M-Sabermetric All Stars, Vol. I: The BABIP All-Star Team

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by Cam Cain, Matthew Kikkert, Connor Stemme, Max Brill, Jack Gioffre, and Max Smith

Though it no longer arbitrarily decides home field advantage for the World Series, it’s hard not to love the All Star Game. From newcomers off to a hot start, to future Cooperstown residents making their last go around, everybody gets their time to shine during the Midsummer Classic.

At M-SABR we figured we would take our own crack at the All Star Game, and with that we welcome you to Volume I of the M-Sabermetric All Stars!

While MLB’s All Star Game usually selects its participants based on overall performance, name recognition and extremely biased fan bases (looking at you, Kansas City and Chicago), our approach is a little different. All of our selections are based off a single advanced statistic of our choosing.

Over this series of articles, our main goal is further our readers’ understanding of various Sabermetric stats by assembling these prestigious hypothetical All Star teams and analyzing in depth why the given honorees excelled in the statistic at hand. Finally we will use the player’s WAR to estimate how our team might fare over the course of a season when paired with a league average pitching staff. Over time this will allow us to keep track of which stats seem to have a stronger correlation with both overall player and team performance.

The first statistic we are tackling is BABIP. The official Fangraphs definition and formula are as follows:

babip

A simplified version of that definition is that while BABIP can be a true skill–players that are fast or hit the ball hard will generally turn more of their balls in play into hits–it is often an indicator of luck, or lack thereof. If a perennial superstar is underperforming, a quick glance at his BABIP might reveal that he is really just getting unlucky and hitting the ball right at infielders; while a relative no-name enjoying a career year might be benefitting from an unusually high .380 BABIP and tons of bloopers or soft ground balls sneaking through the infield.

That being said, let’s take a look at the 2017 positional leaders and see what helped each of them secure their spot on our roster.

Without further ado, our BABIP All Stars:

C – Alex Avila, Detroit Tigers/Chicago Cubs, .264/.387/.447, 3.6 fWAR/162

Surprisingly, the 2017 BABIP leader at catcher was not a household name such as Salvador Perez or Yadier Molina, but two-time ex-Tiger, Alex Avila. Over the season, Avila put together a BABIP of .382 compared to .285 for Molina and .280 for Perez. On the other hand, Molina (.273) and Perez (.268) both put up better batting averages than Avila (.264). So what led to the extreme BABIP advantages for Avila?

There are three main statistical discrepancies between Avila and the others that explain Avila’s success. First, Avila has a way higher strikeout percentage than our two All-Star catchers. Avila’s strikeout rate of 31.9% is 134% higher than Molina’s and 68% higher than that of Perez. This implies much of Avila’s low batting average is contributed to his inability to put the ball in play, an attribute that does not hurt him though when it comes to BABIP. However, when Avila does put the ball in play, his Hard Hit % and Soft Hit % set him apart. Molina and Perez both make weak contact with the baseball 16.5% of balls in play, while Avila only makes weak contact 6.7% of the time. This number is much lower than the 2nd best soft hit percentage among catchers: Wellington Castillo’s 12.2%. Moreover, Avila’s hard hit percentage of 48.7% is nearly 10 points higher than the next best rate. While Perez (38.1%) and Molina (36.4%) were both above the league average of 33.62% among catchers with at least 200 ABs, they still fall well behind the hard-hitting Alex Avila–who also led all catchers in Average Exit Velocity at 90.7 mph.

Combining these three statistics gives us the best indicator of why Avila recorded such a high BABIP during the 2017 season, and why Poppa Avila decided to trade his own son, selling high on a potentially unsustainable streak of good luck and high BABIP.

1B – Trey Mancini, Baltimore Orioles, .293/.338/.448, 2.1 fWAR/162

Trey Mancini led all major league first basemen with a .352 BABIP. Because he is a rookie, it is very difficult to make a meaningful conclusion about Mancini due to this statistic alone. A BABIP this high is always somewhat due to luck, but to what degree? It’s quite possible that Mancini’s .291 batting average and 117 wRC+ reflect his true talent level. It’s also possible that it was a fluke, or perhaps teams aren’t sure how to shift against him, since he only had 15 MLB plate appearances prior to 2017.

Looking deeper into Mancini’s stats, it’s likely that this was the case. His soft contact percentage of 19.6% was 5th highest among qualified first baseman. Similarly, his hard hit percentage and line drive rate are in the lower half of this group. So why are these stats important? While a high BABIP can be due to luck, some players’ BABIPs are naturally high due to other factors in their game. Players who consistently make solid contact will naturally have their balls in play fall in as hits more often. Mancini, at least this season, was not one of them. His high BABIP is most likely due to luck, and I would expect his numbers to regress in 2018.

2B – Jose Altuve, Houston Astros, .346/.410/.547, 7.9 fWAR/162

9413529186_47e3272e95_bAltuve—unsurprisingly—was third amongst qualified hitters in all of baseball in BABIP in 2017, checking in at an impressive .370. He also led all Second Basemen in BABIP by a very wide margin: the difference between Altuve’s BABIP and second-place Dee Gordon’s is greater than the difference between Gordon’s and Jonathan Schoop’s, who was seventh among Second Basemen. So how did Altuve end up with such an impressive BABIP? It wasn’t his Barrels/PA, in which he did not even crack the top 100 in baseball. It wasn’t an impressively low Soft Contact %, as his 19.0% placed him in the bottom half of the league, nor was it an exceptional rate of hard hit balls as his Hard Contact % was in the bottom 20% in the MLB. So what exactly made Altuve so good this season?

In short, Altuve puts the ball in play a lot. When you put the ball in play a lot, if you’re as fast as Altuve–the only player in MLB with 30+ Stolen Bases in the past 6 seasons straight–you’re going to get on base a lot. He isn’t particularly high on the leaderboards in line drive percentage (line drives go for hits most often), but he has the 7th-lowest fly ball percentage of qualified second baseman. Fly balls go for hits the lowest percentage of the time, lower than grounders and line drives, so if Altuve is putting the ball in play nearly 80% of the time, and most of those are grounders or line drives, that bodes well for him. If you put the ball in play a lot, and you’re fast, as Altuve does and is, a high BABIP is sure to follow. His all around excellence at the plate might even end up winning him the AL MVP–an honor second only to earning a place on this team.

SS – Tim Beckham, Tampa Bay Rays/Baltimore Orioles, .278/.328/.454, 4.1 fWAR/162

Beckham was the #1 pick in the 2008 amateur draft, but he was labeled a bust before he even reached the majors. After a 2010 season where he posted an of OPS .704 in High-A, Beckham was dropped off of Baseball America’s prospect list completely. He finally broke through to the majors in 2013, but he was mostly a role player until this season. In 2017 Beckham broke out and posted a 3.5 WAR and 22 home runs, over double the total he hit at any level in any previous season. His .365 BABIP was the highest among major league shortstops, almost certainly due to some extent of luck.

His BABIP was also exceptionally high in 2016, a year where he was below average at the plate, creating conflicting results from the past 2 seasons. I think Tim Beckham’s performance is sustainable. He hit only 15.5% of balls for soft contact this year, down from 20.3% in 2016. He also hit 39.1% of his balls for hard contact, 4th-highest among qualified shortstops and up from a rate of 36.1% from the year before. Additionally, he cut down on infield fly balls, the least likely type contact to fall in for a hit, by over 300%. Now that he is finally making solid contact, Beckham is finally starting to show why he was loved by scouts 9 years ago.

3B – Miguel Sano, Minnesota Twins, .264/.352/.507, 3.3 fWAR/162

Miguel Sano’s 2017 season, albeit shortened by injury, was a highly productive one. He hit 28 HR, drove in 78 runs, all while accruing an OPS of .859, and an fWAR of 2.3–a number largely limited by his lackluster defensive ability and baserunning. What he lacked in defense at the hot corner though, he made up with his uncanny ability to turn batted balls into hits, accruing a .375 BABIP that outpaced second place Cory Spangenberg by .33 points. Let’s look at some underlying factors that allowed Sano to cement himself as the third baseman and one of the stars of our illustrious All Star team:

34b6c6518a72b4d8985e0ee98ed52a87This year Sano was ranked 43 of our qualified hitters in ISO, denoting his ability to hit for extra bases and power. His ISO of .243 was ahead of sluggers such as Kris Bryant, George Springer, and Anthony Rizzo. A high ISO typically denotes a player who hits the ball harder than average, which leads us to Sano’s remarkably high Hard Hit Percentage, or Hard%. Sano ranked 6th out of 287 qualified hitters with a Hard% of 44.8% behind only Aaron Judge, Joey Gallo, Alex Avila, JD Martinez, and Jesus Aguilar. This means that almost half of the balls Sano hit in play were classified as hard hit, giving him lots of chances for hard ground balls and line drives to fall in for hits or get through holes.

Sano also bolsters an extremely effective approach at the plate. Although he is renowned for lots of strikeouts, those actually do not hurt his BABIP performance–each strikeout is one fewer time he gets out on a ball in play. However, while he is a bit of a free swinger, Sano’s 75% Inside the Zone Swing Percentage (Z-Swing%) was third amongst third basemen, which indicates that even if he swings and misses, he is swinging at good pitches. Swinging at pitches in the strike zone is distinctly related to a high chance of good contact, and thus more hits. Given his career BABIP of .362, and the natural translation of his overall game as a hard-hitting, high strikeout slugger to a high BABIP, Miguel Sano should be a staple of this team for years to come.

LF – Howie Kendrick, Philadelphia Phillies/Washington Nationals, .315/.368/.475, 2.8 fWAR/162

Not only did Howie Kendrick prove himself to be an extremely shrewd trade deadline pickup for the Nationals—as he helped the team overcome various major outfield injuries—but he also hit his way to the starting Left Field spot of the MSABR BABIP All-Stars, checking in at .378 in 91 games. Kendrick boasts a career .340 BABIP, so while 2017 was an especially good year, turning balls in play into hits has always been a strength of his. While he has always possessed the speed we see in many of our BABIP All Stars (12 SB and 9 in-field hits and 2 bunt singles on 2 attempts in 2017), the best indicators for his excellent season are his contact stats. Kendrick only made Soft Contact on 17.2% of the balls he put in play, and that coupled with a 21.9 Line Drive % and a miniscule 20.3 Fly Ball % that was second lowest at his position, left Howie reaching base on balls in play more than any other left fielder in the league.

CF – Austin Jackson, Cleveland Indians, .318/.387/.482, 3.4 fWAR/162

Austin JacksonMuch like the majority of players on our team, the 2017 BABIP All-Star centerfielder was not a perennial All-Star, but a player who often flies under the radar in Austin Jackson. However, Jackson not only earned his way onto our BABIP All-Star team, but also had a great overall season, checking in with a career high .318 batting average, good for second in the majors among centerfielders, trailing only Charlie Blackmon (.331). To analyze how Jackson was able to have a higher BABIP (.385), than Blackmon (.371), despite a lower batting average, there are various underlying statistics we can turn to.

First, Jackson hit far fewer infield fly balls than Blackmon, his 2.9% rate being much lower than Blackmon’s 6.9%. As we said earlier, infield fly balls fall for hits less than any other type of contact. However, the main difference between the two came in soft hit percentage. Jackson held the lowest among centerfielders at just 11.4%, while Blackmon’s was at 17.1%. This means Jackson more consistently put the ball in play with medium or hard hit balls, as he was above average in each of these categories. Along with possibly a little luck, another ex-Tiger in Jackson was able to find his way onto our BABIP squad.

RF – Avisail Garcia, Chicago White Sox, .330/.380/.506, 5.0 fWAR/162

Avisail Garcia led not only his position in BABIP in 2017, but the entire Major Leagues, posting an incredible .392. He is not a fast player, as he only had 5 stolen bases last season, but makes consistently solid contact, evidenced by his .330 batting average and 90.1 Average Exit Velocity last year. Garcia’s BABIP is so high mainly due to his ground ball percentage. 52.2 percent of the time Garcia makes contact, the result is a ground ball. This works to Garcia’s advantage as it makes the fielders work for the out, which resulted in quite a few extra infield hits–his 26 IFH were third in the Majors. Garcia is also second in the league in swing percentage with a 59%. This means that he swung at about 60 percent of pitches thrown his way, giving him a larger potential of putting balls in play, compared to leaving the bat on his shoulder, leading to more opportunities of getting on base.

In addition to a high swing rate, when Garcia makes contact it is consistently very solid. Garcia posted the 6th lowest soft contact percent, at 15.7%, and the 9th highest hard hit percentage, at 35.3%. Overall, Garcia’s BABIP of .392 is historically high this year, compared to his career average of .342. While there is obviously a lot of luck involved, this high BABIP evidently is also due to his great contact percentages.

DH – Nick Williams, Philadelphia Phillies, .288/.338/.473, 1.6 fWAR/162

Although Nick Williams did not lead his position, he does qualify for our designated hitter award, as he posted the highest BABIP among non-positional leaders with a .375, good for 6th in the league. Since Nick Williams is not particularly strong in the speed or power departments, his strong BABIP is mainly attributable to his swing percentage. Nick Williams led the entire league with players over 300 PA in swing percentage. He swung 59.1 percent of the time, giving himself the opportunity to put the ball in play and reach base a larger amount of the time. Another crucial factor is his groundball percentage, as half of his balls hit were ground balls. Being a rookie, Nick Williams does not have a lot of history, however, his high BABIP may be sustainable. Just 15.5% of his contact was soft, 50.9% medium, and 33.6% hard, and he was among position leaders in every category.

Williams also owns an exceptionally low Infield Fly Ball Percentage, decreasing his chances of a near certain out. This rate means that practically all of his fly balls make it beyond the boundary of the infield and thus far more likely to fall for hits. In the end, Nick Williams has a great BABIP due to his swing percentage and contact rates, giving the Phillies a much needed ray of hope in their time of despair.

Putting the Team on the Field

Now that we’ve introduced the team, we know curious readers must be wondering how this team might fare over the course of a season, and even more generally speaking how BABIP might or might not translate to being an important factor in team performance.

To calculate a record for our BABIP All-Star team, we started by calculating a 162-game WAR for each player, assuming they will be in the starting lineup every day. This was found through the player’s fWAR, which we used to extrapolate to a 162-game total. The next step was to find the average WAR of an MLB pitching staff over the 2017 season, which was found to be a solid 14.9 WAR, comparable to that of the San Francisco Giants. Combining our starting BABIP lineup and the average pitching staff, our total team WAR comes out to be 48.7. Using Fangraphs WAR totals, they recommend adding 48 wins to calculated WAR to predict a 162-game average record. In the end, our projected amount of wins comes to 96.7, rounded to an expected record of 97-65.

We forecast this team to contend for a World Series on a yearly basis. Stay tuned for future articles to see how our BABIP All-Star team fared in an OOTP season simulation and if they actually were able to bring home the Commissioner’s Trophy. Also keep your eyes open for the future editions of M-Sabermetric All Stars, as we deep dive into other advanced stats and examine how those teams might perform.

Feel free to leave any suggested Stats in the comments below!

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