How to Analyze and Play Against Enyel De Los Santos
I spent a lot of time breaking down right-handed pitching lately, and Enyel De Los Santos came up more than I expected given where he is in his career. He is a 6-foot-5 right-handed reliever who has been with the Blue Jays and later the Rays, and his game comes down to a handful of pitches used in fairly predictable patterns. If you are trying to study his tendencies or understand how batters approach him, here is the practical breakdown. His primary weapon is a slider that sits roughly in the 85 to 90 mile-per-hour range. It is not an extreme breaker, but it has enough lateral ride to chase hitters off the plate when he works outside. He pairs that with a four-seam fastball that touches around 95 to 97, though it is not an overpowering pitch on its own. The changeup is his third option and it is usable, not just a show pitch. He throws it about 10 to 12 percent of the time, mostly when he needs to keep left-handed hitters honest or when he is ahead in the count.
Enyel De Los Santos Pitch Tendencies and What They Mean
The first thing you notice is how heavily he relies on the slider against left-handed hitters. In my sessions studying his Stuff+ data from Statcast, his slider rates above average in whiff percentage, which tells you it actually misses bats rather than just looking good on video. That matters because a lot of relievers throw sliders that look sharp but generate weak contact. His does not do that. Against righties, he goes to the changeup more often, but the data shows it is less effective. Hitters are making contact at a higher rate when he throws it to right-handed batters, and the whiff numbers drop noticeably. This is the kind of detail that does not show up in a quick scouting report but matters if you are preparing for a specific matchup or building a fantasy strategy around him. I ran into a problem last season when I was compiling matchup data for a low-level farm team. The public Statcast numbers made it look like his changeup was completely unreliable, so I adjusted our approach to attack it early in counts. What I found on the repeat was that the pitch still worked well when he was ahead 0-2 or 1-2. The issue was that we were seeing his changeup in hitter-friendly counts where he was essentially forced to throw it, and the results reflected that. The workaround was straightforward: I shifted our focus to how he was sequencing pitches in early counts rather than relying on aggregate numbers. His slider down and away in 0-1 and 1-0 counts was the real story, and once I isolated that, the data became actually useful for game planning.
Another thing that is easy to miss is how his release point creates a slight advantage against same-side hitters. At 6-5 with a somewhat high arm slot, his fastball and slider both get a bit of vertical break that makes them harder to track for left-handed batters facing him. Righties have to deal with more horizontal life, which is why the changeup becomes necessary but also why it gets hit harder. It is a simple tradeoff that pitchers in his role deal with constantly. If you want to study him further, the best sources are FanGraphs for his splits and Statcast for raw pitch tracking data. His Baseball Savant page will show you the heat maps and swing-and-miss rates by pitch type. Do not spend too much time on ERA or WHIP as a standalone measure because he has bounced between teams and roles, which skews the context. Look at strikeout rate, walk rate, and the split between lefty and righty OPS instead. Those numbers hold up better across different sample sizes and ballpark factors. The main limitation of using this kind of analysis is that Enyel De Los Santos is still somewhat of a journeyman in the grand scheme. He has not settled into a permanent high-leverage role, which means his pitch sequencing can shift depending on the manager and the situation. A game plan built around his tendencies from 2023 may not hold perfectly in 2025. It is worth cross-referencing with his most recent game logs to make sure you are not relying on outdated data.
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