Daily Fantasy Baseball: The Immense Value of Hard-Hit Rate

Just looking at the name of various statistics, you'd think you're sipping on an alphabet soup. You've got your wOBA, your wRC+, your BABIP. Some are common sense once you're familiar with them, but if you're not, your eyes will be swimming.

Then you get a stat like hard-hit rate. The stat's utility is right in its name. Bless you, benevolent Sabermetricians.

It isn't just simplicity that makes a stat like hard-hit rate our shining beacon of Gucciness, though. The data that it provides is something that can help take our daily fantasy baseball performance beyond what most other stats can do.

Let's run through just why hard-hit rate is as good as it sounds. If they're going to make things easy on us by presenting us with the golden goose of statistics, we may as well take advantage.

How Predictive is Hard-Hit Rate?

Before I gush about why you need to use hard-hit rate and its various applications, I suppose I should show to you its superiority over other batted-ball stats. That might be a wee bit important.

We can do this using correlation coefficients, which illustrate how closely two sets of numbers are tied to each other. The closer a correlation coefficient is to either 1 or -1, the stronger the relationship is between the two numbers.

The other batted-ball stats we'll look at are soft-hit rate, fly-ball rate, and ground-ball rate. These are the other main batted-ball stats that you can find on FanGraphs, and they'd all have fairly logical applications when it comes to predicting extra-base hits in MLB DFS.

We want to find out how closely each of these numbers is tied to various stats. If we were able to see that fly-ball rate had a heavy correlation to slugging percentage, we'd want to focus on that when doing our research for a slate of games so that we can find guys who are likely to snag some extra-base hits.

While we're on the topic, let's start things off with slugging percentage. The table below shows the correlation coefficient between our various batted-ball stats and slugging percentage for all players who had at least 200 plate appearances in 2015. Because of the relatively small sample sizes, you'd assume that each of these correlations would become stronger as the minimum number of plate appearances increased.

If that doesn't get you hot and bothered, you'd best check your pulse, bruh. That's a work of beauty.

If you recall our piece on which hitting stats best correlate to fantasy points, slugging percentage led the way. Clearly, if hard-hit rate is closely tied to slugging percentage, it's going to be something we'll want to investigate closely.

Now, this may get you thinking that hard-hit rate is good in theory, but that it may not actually translate into predicting fantasy points. After all, it doesn't account for how often a guy walks or strikes out, and it doesn't change based on park factor or surrounding lineup. That would seem to make it a solid yet incomplete stat.

I don't blame you for thinking that at all, and all of those are things you should be considering. But hard-hit rate is just too good to be denied.

Below are the correlation coefficients for each of our batted-ball stats to FanDuel points per plate appearance. This is from the same sample of batters as above from the 2015 season who had at least 200 plate appearances. The scoring is based on the rules FanDuel implemented prior to the 2016 season, and it looks like hard-hit rate even has utility there despite not fully accounting for other aspects.

For some context, in that same sample, batting average had a correlation coefficient of .553 to FanDuel points per plate appearance. Hard-hit rate -- which doesn't take into account the result of the hard-hit ball -- had a higher correlation to scoring than a results-based statistic.

Hot momma.

If you were to combine hard-hit rate with the things we discussed before such as strikeout rate, walk rate, and park factor, you'd have a top-notch understanding of what to expect out of a hitter. This is why this is such a brilliant tool to use when you're researching DFS.

There is one major aspect of this that we haven't tackled yet, and that would be home runs. Often, when we're filling out the end of our rosters, we want players who have big-time upside. While guys with high hard-hit rates are going to have more extra-base hits over the course of a season, that doesn't necessarily mean they'll present the highest upside with long-ball potential on a given night. I would have assumed that honor belonged to batters with high fly-ball rates.

I would have assumed wrong.

Here's the same information, except with the correlations for each batted-ball category to home-run rate. If fly-ball rate had a higher correlation coefficient here than hard-hit rate did, I'd concede that you could make an argument in its favor. That's just not the case.

Even in the arena that should have been fly-ball rate's playhouse, hard-hit rate still ran laps around it. That's game, set, and match, home slice.

Fly-ball rate is still going to have its value, and it's something you should be utilizing in your research. It should just be in conjunction with hard-hit rate as opposed to instead of it.

For example, let's say you're choosing between two players and looking for homer upside. Both batters have similar hard-hit rates, but one is more fly-ball heavy. As you can tell from the correlations above, that's going to be conducive to some dingers, so you're going to want to favor the fly-ball hitter. But if there's a chasm between their hard-hit rates, then you should be looking toward the guy making better contact.

How to Implement Hard-Hit Rate Into Research

All of this knowledge of how truly dope hard-hit rate is won't do you a lick of good if we don't go through how to use it. That's my bad, yo. Let's amend that now.

You can find hard-hit rate on the individual player pages on FanGraphs. Simply go to a player's page, scroll down to the "Batted-Ball" section, and look for "Hard%." Anything above 40.0% is phenomenal, above 35.0% is very good, and the league average for non-pitchers in 2015 was 29.2%.

Once you know the batter's hard-hit rate, you've got yourself a whole lot of power. Because it's not a results-based statistic, it will normalize to the batter's expected true hard-hit rate more quickly than stats such as batting average and slugging percentage. FanGraphs' breakdown of sample size stabilization notes that both ground-ball rate and fly-ball rate stabilize after 80 balls in play. Hard-hit rate isn't on their list, though one would assume it would be in the same range as those stats.

Utlizing hard-hit rate can allow us to draw conclusions from smaller sample sizes than we would using other stats. When we're dealing with platoon splits or trying to tell whether or not a batter has truly improved, this gives us a huge advantage.

Let's use Corey Seager's 2015 season as an example. After getting called up from the minors, Seager only had 113 plate appearances prior to the end of the season. That's not a huge sample, so it's hard for us to tell whether or not the stats he posted there were legit. However, for both season-long and daily leagues, this is critical information that we need to try to figure out if at all possible.

This is where hard-hit rate comes into play. There were 19 strikeouts mixed in, meaning he put 94 balls in play, enough to warrant a look to see what it says.

In that small sample, Seager's hard-hit rate was a grotesque 46.8%. Only one player with at least 300 at bats had a higher mark than that, and that was Giancarlo Stanton. That should tell you a thing or two about Seager.

Because the number was so large and bordering on an outlier, we wouldn't necessarily have expected Seager to duplicate that success over the entire 2016 season. What it does show, however, is that Seager was a great hitter worthy of a high draft pick in season-long leagues and a hefty salary in DFS. Tossing in his low 16.8% strikeout rate should only further the hype.

Having hard-hit rate in your back pocket allows you to basically limit the scope of investigation of platoon stats to three different areas which will normalize quickly: hard-hit rate, strikeout rate, and walk rate. If a hitter does well in all three of those areas, you can expect him to do well against pitchers of that handedness over the long run, even if their triple slash numbers are poor. Utilizing the triple slash categories over the small sample size could lead to false conclusions, and while the same thing could happen with hard-hit rate, the odds are lower.

As mentioned, you can use hard-hit rate to investigate whether or not a player's improvement (or downturn) is legitimate. On a player's FanGraphs page, there will be a tab titled "Game Log." If you click that, you'll be able to set a start date and end date to see how a player's stats compare before and after a significant change (such as an injury, demotion to the minors, etc).

This would have applied to Marcell Ozuna in 2015. He struggled with strikeouts to start the season, prompting a trip to Triple-A in July. He came back up and had a successful end of the season, rekindling optimism about his future. Should we put more stock into his numbers before the demotion or after?

In every single category, Ozuna's marks after his return to the majors were superior to what he did in the first half. His hard-hit rate was stellar upon his return, and with a near-average strikeout rate, you'd expect him to do some serious damage in 2016 with an increased sample and a clean slate.

Conclusions

Hard-hit rate is your best friend in the statistical world. Its name gets straight to the point, and the information it provides is difficult -- if not impossible -- to replace.

Hard-hit rate has a high correlation to all of the things we're looking for in MLB DFS, specifically slugging percentage and home-run rate. It also outpaces traditional stats such as batting average in correlating to fantasy points scored despite not being a results-based metric, which is an incredible endorsement of its utility.

Utilizing this can allow us to draw conclusions from smaller samples than we would be able to do with triple slash rates and other results-based numbers. If we can draw better information from splits stats and various stretches of time, then we should be able to capitalize on that information more quickly and gain an edge on the competition.

There's no such thing as a perfect stat that will always lead us to correct conclusions. However, hard-hit rate comes pretty darn close. Making this a key portion of your daily research will be a big step in developing sustainable MLB DFS decision-making skills that'll make those bankrolls a little bit healthier.