Vegas lines are important for every DFS sport, but MLB DFS is probably the sport for which line movement in particular is most important. With our new Vegas Dashboard, I have been studying implied runs and movement throughout the day more than I have at any time for any sport.
One question I’ve kept coming back to while researching is how to quantify movement. We can say that movement of 0.2 runs is significant, but just how significant is it? And does baseline matter? It seems like a line moving from 3.1 implied runs to 3.3 is different than a line moving from 4.7 to 4.9. Or is it different? You can see the sort of rabbit holes you can venture down when researching this subject.
In exploring these questions, I created what I’ll call a “movement matrix,” which shows the historical Plus/Minus for batters based on their line movement.
A Word of Warning
I’ll post the graphic in one moment, but I first want to give a word of warning. The matrix shouldn’t be used to target a specific movement. For example, you will see that batters have had a Plus/Minus of -2.17 when the line has moved from 4.2 to 5.1 runs and a Plus/Minus of +6.09 when the line has moved from 4.3 runs to 5.1 runs. That discrepancy is a result of small sample size. Of course, you shouldn’t target a team that moved to 5.1 runs simply because it started at 4.3 instead of 4.2.
There are small samples in the first graphic, in part because lines rarely move more than about 0.3 runs. I included all historical movement for teams projected between 3.0 and 5.5 runs, but be aware that the numbers closer to the edge of the distribution have significantly smaller samples than the numbers concentrated closer to the center.
So that you can see the impact of line movement more clearly, I’ve also posted a second graphic including only movement that does not exceed 0.3 runs in either direction. Of the two graphics, I would lean more heavily on the second one.
Yes, I Still Have More to Say!
The matrices are best considered from a bird’s-eye view. With the conditional formatting, the data and value of the line movements should be fairly easy to visualize. Individual numbers in the graphics might be intriguing — but look less at the individual numbers and more at the colors. As you’ll see in the second graphic, batters on teams with high-projected totals have generally done well from a salary-based perspective even with some negative line movement. That larger trend is more important than an individual number.
Here’s a quick note on how to read the graphics: the vertical axis (y) is the final run total and the horizontal axis (x) is the opening run total. For example, look at the -1.02 number three boxes in and two boxes down. That number is the Plus/Minus of batters when their teams opened at 3.2 runs and went down to 3.1 runs.
The Movement Matrix: Feast Your Eyes
Okay, without further ado, here is the first graphic (click to enlarge):
And here is the second, showing movement of no more than 0.3 runs:
The first thing that stands out to me is that the graphic looks “right.” Generally, it increases from negative to positive as the runs increase.
The Takeaways
What’s intriguing and applicable is that an increase in runs on the lower end didn’t really boost batter value at all. The lesson here seems to be that low-implied teams are low-implied teams. Regardless of movement (unless it is extreme, which is rare and happens only with a late pitcher scratch or something), those teams tend to perform poorly.
The movement you want to focus on is at the upper range. When a team is already projected for a lot of runs, positive line movement has shown to be really valuable, especially as you get into the 5.0-projected-runs territory.
The two takeaways for me are A) ride the Vegas lines, and B) don’t get fooled by line increases for low-projected teams.
There is a strong correlation between value and projected run total. Don’t get cute and go away from that in your cash games. Likewise, in both tournaments and cash, don’t get fooled into thinking that because a team increased its run total by 0.2 runs from 3.1 to 3.3 runs that you’re all of a sudden getting an immense value.
