This article is part of our The Z Files series.
This just in… it's not too early! In a field where many like to arbitrarily set a sample size to fit their narrative, it's comforting to rely on some research to make some tangible observations just over five weeks into the season. Specifically, today's topic will be hitter contact rates with a look at five players to acquire and five to worry about.
The studies cited investigate how many plate appearances are necessary within a season for various skills to be considered real. Because the least time is needed for contact rate, we'll start there. There are actually a few different looks at the issue, published at sites including The Hardball Times, Fangraphs and Baseball Prospectus.
The actual studies won't be included to avoid the temptation to get caught up in the numbers, clouding the big picture. Admittedly, the studies can be used in concert with regression analysis to adjust rest-of-season projections which can be useful for both traditional and DFS formats. However, the theme will be identifying players the market perceives differently. The numbers are secondary to the big picture.
The basis for the ensuing analysis is after 100 plate appearances, a hitter's contact rate has a 50/50 chance of being predictive. If it's better than expected it's a coin flip whether going forward it remains better than expected, and vice versa. In other words, the over/under line is changed. Conventional analysis assumes going forward, performance will be what was originally expected, at least in terms of skills. After
This just in… it's not too early! In a field where many like to arbitrarily set a sample size to fit their narrative, it's comforting to rely on some research to make some tangible observations just over five weeks into the season. Specifically, today's topic will be hitter contact rates with a look at five players to acquire and five to worry about.
The studies cited investigate how many plate appearances are necessary within a season for various skills to be considered real. Because the least time is needed for contact rate, we'll start there. There are actually a few different looks at the issue, published at sites including The Hardball Times, Fangraphs and Baseball Prospectus.
The actual studies won't be included to avoid the temptation to get caught up in the numbers, clouding the big picture. Admittedly, the studies can be used in concert with regression analysis to adjust rest-of-season projections which can be useful for both traditional and DFS formats. However, the theme will be identifying players the market perceives differently. The numbers are secondary to the big picture.
The basis for the ensuing analysis is after 100 plate appearances, a hitter's contact rate has a 50/50 chance of being predictive. If it's better than expected it's a coin flip whether going forward it remains better than expected, and vice versa. In other words, the over/under line is changed. Conventional analysis assumes going forward, performance will be what was originally expected, at least in terms of skills. After 100 plate appearances, it's safe to alter the expected contact rate baseline.
Here's an example to illustrate the concept. Starting the season, a hitter's projected contact was 80 percent. After 100 plate appearances it's 88 percent. Some may chalk that up to sample size and assume the player's contact rate will revert back to 80 percent. Based on the research, you can anticipate the player's contact rate will continue at a clip better than 80 percent. It may not stay at 88, but you can set the new over/under line above 80 as opposed to 80 as it was coming into the season. A higher contact rate not only impacts batting average but it also increases run production and avails more chances to steal.
On the flip side, if this same batter checks in with a 73 percent contact rate after 100 plate appearances, his over/under is now below 80 percent. Accordingly, not only will his average continue to be lower than expected but all his counting stats will suffer.
The criteria used to highlight players was comparing the 2016 contact rate of hitters with at least 100 plate appearances to their mark spanning the past three seasons. In order to be included, the hitter needed a minimum of 750 plate appearances from 2013 through 2015.
Five Players To Acquire
John Jaso, 1B, Pittsburgh Pirates (3-year 78.5 percent, 2016 90.0, difference +11.5): Wearing a first baseman's mitt along with moving to the Senior Circuit has agreed with Jaso. Already a solid on-base contributor, Jaso has added another dimension to his game. Interestingly, the vast majority of Jaso's improvement has come on contact outside of the zone. Not just that, he's chasing significantly fewer pitches. It's as if he's only swinging at pitches outside the zone he thinks he can handle. Over the past three seasons, Jaso has swung at 23 percent of the pitches he's seen that are outside of the strike zone (O-Swing%), making contact on about 56 percent (O-Contact%). So far this year he's swinging at only 16 percent, making contact with 78 percent. It's not just his O-Swing% and O-Contact% that have improved. Jaso is also making six percent better contact on pitches within the zone (Z-Contact%). The only negative aspect of making more contact is more at-bats are over before the patient Jaso draws a walk, so his OBP isn't coming along for the ride. With Jung Ho Kang back, Jaso appears to be falling into a platoon at first which may be enough to pry him from his current owner if they are looking to sell high. So long as you make the offer with less playing time in mind, Jaso can still be a useful piece in deeper formats.
Lucas Duda, 1B, New York Mets (3-year 72.9 percent, 2016 81.3, difference +8.4): Don't blame Duda's lower than normal 0.236 batting average on less contact, as he's been snake bit with a 0.228 batting average on balls in play (BABIP). It's become routine analysis to expect Duda to regress back towards his normal 0.275 BABIP, although it should be noted his line drive rate and hard hit percent are both lagging behind the slugger's norm, so the BABIP drop can't be entirely attributed to bad luck. On the other hand, maybe those rates have his owner worried, lowering the cost of acquisition. Again, it becomes a difference of landing points. Both parties factor in their expected BABIP regression but you additionally account for a continued improvement in contact rate, which for Duda could result in an extra homer or two.
Joe Mauer, 1B, Minnesota Twins (3-year 82.7 percent, 2016 89.5, difference +6.8): Mauer is one of the better examples to use to illustrate this principle based on the numbers, but isn't the most compelling trade target since he lacks the standard thump from a corner infielder. That could be your way in, though, since the party owning Mauer may be looking to cash out the higher than expected average for some counting stats, so it comes down to needs. Like Jaso, Mauer is chasing less but unlike Jaso, Mauer's O-Contact% is in sync with his career mark. Still, this contributes to an overall improved contact rate.
DJ LeMahieu, 2B, Colorado Rockies (3-year 84.3 percent, 2016 91.1, difference +6.8): Hang in there friends, the next name is intriguing and besides, the players to avoid list is always more compelling using this process. A cursory look at LeMahieu's early numbers and he's doing exactly what most expected he'd do. The slight difference is last season his 0.300 average was buoyed by a high BABIP, this campaign it's aided by a spike in an already solid contact rate. If your team could use a boost in steals, check out LeMahieu. There's a good chance he can be pried away for a reasonable price.
Brandon Belt, 1B, San Francisco Giants (3-year 73.9 percent, 2016 80.5, difference +6.6): Okay, now we're talking. This concept works best when you land on a player who's a typical sell-high candidate. With a 0.319 average and 0.448 OBP, Belt will head many a sell-high list. But here's the problem. His elevated average isn't due to a bloated BABIP, as he's displayed the skill to maintain a mark well above league average. This isn't to say Belt will hit 0.320 the rest of the way, but his rest of season average should exceed the 0.280 or so expected back in March. The fact he plays half his games in AT&T Park actually fuels the impulse to cash out since it's known as a pitcher's park. But here's the thing. The park factor should be looked at in a vacuum. A good offense will score runs regardless of venue while a bad offense will struggle to score irrespective of locale. The Giants attack is reminiscent of last year's Kansas City Royals offense, featuring lots of gap-to-gap contact which keeps the line moving. This style of play is very suited to a spacious venue like AT&T Park. Belt won't challenge Barry Bonds for the franchise record in homers per season but his run production should remain elevated with the Giants' lineup peskiness from top to bottom.
Five Players To Avoid
David Wright, 3B, New York Mets (3-year 80.2 percent, 2016 62.9, difference -17.3): Let's get right to the point. You don't need me to point to a huge drop in contact to convince you to avoid Wright. As expected, he's getting a rest every few games and not doing much with the playing time he receives. Next.
Alex Gordon, OF, Kansas City Royals (3-year 77.1 percent, 2016 66.5, difference -10.6): As a team, the Royals are making significantly less contact than last year with Gordon being the primary culprit. The left-fielder's O-Contact% is down 17 percent with an eight percent drop in Z-Contact%. It's actually the Z-Contact that's more concerning since a hitter is more likely to be productive on contact within the zone than outside of it. Gordon's BABIP is at its typically elevated level or his 0.234 average would be even more unsightly. A sage owner should be looking to divest shares of Gordon to an owner that expects his average to revert back to the 0.265-0.270 level. It's likely to head in that direction, that's not the issue. Chances are the rest-of-season number will still be below that target, with the associated drop in production amplified by a team not likely to be as productive as last season.
Carlos Gomez, OF, Houston Astros (3-year 72.8 percent, 2016 62.8, difference -10.0): Houston (and yours truly) has a problem. This analysis has been very successful highlighting players to avoid but the gut says ignore it and make Gomez the exception. That said, Gomez has been so terrible that the process can still be applied with a resulting landing point still better than that of someone else. Gomez has always had terrible pitch recognition, and this season is no different. His O-Swing% is higher than average, as usual. His O-Contact% is a putrid 49 percent so not only does he swing at a lot of balls but he's making contact on less than half of them. That's giving away a ton of at-bats. His Z-Contact% is down a more reasonable but still significant eight percent. It's all about comparing expectations. You can still factor in a poorer than usual contact rate for the rest of the season and still be more optimistic than someone looking to rid themselves of all things Carlos Gomez.
Corey Dickerson, OF, Tampa Bay Rays (3-year 75.8 percent, 2016 66.4, difference -9.4): Here's a guy you have a fighting chance to pawn off to an unsuspecting owner. Point out Dickerson's low 0.175 BABIP in tandem with a healthy seven homers and you might convince someone that once the BABIP regresses, Dickerson will be fine. All that is true, save for the fact he's likely to continue to whiff at an elevated rate so even when (if?) the BABIP corrects, the full impact won't be felt since more strikeouts mean a lower average and dip in run production.
Salvador Perez, C, Kansas City Royals (3-year 83.8 percent, 2016 75.4, difference -8.4): It's not surprising another Royals hitter is on this list, though it is curious the subject is Perez as his contact rate has been so consistently good over the years and is supported by a similar minor league history. Dealing a backstop in two-receiver leagues is tough since the replacement level to backfill is so poor. The exception is receivers originally drafted as reserves who have emerged as useful assets. Keeping in mind the following examples need to be considered in context with league format, Jarrod Saltalamacchia, Geovany Soto, Trevor Brown, Chris Herrmann and Tucker Barnhart are all playing more than expected and at a potentially helpful level. If the Astros follow through with their intention to use Evan Gattis behind the plate, on the off-chance you own both Gattis and Perez, dealing the latter makes a ton of sense.
In summary, I'll be honest, while the names that are flagged by this analysis are nowhere near as compelling as recent campaigns, the thought process is sound. At the very least, add comparing current strikeout rate against a player's history to your checklist when evaluating a potential move. If nothing else, it can serve as the tiebreaker for a close call.
