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MLB DFS: Hitter Trends that Matter

Here at FantasyLabs, we have a dizzying array of data at your fingertips. If you so choose, you can simply load one of our MLB Pro Models and build your lineups from there. That’s a solid strategy and one that can be used to turn a profit (coupled with solid lineup construction and game selection, of course).

However, if that’s how you want to play, this article isn’t for you. We’re going to take a look at some potential trends in our MLB Trends tool to consider for MLB DFS and how we can use them to build our own lineups (or even our own models, if you so choose) to gain an edge on our competition.

One of the great – and frustrating – things about MLB DFS is the high-variance nature of the sport. Particularly for hitters with their small sample sizes. LeBron James is never going to score 0 points in a game (barring an injury), and neither is Tom Brady. Ronald Acuna Jr. – last year’s highest-scoring player by points per game – put up goose eggs in 10% of his outings last year.

A side effect of that is we need to be far more sensitive to ownership in MLB than other sports. It’s simply not worth eating the chalk on a player with such a high failure rate. He’s not appreciably more likely to post a “have to have it” score than other less popular players, but our benefit is far less if he does. As an example, Peter Alonso has the exact same (18%) upside score last season but at less than half the ownership.

While that’s more of a GPP consideration, his consistency score matches Acuna’s as well. (There’s a handful of other players who fit this bill too, simply sort by “upside” in the player list in the Trends Tool.)

The difference is Acuna’s best games are 40+ points, where Alonso falls in the 30s. However, most days, nobody scores 40 points. In that case, getting a lower owned (and cheaper) player who scored 30 is just as – if not more – valuable.

With all of that out of the way, let’s dive into some data on trends that matter in MLB DFS.

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Park Factor and Weather Rating

My two favorite data points in our Player Models are the Park Factors and Weather Ratings. It’s fairly obvious that both of those things matter tremendously in baseball, but parsing out every factor yourself is difficult. Fortunately, our proprietary scores look back at the impact of each Weather Factor (temperature, humidity, wind speed, etc) and every park has on fantasy scoring. This saves us the trouble of trying to decipher whether a bump in humidity is worth sacrificing temperature or wind speed, for example.

Things get really interesting when you combine the two factors, though.

Here’s what it looks like when you take the top half of parks and combine that with top-30 percentile Weather Ratings:

(Not pictured in the screenshot, but the upside score is 18%. That’s the same as Acuna’s in all games.)

Pretty solid! We’re getting three-quarters of a point on average, but with Acuna-esque upside from an average player. However, we’re eating just over 3% ownership on average. We can do better.

First, we can invert the Park Factors and Weather Ratings. This gives us the top 10 parks on any day with above-average Weather Ratings. When we do that though, our Plus/Minus goes down, and our ownership goes up. Let’s move on.

What happens if we remove Coors Field from the equation? Coors is notorious for its great hitting conditions, but that’s baked into the price and ownership equation.

Here’s how that trend looks without Coors Field:

(Upside dips to 17% here.)

That’s the ticket. While we’re losing roughly a quarter of a point on average, our ownership drops 2.2%. Players in these conditions are owned only 0.8% more than average but get us an extra half-point while boosting upside by 2%.

Another factor here is whether there’s a game at Coors that day or not. It’s not possible to do this in the Trends tool, but my intuition would be that ownership is further reduced elsewhere when the Rockies have a home game. (Conversely, ownership probably approaches Coors levels at the best park when the Rockies are on the road or off the slate.)

That means playing the second-best situation for your stacks has extra value.

Handedness Splits

Another factor worth considering is the splits hitters have against the handedness of the (starting) pitcher they’re facing. Some hitters are fairly agnostic to their opponent’s throwing hand, while some demonstrate extreme differences.

We have two ways of evaluating that in our Player Models, ISO and wOBA differential. ISO (isolated slugging) is a measure of power, while wOBA (weighted on-base average) tries to encompass the total value provided in every at-bat.

This analysis is based on players being on the “right side” of their splits: positive differentials compared to negatives.

However, it didn’t really change much if stricter criteria were used. (Splits of at least 0.1, for example.)

As you can see, ISO is slightly more impactful, but neither really provides much value. Both came at a slight ownership cost while providing less than 0.2 extra Plus/Minus. There’s a key reason for that. Hitters may only have half of their at-bats against the starting pitcher, or even less when teams use an “opener.” As teams pull their starters quicker and quicker every year, that ratio is further decreasing.

We can potentially get around that in some cases, though. A righty sandwiched between two left-handed hitters is far more likely to face a lefty reliever, for example. We can also look at the composition (and recent usage) of opponent bullpens for a clue as to the likeliest relievers to come in. If both of a team’s lefty relievers pitched the day before, it’s likely that right-handed relievers do the bulk of the work.

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Sign up and deposit up to $100

Your deposit will be fully matched

New users only

Umpires

Our models also list (as soon as available) the home plate umpire for each game. This is tremendously valuable for baseball. Strike zones are (maddeningly) subjective, and some umps call games in a way that benefits hitters more than pitchers, or vice versa.

Over the last two seasons, there were eight umpires that produced hitter Plus/Minus scores over 1.0. Let’s look at the data when those eight were behind the plate:

That’s a huge difference! Hitters scored an extra 1.28 points in games umpired by them. Their upside also jumps all the way to 22%. That’s well above the 16% average. Even better, ownership is actually lower in these games. I’m sure that’s just noise, as players aren’t actively avoiding hitters in these games. They certainly aren’t seeking them out either, though.

This is impactful and well worth the price of a FantasyLabs subscription in and of itself. Our data is back-tested across every game and gives you instant access to the past performance of every umpire since MLB DFS began on DraftKings.

In peer-to-peer games like DFS, it’s not what you know that matters. It’s what you know that your opponents don’t. Labs subscribers have a major edge over their competition with this one.

You can check out the pitcher version of this article here.

Also, don’t forget that for large-field tournaments, you can utilize our Lineup Optimizer to effortlessly create up to 150 lineups, or use our Lineup Builder if you like to hand-build your lineups.

Here at FantasyLabs, we have a dizzying array of data at your fingertips. If you so choose, you can simply load one of our MLB Pro Models and build your lineups from there. That’s a solid strategy and one that can be used to turn a profit (coupled with solid lineup construction and game selection, of course).

However, if that’s how you want to play, this article isn’t for you. We’re going to take a look at some potential trends in our MLB Trends tool to consider for MLB DFS and how we can use them to build our own lineups (or even our own models, if you so choose) to gain an edge on our competition.

One of the great – and frustrating – things about MLB DFS is the high-variance nature of the sport. Particularly for hitters with their small sample sizes. LeBron James is never going to score 0 points in a game (barring an injury), and neither is Tom Brady. Ronald Acuna Jr. – last year’s highest-scoring player by points per game – put up goose eggs in 10% of his outings last year.

A side effect of that is we need to be far more sensitive to ownership in MLB than other sports. It’s simply not worth eating the chalk on a player with such a high failure rate. He’s not appreciably more likely to post a “have to have it” score than other less popular players, but our benefit is far less if he does. As an example, Peter Alonso has the exact same (18%) upside score last season but at less than half the ownership.

While that’s more of a GPP consideration, his consistency score matches Acuna’s as well. (There’s a handful of other players who fit this bill too, simply sort by “upside” in the player list in the Trends Tool.)

The difference is Acuna’s best games are 40+ points, where Alonso falls in the 30s. However, most days, nobody scores 40 points. In that case, getting a lower owned (and cheaper) player who scored 30 is just as – if not more – valuable.

With all of that out of the way, let’s dive into some data on trends that matter in MLB DFS.

Start Your PRO Trial Today

Lineup builder and optimizer

Real-time DFS models

Data-driven analysis & tutorials

Park Factor and Weather Rating

My two favorite data points in our Player Models are the Park Factors and Weather Ratings. It’s fairly obvious that both of those things matter tremendously in baseball, but parsing out every factor yourself is difficult. Fortunately, our proprietary scores look back at the impact of each Weather Factor (temperature, humidity, wind speed, etc) and every park has on fantasy scoring. This saves us the trouble of trying to decipher whether a bump in humidity is worth sacrificing temperature or wind speed, for example.

Things get really interesting when you combine the two factors, though.

Here’s what it looks like when you take the top half of parks and combine that with top-30 percentile Weather Ratings:

(Not pictured in the screenshot, but the upside score is 18%. That’s the same as Acuna’s in all games.)

Pretty solid! We’re getting three-quarters of a point on average, but with Acuna-esque upside from an average player. However, we’re eating just over 3% ownership on average. We can do better.

First, we can invert the Park Factors and Weather Ratings. This gives us the top 10 parks on any day with above-average Weather Ratings. When we do that though, our Plus/Minus goes down, and our ownership goes up. Let’s move on.

What happens if we remove Coors Field from the equation? Coors is notorious for its great hitting conditions, but that’s baked into the price and ownership equation.

Here’s how that trend looks without Coors Field:

(Upside dips to 17% here.)

That’s the ticket. While we’re losing roughly a quarter of a point on average, our ownership drops 2.2%. Players in these conditions are owned only 0.8% more than average but get us an extra half-point while boosting upside by 2%.

Another factor here is whether there’s a game at Coors that day or not. It’s not possible to do this in the Trends tool, but my intuition would be that ownership is further reduced elsewhere when the Rockies have a home game. (Conversely, ownership probably approaches Coors levels at the best park when the Rockies are on the road or off the slate.)

That means playing the second-best situation for your stacks has extra value.

Handedness Splits

Another factor worth considering is the splits hitters have against the handedness of the (starting) pitcher they’re facing. Some hitters are fairly agnostic to their opponent’s throwing hand, while some demonstrate extreme differences.

We have two ways of evaluating that in our Player Models, ISO and wOBA differential. ISO (isolated slugging) is a measure of power, while wOBA (weighted on-base average) tries to encompass the total value provided in every at-bat.

This analysis is based on players being on the “right side” of their splits: positive differentials compared to negatives.

However, it didn’t really change much if stricter criteria were used. (Splits of at least 0.1, for example.)

As you can see, ISO is slightly more impactful, but neither really provides much value. Both came at a slight ownership cost while providing less than 0.2 extra Plus/Minus. There’s a key reason for that. Hitters may only have half of their at-bats against the starting pitcher, or even less when teams use an “opener.” As teams pull their starters quicker and quicker every year, that ratio is further decreasing.

We can potentially get around that in some cases, though. A righty sandwiched between two left-handed hitters is far more likely to face a lefty reliever, for example. We can also look at the composition (and recent usage) of opponent bullpens for a clue as to the likeliest relievers to come in. If both of a team’s lefty relievers pitched the day before, it’s likely that right-handed relievers do the bulk of the work.

Get Your First Deposit Matched Up to $100!

Sign up and deposit up to $100

Your deposit will be fully matched

New users only

Umpires

Our models also list (as soon as available) the home plate umpire for each game. This is tremendously valuable for baseball. Strike zones are (maddeningly) subjective, and some umps call games in a way that benefits hitters more than pitchers, or vice versa.

Over the last two seasons, there were eight umpires that produced hitter Plus/Minus scores over 1.0. Let’s look at the data when those eight were behind the plate:

That’s a huge difference! Hitters scored an extra 1.28 points in games umpired by them. Their upside also jumps all the way to 22%. That’s well above the 16% average. Even better, ownership is actually lower in these games. I’m sure that’s just noise, as players aren’t actively avoiding hitters in these games. They certainly aren’t seeking them out either, though.

This is impactful and well worth the price of a FantasyLabs subscription in and of itself. Our data is back-tested across every game and gives you instant access to the past performance of every umpire since MLB DFS began on DraftKings.

In peer-to-peer games like DFS, it’s not what you know that matters. It’s what you know that your opponents don’t. Labs subscribers have a major edge over their competition with this one.

You can check out the pitcher version of this article here.

Also, don’t forget that for large-field tournaments, you can utilize our Lineup Optimizer to effortlessly create up to 150 lineups, or use our Lineup Builder if you like to hand-build your lineups.