to do that, I need to get better at using the PUBG API, so this week I worked on a simpler question.

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AGGRESSIVE AND PASSIVE PLAY STYLES

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What are teams good at, compared to other teams? Are they better at shooting everyone else, or better at staying out of the way? Does playing aggressively always come at a cost?

To look into this, I plotted each team’s damage output versus the damage they allowed against them, averaged over all 32 games of Phase 2 so far.

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Figure 2: Comparison of damage output and damage taken per match for each NPL team, as of Phase 2, Week 4. Teams that are above the black line took less damage than expected based on how aggressively they played, and teams below the line took more damage than expected.

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In this plot, the x-axis is the average amount of damage a team puts out in a match, and the y-axis is the average amount of damage they take from other teams in a match, flipped around so the “good” end is at the top of the graph.

These metrics – damage output and damage taken – very roughly correspond to offensive and defensive skill.

Teams with high offensive and defensive skill can be found on the top right, and teams who are not doing as well as other teams in either aspect are on the bottom left.

The horizontal line is the average damage taken per match, 724.3; the vertical line is the average damage output, 721.8.

The line running diagonally through the graph is the linear model of the relationship between these variables – the expected amount of damage taken based on how much damage is dealt by a team. Teams that are above this line are performing better than expected, relative to the other teams; they’re playing aggressively, but they’re not paying for it too much. Teams below this line are underperforming. The gray is the 95% confidence interval of the linear model – it’s really only useful in that we can be pretty sure that there’s a positive relationship between damage dealt and damage taken.

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TAKEAWAYS

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Based on the linear model of the relationship between damage dealt and damage taken, there is a consistent cost associated with more aggressive play, but it’s much less than 1:1, and not every team falls into that trap. Each time a team generates 100 points of damage, they will take 40 points of damage on average (R2 = 0.4977, p < 0.005).

Teams on the passive side tend to be performing worse in the standings.

Rumblers really stand out here again. They take very little damage relative to other teams, and their damage output is very high.

Spacestation’s damage output comes at a high defensive cost.

There’s an interesting spread in damage output per match, with some teams dealing almost double what others are doing.

Envy and eUnited do well by this metric, with high damage output and relatively low damage taken, but are middle-of-the-pack in terms of overall points. Is this really a case of skill not translating well to points in the standings, or is something else going on? I’m not sure.

Keep in mind that damage taken and damage output might both be biased by time alive, because teams aren’t taking damage when they’re dead. Going back to hockey, as I always do, a better way of evaluating this might have been to translate damage per game to a sort of p/60 – damage dealt or taken per half hour alive rather than per game played – but I didn’t have time to get to that this week.

Overall, this was a surprisingly tough exercise in learning how to use the PUBG API. I’d never tried to work with json files in R before, and it was pretty difficult to transform 32 nested lists of lists, each 16 million lines long, into a workable dataframe, even when I cut it down just to the parts I was interested in. It should be a challenge when I move onto other things like survival analysis, but this was a good start.

I also want to mention Micah Blake McCurdy’s work again, because this week’s analysis is very inspired by his shots for/shots against charts.

The data I recorded and used is here, and my .Rmd file is here, for those of you who are interested.

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tl;dr “Passive” play doesn’t seem to pay off. Rumblers manage to deal a lot of damage while avoiding the costs of playing aggressively, Spacestation pays a high price for their high damage, and Pecadores are the most passive team. Rumblers, Spacestation, and Endemic are outperforming their circle favor this week.

Edited to filter out irrelevant damage types.