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So far, we’ve discussed the best way to create projection baselines for PlayLine NBA contests and how to analyze ownership to find inefficiencies. These concepts are the building blocks to a successful DFS plan. Today, I’ll be taking a look at stat correlation and how that could/should impact your decisions.
To start, I needed data. I grabbed the box score output for every game in the 2017-18 season through Game 1 of the NBA finals. I filtered out all individual performances that had less than 20 minutes played so that we’re generally looking at a similar set of players. This dataset has 17,093 entries, just for reference. After that, I calculated the correlation between the stats that PlayLine uses for their contests: Points, Rebounds, Assists.
As you can see, these three stats are positively correlated. For those that are unaware, the correlation scale runs from -1 to +1, with 0 meaning there is no relationship between the values. In the case of these stats, points shows a decent sized correlation to rebounds and assists. This is important to note with regards to making your PlayLine prognostications. The relationship between rebounds and assists, however, is nowhere to be found. What this tells me, at first glance, is that when I‘m making my projections for an individual player, I should increase my expected rebounds and assists as I increase my projection for points. If we have Player X with averages of 20-5-5, but I‘m projecting 28 points, it would be in my best interest to increase the rebounds and assists.
If only it were that easy. I decided to take a look at some individual player correlations to see if the relationship was consistent on a player-by-player basis. Spoiler alert: it wasn’t.
For example, LeBron James has the following correlations for this season:
Pts x Ast: .01
Pts x Reb: .13
Reb x Ast: .18
Compare that to Russell Westbrook:
Pts x Ast: -.27
Pts x Reb: -.09
Reb x Ast: .22
Well, that’s…different. The Russell Westbrook example flies in the face of the general correlations, but it makes 100% sense. As Russ is scoring more, he’s getting less assists. If you’ve seen Westbrook play, that matches the eye test. He also has a high correlation between assists and rebounds, which again makes perfect sense, as Russ is usually chasing both of those stats for his triple-double quest.
There are a few conclusions to draw from this information, but they’re all rooted in similar logic. When playing at PlayLine, you want to be aware of how stats correlate. If you’re short on data, I would use the overall correlations when thinking about your personal projections. If you’re projecting players that have a solid sample size, you’re likely better off checking their individual correlations. This can be done easily by copying a player’s game logs from Basketball Reference and pasting them into Excel. You can run a correlation on two columns using the CORREL function. Just type =correl(Column1,Column2) in any cell, where Column1/Column2 corresponds to the pieces of data you want to test. If you have any questions, you can reach out here in the comments section or email me: [email protected]. Try it out tonight!
