Cleveland Indians: A Strange Quirk About Carlos Santana’s Batting Statistics

Jul 8, 2016; Cleveland, OH, USA; Cleveland Indians designated hitter Carlos Santana (41) hits a home run during the first inning against the New York Yankees at Progressive Field. Mandatory Credit: Ken Blaze-USA TODAY Sports
Jul 8, 2016; Cleveland, OH, USA; Cleveland Indians designated hitter Carlos Santana (41) hits a home run during the first inning against the New York Yankees at Progressive Field. Mandatory Credit: Ken Blaze-USA TODAY Sports /

The Cleveland Indians’ Carlos Santana has a highly rare trait in his batting statistics

While researching for the update on Carlos Santana’s 2017 option, I stumbled upon a strange quirk in his batting statistics. This in itself comes as a rather small surprise since he has a rather quirky batting profile. He has a low batting average, but his low strikeout rate and high walk rate combine with some quality power to make him a highly valuable bat. Over his entire career with the Cleveland Indians, he has always been well better than the average run producer. 

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But this is not the strangest part of his batting statistics. His batting profile is unorthodox, but at least it makes statistical sense. The truly odd part, however, does not. Although it may defy logic, Carlos Santana has a higher batting average than batting average on balls in play.

At first, this seems like an error, as it seems counterintuitive that a player’s batting average on balls in play (BABIP) could possibly be lower than his normal batting average. After all, a ball must be put into play to record a hit, right? And it is not as if this is a mere fluke – 36 other players have experienced this phenomenon in the past five years.

In an effort to understand what caused this, let’s take a look at a “dummy player”. This player is not a real player, but rather an approximation of the most average player possible over 300 at-bats. The only reason why a player like this is valuable for us it that we can manipulate a certain part of his profile until we can create a player with a batting average above his BABIP. Why a league-average player, one might ask? Well, that is because the average makes a nice benchmark, and it makes the results more applicable than, say, finding a way to make Mike Trout fit the mold.

Credit: Ken Blaze-USA TODAY Sports
Credit: Ken Blaze-USA TODAY Sports /

Back to the research, our Mr. Normal has recorded 77 hits in 300 at-bats, good enough for a .257 batting average. As for the other components of BABIP calculations (which have a deeper explanation here), he has hit ten home runs, two sacrifice flies, and struck out 70 times. Again, this is the blandest, least interesting player in baseball whose only remarkable feature is that there is nothing remarkable about him.

Let’s try finding what makes a player have a lower BABIP than batting average by raising and lowering this player’s hits, home runs, strikeouts, and sacrifice flies. We will only change one variable at a time and leave the 300 at-bats constant to allow us to draw meaningful conclusions. For the more curious readers, the 300 at-bat total is not important to this part, it just makes things a little cleaner down the road.

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After much tinkering, I found the nearest whole values that make Mr. Normal’s batting average just greater than his BABIP, meaning that moving the value in one direction would make the difference negative and not unique. In order to put Mr. Normal in the same club as Carlos Santana, he would have to lower his hits from 77 to 38, increase his home runs from 10 to 24, lower his strikeouts from 70 to 30, increase his sacrifice flies from two to 42, or some combination of those adjustments.

It seems that the easiest way to join this exclusive group of players would be to become one of the league’s worst batters with a .127 batting average, or to reduce the amount of strikeouts by 57 percent. The hardest way would be to increase one’s home run total by 140 percent or sacrifice fly total by 2,000 percent. Certainly, the first and last options are not feasible. Any player who bats .127 will almost certainly lose his job before reaching the 300 at-bat threshold for this study, and the sacrifice fly increase is just not possible.

This means that to have a batting average higher than one’s BABIP, the player should focus on hitting more home runs while simultaneously striking out less. Also, worth noting is that it is easier to achieve this feat if one has a low BABIP, as that means that the player’s batting average can be lower than if he were to have a high BABIP. In fact, the players who have had a higher batting average than BABIP averaged a .260 BABIP for that season. A typical batter, on the other hand, usually has a .300 mark.

Applying this logic to real-world data from the past five seasons, all of which has been adjusted to 300 at-bats, gives confirmation for our findings. Of the 36 players on the list, they have an average of 18 home runs and 46 strikeouts, which is a 93 percent increase in home runs and a 27 percent decrease in strikeouts. Also of note, this group had a pretty typical batting average, which bodes well for our theory that a high batting average harms one’s chances of joining this group. Sacrifice flies are of incredibly low significance to these odds; but for whatever worth, this may have, the group of 36 average one more sacrifice fly than everyone else’s two.

Players with a higher batting average than BABIP tend to be better overall hitters.

Since the two biggest changes that got players onto this list, more home runs and less strikeouts, both changes are clearly beneficial attributes to have. Is it possible that these players are better than everyone else? Yes, in fact, these players have a collective weighted runs created plus (wRC+) of 130, while the total sample averaged just 105. This does not mean achieving this feat makes a batter better, rather a subgroup of better batters tends to have more home runs and fewer strikeouts than a typical player.

Finally, I would like to make a note of the biggest reason why this quirk exists: home runs are included in batting average but not BABIP. This is because a home run, or rather an over-the-fence home run, is technically not in play. Every home run a player hits increases his batting average while leaving his BABIP alone. The other feasible option of fewer strikeouts aids this cause because strikeouts are in the denominator of the BABIP equation. Therefore, having fewer strikeouts increases the denominator and lowers a player’s BABIP.

Next: An Update on Carlos Santana's 2017 Option

So there it is. Carlos Santana’s strangest quirk in his unorthodox batting statistics has put him in an elite club. Projection algorithms foresee this oddity correcting itself in the future, as he has been hitting more home runs and striking out less so far this season than in the past. His batting average still has an eight-point lead over his BABIP, and we are already 87 games into the season. Here’s to hoping for this trend to continue.