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The most important thing to consider when building a DFS lineup is player salary. A player’s price point is a lot like a betting line. Most of the information that you are taking into account in your own research is already baked into this price. If you want to have any chance of making money over the long haul in DFS, you have to be very sensitive to and smart about salaries.

Using Python coding, I went back and collected all the relevant data from the 2019 season in order to do some advanced analytics into the DFS game. What I ended up with is a giant data set of each individual game line for every hitter and pitcher in 2019. Here’s is a quick screenshot of what I have for Max Scherzer, just as an example:

Having this data for every single player in the league gives us all kinds of opportunities for exploration.

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Question 1: How Good, Generally, is the DraftKings Pricing Algorithm?

What I wanted to attempt to answer in this post is the question of how much predictive power pitcher salary has on DraftKings points output.

We can compare salaries and points scored to see how they are related with this data. The goal of the DraftKings pricing algorithm should be to achieve a linear relationship with points scored. Of course, this is impossible, but that should at least be the goal they have in mind when setting prices in order to make the game as competitive as possible.

Simply put, if the prices were done all wrong, the players who really knew what they were doing would absolutely dominate the casual player and they would quickly scare them all away. This is not what the DFS site wants, of course. They want some semblance of every kind of player having a chance to win so as to encourage as many players as possible.

What we would see if the pricing algorithms were perfect would be something like this:

What we actually see from the 2019 data is this:

The answer to the question is: pretty darn good. As the salary on a pitcher rises, so will the average points they score. There are some outlier price points in the results (for example, pitchers priced at $10,100 averaged 5 less points than pitchers at $10,000 and $10,200), but those are almost surely just an outcome of randomness.

The conclusion here is that, in general, there is really not much of an edge to be garnered when selecting pitchers. Their salary seems to have enough cooked into it to level the playing field enough. A much more important, and much tougher question to answer would be if this is true on the individual player level as well.

Question 2: Does Salary Predict Points at an Individual Pitcher Level?

I don’t believe this is a question we can get a consensus answer on, because it is so different between all the players, and there is no real great way to test them all together. We can still learn some things from trying though.

What I did here was loop through every pitcher who made more than 20 starts last year and find their average DraftKings points outputs at each price point. Before doing that, I rounded their salaries each to the nearest $500 mark so as to make the groups a little bigger. For example, a $9200 pitcher will go into the $9000 category, and a $9300 pitcher will go to $9500.

This ended up looping through 111 different pitchers, and I have a plot like this for each of them:

Adding the trend line makes it a little easier to learn quickly by just looking at all of the plots together. As you can see, Aaron Nola did his best work around the $8000 mark last year, his worst at the $8500 mark, and everywhere between $9000 and $10,500 was pretty much the same.

Of course, doing this for one pitcher does not mean anything. We cannot say that you should always pay for pitchers when they are priced way down just because Nola had a couple great starts when he was very cheap

To draw some not perfectly scientific conclusions, I went through all of the plots and marked down if the trend was “up”, “down”, or “flat”. The results were interesting, it was about an even split – 39% of the charts trended downwards, 36% trended upwards, and the rest I judged as being pretty flat.

As unscientific of a result as that is, it still does tell you that the salary does not do a great job at predicting points totals at the individual player level. It suggests that the DraftKings algorithm might factor in recent performance and matchup too heavily in the pricing for a lot of pitchers, making it advantageous for us to play them when their salaries are depressed even if it doesn’t feel very good to do.

If you would like to have a look at all of the plots yourself, you can check them out here.

More Thoughts on the Matchup

We know that the team the pitcher is facing has a lot to do with what their price will be, so I was curious to see the average DraftKings points scored by starting pitchers against each team last season. Here is that data:

You can see here that the Marlins, Tigers, White Sox, Padres, and Giants were significantly better matchups for pitchers than the rest of the league, and the Twins and Astros were really awful matchups. However, the rest of the league all fell within five points of each other in terms of average SP points scored against them. You may have been much more excited to draft a pitcher going against the Royals last year rather than the Cubs, but at the end of the season those matchups did not turn out to be much different.

I will be bringing more of this kind of DFS content to you as we (hopefully) approach a condensed 2020 season. Please reach out to me if you have any questions or ideas for future posts! Thanks for reading, and keep it locked right here on FantasyPros.

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