Two of the biggest questions of each MLB offseason are "Who’s going to breakout this upcoming year?" and "Who’s going to regress?" Those are also two of the more difficult questions to answer.
Today, we’ll use math and magic (but mostly math) to try to do exactly that. Now, this isn’t an exact science by any means. Mostly, it’s trying to find which players were "lucky" or "unlucky" during the 2013 season. This post contains the players that are due to rebound next year, and a post tomorrow will have the players who are knocking down regression’s door.
The equation used for this was batting average on balls-in-play (BABIP) divided by line-drive percentage. Usually, line-drive percentage is expressed as a percentage (21.2 percent, for example), but for scaling sake, I used it in the same format as BABIP (as in .212).
The reasoning behind this was pretty simple. You would think that the more line drives you hit, the higher your BABIP would be and vice versa. After compiling the BABIPs and line-drive percentages for each of the players in the league with 300-plus plate appearances, the correlation between the two sets of data was .455. This isn’t necessarily what is called a “strong positive” correlation, but it is significant enough to show that there is a relationship between the two.
Now, for those of you who have taken a basic stats class that are shouting at your computer, "Correlation isn't causation, bro," you are right. Gold star! However, the combination of logic (line drives lead to hits) and the .455 correlation should be enough to at least justify the exploration of this.
Let’s get to the list. If you want to see where various players ended up, you can click here to view the Google doc I used to compile the data. Sheet 1 is a list of each player by team. Sheet 2 (you can change between sheets by clicking which sheet you want in the lower left-hand corner) shows each of the players sorted by their luck with the "unlucky" players at the top and the "lucky" players at the bottom. Sheet 3 is the same data except by team, again with the "unlucky" teams at the top and the "lucky" teams at the bottom.
The average BABIP/LD percentage throughout the league was 1.423. If a player’s ratio was lower than that, they could be looking for an uptick in success this year. If it was higher...sad panda.
The equation for the “line of best fit” for the data is BABIP = (LD%)*.5534 + .1837. I’ll be using this below to show where a player’s BABIP would have been if they had an average ratio as opposed to what they ended up with. Without further ado, let's get at it.
1. Alberto Callaspo, Oakland Athletics
You had to know there would be at least one A’s player on this list. Callaspo finished last year with just a .266 BABIP despite a stout 24.6 line-drive percentage. With a line-drive percentage that high, Callaspo’s projected BABIP would have been just below .320, almost 54 points lower than his actual total.
Callaspo put 396 balls in play in 2013 between his time with the A’s and the Angels. If his BABIP were .320 instead of .266, he would have produced 20 additional hits, the difference between an average of .258 and .302. This would also give his OBP a bump from .333 to .369.
In Callaspo’s best offensive seasons (2009 and 2011), he had line-drive percentages of 17.2 and 22.4, respectively. Last year was better than both of those by a significant margin. This doesn’t mean that Callaspo is going to come out this year and reach his “projected” totals, but he should come closer to the mean and be a decent presence in the A’s lineup.
2. Paul Konerko, Chicago White Sox
And 90 percent of you just checked out. Sweet. I can’t even blame you. Dude could prove to Carl Everett that dinosaurs existed because he was there to witness it. But his age doesn’t dismiss the fact that Konerko got cheated in 2013.
Konerko’s numbers were very similar to Callaspo’s: .265 BABIP and 24.4 line-drive percentage. If he had an average BABIP/line-drive percentage ratio, he would have finished with a .287 average (up from his .244) and a .351 OBP (up from .313).
In 2012, Konerko finished with a .298/.371/.486 slash despite having a lower line-drive percentage (22.3) than he had in 2013. It’s uncertain how many at-bats Konerko will get now with Jose Abreu in the mix, but his numbers shouldn’t be as paltry as they were this year when he gets those chances.
3. Alex Avila, Detroit Tigers
Avila, when he was healthy, hit the snot out of the ball in 2013. That is, you know, when he actually hit the ball. Avila’s 29.6 strikeout percentage makes it hard to talk about his BABIP because that generally requires you to put the ball in play. Regardless, Avila still should see a bump in his numbers this year.
Avila’s .305 BABIP was actually above the league average of .302. But when your line-drive percentage is an ill 28.0 percent, brudduh better get paid. He didn’t. Instead, the baseball gods were stingy with Avila, giving him his .305 BABIP as opposed to his projected .339. This would have upped his average to .246 from .227 and his OBP to .333 from .317.
For Avila, the bigger problem has been the injuries. From concussions to forearms to knees, Avila saw it all last year. This could have contributed to his jump in strikeout percentage (up from 24.0 percent in 2012). If he can limit the strikeouts, Avila may be able to reap the benefits of his ball-clobbering ways.
4. James Loney, Tampa Bay Rays
Loney certainly didn’t have a bad year in 2013 with his above average .339 wOBA. It could have been a lot better based on how Loney hit the ball.
In his first year with the Rays, Loney had a .326 BABIP and a 29.6 line-drive percentage. With those totals, Loney’s projected rates would have been a .314 average and .363 OBP as opposed to his actual .299 and .348 totals.
The problem with this is that Loney’s line-drive percentage was the highest it had ever been in 2013. Prior to his 29.6 line-drive percentage last year, his career high was 24.7 in 2012. Can he sustain that torrid pace? Probably not. Loney led the majors in this category, beating out runners up Gregor Blanco and Joe Mauer by over two percentage points (27.7 percent).
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In This Article
2B/3B, Atlanta Braves
C, Detroit Tigers
1B, Tampa Bay Rays
LF, Miami Marlins
C, Minnesota Twins
2B/SS, Oakland Athletics
1B, Chicago White Sox
C, Boston Red Sox