Analytics And Baseball

Many baseball fans are upset with the current state of baseball. “The game’s not the same”, they say. Even renowned baseball analyst Tim Kurkjian has taken notice. Strikeouts are up and entertainment is down. While an argument can be made about what the real issue is with the modern game, the bottom line is strikeouts are up. The reason for that could be the quality of pitchers and pitchers still hitting. However, most people conflate strikeouts with analytics. After all, the rise of analytics has seen the rise in strikeouts.

However, analytics has nothing to do with strikeouts. Yes, there has been a change in philosophy in baseball and strikeouts are up as a result. Offenses have stopped trying to string together hits in a row. Teams are less interested in a single, stolen base, sacrifice bunt, sacrifice fly, groundout, and more interested in a strikeout, home run, strikeout, strikeout. Even a multi-hit inning has been ditched in favor of hitting one home run.

There’s a couple of reasons for this. Teams have begun figuring out that stringing those hits together is hard. Pitching is so good today that expecting to string together three hits in an inning is nigh impossible. For most teams, two and three starters are more than capable of pitching an excellent outing, making the hitter’s job (which was already really hard) even harder.

But analytics isn’t the result of this shift. It’s not causal, either. The rise of analytics and the “fall” of baseball coming at relatively the same time is purely coincidental. Analytics has nothing to do with this. It’s purely a change in philosophy. What analytics does, contrary to the popular belief, is improve the game.

Many fans decry the fact that a .300 hitter (while becoming rarer and rarer) isn’t the telltale sign of a good hitter anymore. A 3.00 ERA doesn’t mean what it used to. 100 RBI’s is a meaningless plateau. The fact of the matter is that all those things were always true. We just didn’t know it. The .300 batting average isn’t always indicative of the quality of the hitter, we just never had the additional numbers to determine that until analytics came around.

Analytics’ primary goal is a more accurate player analysis. We can finally determine how good that pitcher with a 2.38 ERA is. We can determine if the hitter with a .322 batting average is really that good. We can also determine if a player who appears to be struggling, say with a .231 batting average, is actually good or not.

Look at Ronald Acuña, Jr. His .279 batting average isn’t inspiring. In fact, it’s 39th in the league. Shohei Ohtani, a frontrunner for MVP, is hitting .270, good for 55th. But if you look at wRC+ (weighted runs created plus) it tells you just how good they’ve actually been. Acuña, Jr.’s 163 wRC+ puts him at ninth in baseball, and Ohtani’s 160 places him 11th. That’s not something you’d guess by looking at their slash lines.

Matt Olson is hitting .264 but his 153 wRC+ is 15th in MLB. His Oakland teammate Mark Canha has a .254 batting average, but a 151 wRC+, putting him at 18th. Jose Ramirez is hitting .251 with a 137 wRC+, 34th among hitters. Mike Yastrzemski is hitting .221, something that would have been looked upon with disgust. His 120 wRC+ looks pretty good, though. And wRC+ isn’t the be-all-end-all, it just gives you a better look at how good someone’s hitting.

For pitchers, a good stat to look at is FIP (fielder independent pitching). This tells us if a pitcher has been helped by his fielding. A low FIP indicates he’s been pretty great regardless of the defense (or in spite of). Robbie Ray sports a 3.42 ERA. Solid, right? Well, his 4.86 FIP puts him at fourth worst. Jose Berrio’s 4.84 ERA is pretty rough, but a 3.40 FIP puts him at 27th in baseball.

Another side of analytics is the expected stats. These are great in determining the luck factor of hitting. When a player hits a 102 mile per hour line drive right to the first baseman who is shifted perfectly, that’s pretty unlucky. It’s the same with having a home run robbed. Expected stats remove the luck and tell us what the player should be at, in normal circumstances. Each batted ball has an xBA and those add up to see where a player would be without luck- either luck for him or against him.

For example, Detroit Tigers Harold Castro is hitting .341. Is he that good? well, his xBA indicates that, yeah, he’s pretty good. His .347 xBA is tops in the league. Aaron Judge is second with a .345 despite having a .308 batting average which wouldn’t rank second. Nick Senzel is hitting .255 with a .306 xBA indicating he’s been better than his batting average says.

The same thing goes for pitchers. Atlanta’s Jacob Webb has a .338 batting average against, but an xBAA of .269, so he’s been the unluckiest pitcher thus far. The Mets Joey Lucchesi has a whopping 7.32 ERA but an xERA of 3.14 so he’s also been one of the unluckiest pitchers in baseball. The Braves Luke Jackson sports a sterling 1.47 ERA, but his xERA is 5.20.

Analytics has improved baseball to the point, at least to where we can have a much fuller, more accurate analysis of players. Yes, baseball has changed and it seems to be losing some offensive luster but it’s not because of analytics. Analytics is the best thing to happen to baseball in a while.