Recently, while perusing **Mike Trout**‘s FanGraphs page, I noticed for the first time that he strikes out at about a league-average clip. Indeed, Trout maintained 20% and 20.4% strikeout rates over the last two seasons. Think about that for a moment. The best hitter of this generation strikes out as much as the average hitter. Scrolling down to his plate discipline metrics, I discovered that Trout actually took a swing on only 36.8% of the pitches he faced last season, good for fourth-lowest among qualified hitters. He actually does that every year.

That got me thinking. Does swinging less result in more strikeouts? What about players who swing more? And which group has higher batting averages? How do certain hitters with poor plate discipline still have great batting averages, while others with excellent plate discipline have poor batting averages? This article is the first of two parts designed to answer these and other questions.

**Low Swing%**

Let’s begin with Troutian hitters. No, none of them are as good as Trout, but a little hyperbole never hurt anyone and they *do* swing as infrequently as he does. For a sufficient sample, I collected 60 players with the lowest single-season swing rates dating back through 2017. If you’re curious, **Matt Carpenter** in 2017 and **Daniel Vogelbach** in 2019 had the lowest single-season swing rates — tied for 34.1%, a full percentage point below the next lowest mark — of all hitters during this three-year period.

Then, I found the averages for this group of various plate discipline and other metrics that relate to batting average. For context, the first row of the following table contains the averages of the 60 players’ with the lowest single-season swing rates, while the second row contains the averages for qualified hitters from 2017-19.

Swing% | O-Swing% | Z-Swing% | O-Contact% | Z-Contact% | Contact% | SwStr% | K% | BB% | BABIP | AVG | |
---|---|---|---|---|---|---|---|---|---|---|---|

Sample Average | 39.1% | 22.5% | 60.5% | 67.0% | 87.8% | 81.0% | 7.5% | 19.5% | 13.4% | 0.301 | 0.267 |

2017-19 MLB Average | 46.9% | 30.9% | 68.6% | 65.2% | 86.6% | 78.5% | 10.1% | 19.8% | 9.1% | 0.305 | 0.269 |

Some preliminary observations are in order. First, these players obviously swing less-frequently both on pitches inside and outside of the zone than the MLB average hitter. Second, their patience does not necessarily enable them to fare better in terms of batting average or BABIP than the average qualified MLB hitter. Third, their walk rates are far higher than average MLB hitters (13.4% vs 9.1%).

Fourth, and perhaps most importantly, these 60 hitters maintain substantially lower swinging-strike rates than the MLB average. Yet, they strikeout at basically the same rate. I ran a quick regression in the entire sample of qualified hitters to find a formula for predicted strikeout rate based on swinging-strike rate. Plugging in the 7.5% mark above yields a 16.2 K%, yet these 60 guys average 19.5%.

My theory is that, by swinging less often, these hitters put fewer balls in play. That also means that they take more pitches — balls and called strikes — and see deeper counts. By taking more called strikes and balls than other hitters, they strike out more and walk less than other hitters. That also means they strike out more than we would otherwise expect based solely on their swinging-strike rates. Accordingly, they must maintain lower swinging-strike rates than other hitters, which they generally do, to have similar strikeout rates.

So, when you evaluate a hitter and his swinging strike rate is far lower than what you’d expect based on his strikeout rate, check to see if he’s a passive hitter. If so, I wouldn’t expect a lower strikeout rate in the near future.

**Finding Success With Patience**

Back to discerning between the successful and unsuccessful patient hitters.

Metric | Patient Hitters R2 to AVG | 2017-19 MLB R2 to AVG |
---|---|---|

Swing% | 0.004 | 0.010 |

O-Swing% | 0.031 | 0.007 |

Z-Swing% | 0.001 | 0006 |

O-Contact% | 0.136 | 0.119 |

Z-Contact% | 0.299 | 0.129 |

Contact% | 0.258 | 0.132 |

SwStr% | 0.229 | 0.072 |

K% | 0.279 | 0.203 |

BABIP | 0.613 | 0.588 |

Here’s the really fun part. In a vacuum, this table is rather unremarkable. In the second column, it shows the amount of variation in these 60 patient hitters batting averages explained by each metric in the first column. For example, these 60 players’ BABIPs explained 61.3% of the variation in their batting averages. The third column does the same, but for the entire sample of qualified hitters in MLB from 2017-19.

The first thing that jumps out is that, once we control for patient hitters, it doesn’t matter whether they’re more patient than one another. Put differently, there is no correlation between being the *most *patient of patient hitters and batting average. There is similarly no correlation between being the most patient in terms of swings in the zone or chases and batting average.

Next, I’d note that BABIP is extremely important for these (and all) hitters. In terms of batting average, successfully patient hitters generally run higher BABIPs than others.

Beyond the obvious — like BABIP influencing batting average — there are interesting results vis-à-vis swinging-strike and Z-Contact rates. Because we’re controlling for patient hitters, they will all take far more called strikes than other hitters. Therefore, their ability to control swinging strikes is crucial to their batting average success relative to one another. That is borne out in the table. For patient hitters, swinging-strike rate explained 22.9% of the variance in their batting averages, whereas it explained only 7.2% for the entire sample of MLB hitters.

What’s more, these hitters’ Z-Contact% (contact made on pitches in the zone/swings at pitches in the zone) actually explained more variance in their batting averages than their strikeouts rates. In other words, when just looking at hitters with low swing rates, their batting averages relative to one another are more likely explained by their ability to make contact on swings in the zone than their strikeout rates. This is probably because these hitters swing so infrequently and take so many called strikes that they need to maximize the swings they do actually take on good pitches to hit. This also feeds back into how controlling swinging-strike rate is important for these hitters. Indeed, Z-Contact% is simply the inverse of Z-Whiff%, if there were such a metric.

Take **Rhys Hoskins**, for example. He’s a notoriously patient hitter whose 2017 (had he qualified), 2018, and 2019 seasons all showed up in the 60 lowest single-season swing rates.

Year | Swing% | Z-Contact% | SwStr% | K% | BABIP | AVG |
---|---|---|---|---|---|---|

2017 | 38.4 | 88.8 | 7.1 | 21.7 | 0.241 | 0.259 |

2018 | 39.1 | 87.4 | 7.9 | 22.7 | 0.272 | 0.246 |

2019 | 39.2 | 84.0 | 8.5 | 24.5 | 0.267 | 0.226 |

You probably thought Hoskins’s batting average would improve after his rookie campaign in which he hit .259 with an extremely low .241 BABIP. Yet, it oddly *decreased* in 2018 and stayed down in 2019 even though his BABIP substantially improved after 2017.

Here’s why. As a patient hitter, he’s taking a substantial number of called strikes at the outset. That’s why his strikeout rates are much higher than expected based on his swinging strike rates. From there, he *must* make contact in the zone in order to compensate for the called strikes and keep his strikeout rate down. However, both his Z-Contact% and SwStr% decreased year-over-year. Consequently, he’s watching too many pitches go by in the zone, and failing to capitalize on the pitches he goes after. Not to mention that his approach lends itself to a low BABIP when he does put the ball in play. Whereas in 2017, he was able to maintain a decent batting average despite a terrible BABIP, because his 88.8 Z-Contact% was quite good (above the 86.6% MLB average).

Let’s return to Trout. Despite being patient like Hoskins and striking out a lot, Trout has hit for excellent batting averages each of the last three years. The combination of a low swing rate, (relatively) high strikeout rate, and high batting average is possible either with an inflated BABIP or excellent swinging-strike and Z-Contact rates.

For example, Trout’s batting average was .312 in 2018, undoubtedly the product of an elevated .346 BABIP. However, he was able to overcome his called strikes in 2017 and 2019, seasons in which he had relatively typical BABIPs: .318 and .298. But how? He maintained far higher Z-Contact (90.1% and 88.8%) and lower swinging-strike (6.2% and 6.5%) rates than Hoskins and the MLB average in both seasons.

**Conclusion**

In sum, patient hitters do not succeed, on average, more than other hitters in terms of batting average. They take more called strikes than their counterparts, and will, therefore, sustain higher strikeout rates than their swinging strike rates would portend.

Patient hitters can compensate for those called strikes only if they run exceptionally high BABIPs or are excellently skilled in terms of swing and miss. On the latter score, they must make more contact in the zone and keep their swinging-strike rates down.

Check back in next week for a discussion on the successes and failures of aggressive hitters.

*Photo by Brian Rothmuller/Icon Sportswire | Adapted by Justin Paradis (@FreshMeatComm on Twitter)*