There was an interesting poll question awhile back on ToutWars.com that asked, “Do you have a go-to statistic that you use to evaluate hitters?” Take a minute to read the responses submitted by some of the best minds in the fantasy sports industry. I’ll wait.

You will very likely note that every expert has their own way of evaluating hitters. Some like to stick with the tried and true traditional stats while others favor sabermetrics. However, one theme that is repeated throughout most of the answers is the use of skill metrics. The statistics that attempt to take luck and the team aspect of baseball out of the equation to allow us to compare players to each other.

As we begin the second half of the season, you probably need to tweak your team for one reason or another to prepare it for the stretch run. The trade deadline for MLB (and most fantasy leagues) also looms large on the horizon, so you need to be ready to make and receive trade proposals, which requires you to decisively choose players who will (or won’t) help your fantasy team. So, I am going to walk you through the process that I use to evaluate players and show you the statistics I count on most to give me a snapshot of a player’s production and perhaps predict how they may be trending.

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Beware of Small Samples

One of the mistakes many fantasy owners make with statistical evaluations is putting too much stock in stats generated from small sample sizes. The problem is that many baseball stats require nearly a full season of sample (plate appearances) to become mathematically meaningful. For instance, batting average and BABIP require upwards of 650 plate appearances to become statistically stable. The solution to this is to use other statistics to confirm or disprove the BABIP assessment of a player. In other words, never rely on any single statistic to make a player evaluation. Instead, look to other statistics, especially skill-related metrics, to confirm or deny the validity of any assessment.

For example, many fantasy owners use BABIP as the basis for their player evaluations. But the mistake many of them make is stopping there. They don’t dig deeper to confirm the validity of the numbers. For example, let’s say they are evaluating Player X who has a career BABIP of .305 with a career batting average of .240. A look at his current season numbers reveals a .219 BABIP with a corresponding .158 batting average. Our fictitious fantasy owner sees those numbers and concludes that Player X is due for some positive regression during the second half. He proposes a trade to Player X’s owner, sits back to wait for an answer and wrings his hands in anticipation of a big power surge.

BABIP Is A Good Start

Looking back at Player X’s numbers reveals that a similar evaluation could have been made roughly six weeks ago at the end of May when he was batting just .144 with a corresponding BABIP of .220. Obviously, not much has changed during the six weeks since the end of May. His isolated power (ISO) was .089 in May and there has been just mild improvement to .157 in July. If the fantasy owner acquired Player X in May in anticipation of a forthcoming power surge, he is gravely disappointed now. To find out what happened and make a better assessment for the rest of this season, we need to look closely at the context his skill metrics can provide. By the way, Player X is Chris Davis of the Orioles.

It’s All About the Skills

I usually begin a player evaluation with a look at his hitting skill metrics, meaning strikeout rate (K%), walk rate (BB%), isolated power (ISO), contact rate, and home run rate (HR/FB) among others. Those statistics have one major thing in common. They all become stable at a relatively low number of plate appearances. Because of that stability, those statistics also correlate well from year to year.

Davis is striking out just shy of 36% of the time, a full 20% higher than during his peak production seasons 2012-15, and his walk rate (8%) is well below those same seasons. We’ve already seen that his ISO is off the mark, but you may not realize just how off it is. Again, during his peak seasons, he had an ISO low of .242 with a stunning .348 high. That makes his current .116 a tragedy. His contact rate (68.8%) is virtually the same now as it has always been (67.6% career mark), so that’s not at the root of Davis’ problem.

Defining the Problem

It’s Davis’ HR/FB rate where we find the largest disparity. His career average is 23.4% but his current rate is just 12.9%. In addition, he’s hitting more groundballs (42%) than ever, which means fewer flyballs. But it’s the quality of the flyballs that really hurt his production. He’s not hitting the ball as hard now (33.7 Hard%) as he was during most of his career (37.2%) and his softer swings (62.8% Medium plus Soft%) make up a larger portion of his total batted ball profile.

The bottom line here is that Davis is striking out more, not hitting the ball as hard, walking less and not getting extra base hits as often. More importantly, there is nothing in his batted ball profile to indicate any sort of positive change anywhere on the horizon. If anything, his current numbers are a continuation of negative trends that Davis has suffered for the last three seasons. All this makes Davis a player to avoid for the second half of the season.

Go Get ‘Em

Now you’re ready to hit the trade market armed with the knowledge of how to assess hitters. Begin with BABIP and compare a player’s career mark with their current season. Players with a large difference between the two might be in for some regression. However, you need to dig deeper to confirm or disprove what the BABIP tells you. Continue with the skill metrics that tell you how well he’s using the strike zone (K%, BB%) and give you an idea of what he’s doing with the pitches he sees (contact rate, HR/FB, ISO).

If you need confirmation of contact issues or strike zone problems, you can hone in on the problem with a look at the plate discipline metrics. You can determine whether a hitter is swinging at pitches outside the zone too often (O-Swing%), just swinging and missing more than he should (SwStk%) or taking too many first-pitch strikes (F-Strike%). Knowing why a hitter is or isn’t hitting well makes it much easier to figure out whether that player can or cannot improve his performance.

From there, go on to look closely at the player’s batted ball profile (GB%, LD%, FB%) to get an idea of how he’s hitting the ball. Then get an idea of the quality of his contact (Hard%, Med%, Soft%) and whether he’s utilizing the whole field or pulling the ball (Pull%, Cent%, Oppo%). And there is even more to come in this regard. We’re already beginning to use new statistics from Statcast to evaluate players. In time, those stats will be used to provide context for the metrics touched on here. For now, just remember that no single stat can fully sum up what a player is giving his fantasy owners.

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