Effect of Reverse Line Movement on DFS Player Performance

Not all Vegas lines are created equal. For DFS players (or any fantasy players, for that matter), one of the most notable occurrences in the betting lines is reverse line movement.

What's reverse line movement and why is it so notable?

In most cases, when the majority of bets are on one team, the point spread, or line, moves in favor of that team. Reverse line movement, however, is when the line moves against the majority of public bets. For example, when a team is favored by six points and is getting 65 percent of public bets, the sports book would move the line up to -6.5 to try to even out betting in most scenarios. But if the line goes in the opposite direction to -5.5 instead, you have reverse line movement. A reverse line move indicates that either the sports book is very confident the public is wrong or that large amounts of money by professional bettors is coming in against the public, creating a situation where more money is on the team not getting the majority of bets. Either way, someone much smarter than the public is in disagreement.

The most common application of this in DFS is to follow the reverse line move by either targeting players on the team that the move favors or fading players on the team that the move is against.

But is reverse line movement actually predictive of fantasy value?

This article has been updated to reflect data through the 2017 season.

Measuring the Impact of Reverse Line Movement

Since 2013, there have been 465 games with reverse line movement. For each of those games, I compared actual PPR points scored for players at each position to their 4for4 projection. I then compared the differential in projected to actual points in games with reverse line movement to the differential for all games during that span. Any major discrepancies in fantasy output between each scenario would suggest that reverse line movement has a predictable impact on fantasy scoring.

Player Performance in Games with Reverse Line Movement

Here is the data on how each position has scored relative to 4for4 projections, both overall and for games that included any reverse line movement, over the past four seasons:

_{Positional rank determined by players who had the highest projection on their team in a given week.}

When compared to every game over the last four years, games with reverse line movement saw the biggest difference in fantasy output from QBs on teams when the line moved in their favor and from RB1s on teams who saw the spread move against them—in both situations, these positions performed marginally better than the overall average difference. The discrepancies are so marginal in every situation, though, that there doesn't seem to be any actionable takeaways from this data alone, so to help clarify things, let's take a look at more data in the form of percentage of bets and actual spreads.

The Effect of Percentage of Spread Bets

The following table shows how players at each position performed relative to their projection based on the percentage of bets coming in on their team:

_{*The sample size for percentage of spread bets 75% and greater was too small to justify an additional bucket.}

Here are the takeaways for each position:

Quarterback

• As betting becomes more disproportionate, QBs on teams with reverse line movement in their favor perform better relative to expectation.
• Recall that as a whole, QBs perform below expectation when there is reverse line movement against their team. So why does every bucket in the above table show QBs performing above expectation? Because it was the QBs not captured in the table who were responsible for the below-expectation performance: QBs on teams with reverse line movement against them that got less than 55 percent of public bets collectively scored 2.13 fantasy points per game below their projection, and QBs on teams with reverse line movement in their favor that got more than 45 percent of public bets collectively scored 0.98 fantasy points per game below their projection.

Running Back

• RB1s on teams with reverse line movement in their favor perform worse as betting becomes more unbalanced, while RB1s on the other side of the line movement perform better.

Wide Receiver

• Scoring is somewhat erratic for WR1s, but WR2s and WR3s on teams with the line moving against them see their production decline as betting becomes more disproportionate.

Tight End

• A team’s primary tight end improves as betting becomes more imbalanced if the line is moving in their team’s favor, but the improvement is not significantly different than the overall league average relative to projections.

Kicker

• Unbalanced betting has led to a drop in kicker production, no matter what side of the line the kicker is on, but the difference is negligible relative to the overall league average versus projections.

Explaining the Reverse Line Movement Trends

Some of the aforementioned trends might seem counterintuitive until you consider why reverse line movement happens: The public tends to bet heavily on favorites and the biggest favorites usually garner the most attention. Consider the average spread for games that have seen reverse line movement over the last four seasons:

This is why we see a trend like running backs performing better even when the line is moving against them; the teams with a large percentage of bets are often huge favorites and usually remain big favorites even after the line moves.

Most reverse line movement is subtle. The average line moved just .10 points and only 42 of the 465 games (9%) with reverse line movement since 2013 saw the line move by three points or more.

Putting Reverse Line Movement Data Into Action

Unless there is massive line movement or a favorite becomes an underdog, it’s best to take reverse line movement for what it more often than not is: A slight correction to the public being overconfident on a team that is probably going to win, just not by as much as the public thinks.

Even after there is reverse line movement, the closing lines are still the main consideration, and the principles detailed in DFS studies such as the DFS Playbook and Big Game Profiles should still be applied.

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