*“Baseball is like church. Many attend few understand.*” ~ **Leo Durocher**

Durocher, a 17-year major league vet and Hall of Fame manager, sums up the game of baseball quite brilliantly in the above quote, and it’s pretty ridiculous how much fans really don’t understand about the game of baseball that they watch so much. This holds especially true when you start talking about baseball stats. Sure, most people can tell you what a home run is and that batting average is important, but once you get past the basic stats, the rest is really uncharted territory for most fans.

But fear not! This is your crash course in advanced baseball stats, explained in plain English, so that even the most rudimentary of fans can become knowledgeable in the mysterious world of baseball analytics, or sabermetrics as it is called in the industry. Because there are so many different stats that can be covered, I’m just going to touch on the hitting stats in this article and we can save the pitching ones for another piece. So without further ado â€“ baseball stats!

## The Slash Line

The baseball “slash line” typically looks like three different numbers rounded to the thousandth decimal place that are separated by forward slashes (hence the name). We’ll use **Mike Trout**‘s 2014 slash line as an example; this is what a typical slash line looks like: .287/.377/.561

The first of those numbers represents *batting average*. While most fans know about this stat, I’ll touch on it briefly just to make sure that I have all of my bases covered (baseball pun intended). Batting average is calculated by dividing a player’s total number of hits by their total number of at bats, which gives you a number that tells you how often (on average) that player gets a hit.

If you take a batting average and multiply it by 100 (or slide the decimal point over two spots to the right), it will give you a raw percentage of how often a player gets a hit. So using Mike Trout’s batting average as an example, he accumulated 172 hits in 602 at bats last year for a batting average of .287 (172/602). Multiplying .287 by 100 gives you 28.7 which tells you that in 2014, Mike Trout averaged a hit in 28.7% of his at bats.

Batting Average in Context | ||

League Average |
Best in 2014 |
Worst in 2014 |

.251 | .341 | .196 |

The second number in a slash line represents *on base percentage*. This is calculated by dividing the total times a player gets on base (hits, walks, and hit-by-pitch) by a player’s total number of eligible at bats, essentially all trips to the plate minus events outside of the batters control, like reaching on error and hitting into a fielder’s choice). These “eligible at bats” are calculated by adding regular at bats with the total number of times walked, hit-by-pitch, and hit into a sacrifice fly. That gives you the following formula to calculate on-base percentage, or OBP for short.

Just like with batting average, OBP can be easily turned into a raw percentage by multiplying it by 100. Going back to Mike Trout’s slash, his OBP of .377 means that in 2014, he got on base an average of 37.7% of the time.

On Base Percentage in Context | ||

League Average |
Best in 2014 |
Worst in 2014 |

.314 | .410 | .256 |

The third and final number in a slash line represents *slugging percentage*. This number is very similar to batting average, but instead of treating all hits as equals, it weighs each type of hit according to its significance. Slugging percentage (or SLG) is calculated by adding singles, 2 X doubles, 3 X triples, and 4 X home runs all divided by at bats. Another way of looking at it is total bases divided by at bats. Here is the official formula that is used:

The main application of slugging percentage is to go beyond just being able to tell how good a player is at getting hits, but how good they are at getting *quality* hits. For example, **Robinson Cano** and **Andrew McCutchen** both had a batting average of .314 last year; however Cano slugged just .454 opposed to McCutchen who finished with a .542 mark. While both players got hits just as often, McCutchen got the more valuable kinds of hits more often (he had more doubles, triples, and homers than Cano), so he was the better hitter in 2014.

Slugging Percentage in Context | ||

League Average |
Best in 2014 |
Worst in 2014 |

.386 | .581 | .300 |

So now going back to the original example of Mike Trout’s 2014 slash line (.287/.377/.561), you should be able to look at it and know not only what statistic each number represents, but what it means in regards to Trout’s value as a player. Before we move on to the next section, however, I also want to mention two stats that are commonly associated (and sometimes included) with the slash line, the first of which is OPS.

OPS stands for *on base plus slugging* and is exactly what it sounds like. You take a player’s OBP and add it to their SLG to get OPS. This stat is often used to measure a player’s overall ability as a hitter, combining their skill at getting on base (OBP) with their aptitude to hit for power (SLG). Sometimes it will be included at the end of a typical slash line, so if you see a slash with four different numbers in it, then OPS is what the fourth one represents.

On Base Plus Slugging in Context | ||

League Average |
Best in 2014 |
Worst in 2014 |

.700 | .974 | .568 |

The other statistic I wanted to mention that sort of goes hand-in-hand with slugging percentage is *isolated power*, or ISO for short. This is calculated by subtracting batting average from slugging percentage (SLG-AVG) to give you how many extra bases a player averaged per at bat. ISO is important because it removes singles from the equation to give you a better idea of a player’s true power capability.

To best explain this concept, take **Brian Dozier** and **Denard Span** for example, who each slugged .416 last year. You would think that they both hit for a lot of power because of the high SLG, but Dozier actually hit 18 more home runs than Span (who had 44 more singles). Dozier’s ISO was .174 opposed to Span’s below-average mark of just .115.

Isolated Power in Context | ||

League Average |
Best in 2014 |
Worst in 2014 |

.135 | .279 | .055 |

## Plate Discipline

Now that we’ve taken a good look at the stats that compromise a typical slash line, we can move onto the next category which is measuring a player’s plate discipline. The first stats I wanted to touch on are fairly simple ones, yet quite important: *K%* and *BB%.* These are essentially the same thing as strikeouts and walks, but applied as a rate-based stat by dividing it by the player’s total number of plate appearances.

These are important because they show you how often (on average) a player walks and strikes out. Unlike batting average, K% and BB% are given in a direct percentage format, so there’s no need to translate it. The application of these stats is pretty straightforward; a player with a high BB% and low K% would typically have a good batting eye, and a player with opposite-type numbers would typically have a poor batting eye.

K% and BB% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

20.4% / 7.6% | 33.0% / 17.1% | 6.6% / 2.1% |

Another very common stat used to measure plate discipline is a player’s *walk-to-strikeout-rate* (or BB/K). This is again a fairly straight forward stat, and it is measured simply by dividing a player’s walk total into their strikeout total. The result gives you the average number of times a player walks between each strikeout. So if a player had a BB/K of an even 1.00, that would mean that they walked just as often as they struck-out (which is exceptionally good plate discipline).

BB/K in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

0.37 | 1.67 | 0.14 |

Those three stats (K%, BB%, BB/K) are the bulk of what is used to determine a player’s plate discipline, but there are actually quite a few more advanced stats that can be used get a much deeper look into a player’s approach at the plate. It’s really not necessary to get very in-depth with these stats, but a simple description and league context is really all you need to be able to apply them.

*O-Swing%* – This represents the percentage of pitches a player swings at that are outside of the strikeout zone. It makes sense that the more you swing at bad pitches, the less likely you are to get a hit (unless you’re **Vladimir Guerrero**), so the lower the percentage the better.

O-Swing% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

31.3% | 48.1% | 19.3% |

*Z-Swing%* – This represents the percentage of pitches a player swings at that are inside the strikeout zone. It isn’t really a “good or bad stat”, but a low Z-Swing% typically denotes that the particular player has a more patient approach at the plate.

Z-Swing% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

65.7% | 82.3% | 49.4% |

*Swing%* – This represents the percentage of total pitches a player swings at. Similar to Z-Swing%, a lower percentage usually signifies more patience.

Swing% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

46.7% | 59.5% | 33.1% |

*O-Contact%* – This represents the percentage of pitches a player makes contact with that are outside the strikeout zone. Obviously the more contact that is made the better, so players with a higher O-Contact% are better at putting the barrel on the ball.

O-Contact% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

65.8% | 88.1% | 43.4% |

*Z-Contact%* – This represents the percentage of pitches a player makes contact with that are inside the strikeout zone. Usually a pretty high number, this designates how good a player is at taking advantage of strikes that are thrown his way.

Z-Contact% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

87.3% | 97.3% | 73.9% |

*Contact%* – This represents the percentage of total pitches that a player makes contact with. The application is literally in the name, so players with a higher Contact% are better at making overall contact than players with a lower one.

Contact% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

79.4% | 92.4% | 65.3% |

*Zone%* – This represents the percentage of pitches a player sees inside of the strike zone. A low Zone% usually means a batter has a high Swing%; pitchers don’t typically throw a lot of strikes to batters that are known to swing at a lot of pitches.

Zone% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

44.9% | 51.8% | 35.6% |

*F-Strike%* – This represents the percentage of first pitches a player sees that end up being strikes. It usually characterizes a “fear factor” for pitchers and shows how willing (or unwilling) they are on average to attack this particular hitter right out of the gate.

F-Strike% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

60.6% | 66.7% | 50.5% |

*SwStr%* – This represents the percentage of pitches at which a player swings and misses. It’s pretty common sense that swinging and missing is not good, so the lower SwStr% the better the player is at making contact.

SwStr% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

9.4% | 16.5% | 3.2% |

## Batted Ball Data

Now that we’ve covered slash line statistics and plate discipline numbers, all that’s left to go over is batted ball data. The most common batted ball stat that is used is *batting average on balls in play*, or BABIP for short. While a typical batting average tells you how often a player gets a hit in general, this batting average determines how often a player ends up getting a hit when they hit the ball within the field of play. It is calculated by subtracting home runs from totals hits and dividing that by at bats minus strikeouts minus home runs plus sacrifice flies, which translates to the following formula:

Overall,Â BABIP is a stat that is largely out of the batters control, which makes sense because as a hitter, once you hit the ball onto the field, you can’t affect what happens next. The league average BABIP is usually right around .300, meaning balls in play typically land for a hit about 30% of the time. So in theory, if a player has a BABIP of .350, you might say he had a “lucky” season, and you could expect him to regress the following season, right? On the surface it makes sense, but the whole point of this article is to look beyond just the surface stats, and that is exactly what we will do.

Batting Average On Balls In Play in Context | ||

League Average |
Best in 2014 |
Worst in 2014 |

.299 | .373 | .231 |

Batters actually hold a decent level of influence on their BABIP, which is something that not a lot of people realize. Because there are different types of hitters (mainly speed, power, and contact hitters), not everyone should be expected to have the same “30% outcome” for balls in play. The main source of this influence comes from what is known as a player’s “batted ball profile,” which consists of the following stats:

*LD%* – This stands for *line drive percentage*, which is the percentage of balls a player hits that end up as line drives. As you might imagine, line drives are harder to field than any other type of batted ball, so you can expect them to fall for hits much more often. The league average on liners last year was .690, which means that you can expect a line drive to fall for a hit roughly 69% of the time. It makes perfect sense, then, that the more line drives a player hits, the higher you can expect their BABIP to be. This is supported when you compare the BABIP of players with a LD% above-league average (.313) to their counterparts with a below-league average mark (.297).

LD% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

20.8% | 31.0% | 13.5% |

*GB%* – This stands for *ground ball percentage*, which is the percentage of balls a player hits that end up as ground balls. The league average on grounders last year was just .239, which means that only about 24% of ground balls end up as hits. So, because of that, you would expect players who hit a lot of grounders to have a lower BABIP, right? This is not necessarily true, however, because most ground ball hitters end up being the speedsters that are more likely to beat out grounders than your average player. In general, you can expect players with a high GB% to have a slightly higher BABIP, but you definitely want to take a look at their speed before making that assumption.

GB% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

44.8% | 64.7% | 27.1% |

*FB%* – This stands for *fly ball percentage*, which is the percentage of balls a player hits that end up as fly balls. Flies are the type of batted ball that are least likely to end up as a hit, and the league average is just .212 for a 21% success rate. Also, because a lot of fly balls end up as home runs, the league average BABIP for flies is even lower at .126, which tells us that fly balls that stay in the field end up as hits just 13% of the time. It’s no secret that players who hit lots of flies will suffer in the BABIP department, and a quick comparison of players with an above-league average FB% (.297 BABIP) to their counterparts (.318 BABIP) will really drive home that argument.

FB% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

34.4% | 51.4% | 14.3% |

*IFFB%* – This stands for *infield fly ball percentage*, which is the percentage of fly balls a player hits that end up as infield pop ups. Lazy flies to the infield are about as easy to field as they come, so they are considered essentially automatic outs. Because of that, it would be fair to say that a player who hits a lot of infield flies is not likely to have a very good BABIP. However, even the player with the worse IFFB% last year was at just 17.3%, so hitting a lot of automatic outs isn’t going to make a huge difference, but definitely a noticeable one. Batters who avoided these easy outs last year (better-than-league average IFFB%) had a better BABIP (.312) than their counterparts who did not (.298).

IFFB% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

9.6% | 17.3% | 0.0% |

*HR/FB%* – This stands for *home run to fly ball rate*, which is the percentage of fly balls a player hits that end up as home runs. While this stat doesn’t play much of a role in BABIP due to the fact that home runs are factored out of the BABIP equation, it is definitely a key component of a player’s batted ball profile. HR/FB% is a stat that is largely skill based, but typically doesn’t see much fluctuation from year-to-year, so a player that posts a HR/FB% much lower than their career norm is very likely to bounce back the following season and vice versa.

HR/FB% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

9.5% | 26.9% | 1.0% |

*IFH%* – This stands for *infield hit percentage*, which is the percentage of ground balls a player hits that end up being infield hits. It actually ties right into the fact I mentioned earlier about speedy players beating out grounders, and IFH% is the stat we use to measure that skill. Players with a GB% and IFH% that were both above-league average put up a .315 BABIP last year, as opposed to their counterparts, whose BABIP was just .300.

IFH% in Context | ||

League Average |
Highest in 2014 |
Lowest in 2014 |

6.5% | 15.9% | 1.2% |

While all those stats can be very helpful individually, using them all to establish a batted ball profile will help you to get a solid idea of what a player’s hitting skill set really is. For the most part, a player with a solid LD% and IFH% can be expected to put up an above-average BABIP, while a player with a large FB% and IFFB% can be expected to post a below-average mark. With that framework in mind, let’s look at a quick example from the 2014 season:

**Joe Mauer**– .342 BABIP, 27.2 LD%, 50.8 GB%, 21.9 FB%, 0.0 IFFB%, 2.7 IFH%**Marlon Byrd**– .341 BABIP, 22.7 LD%, 36.9 GB%, 40.3 FB%, 11.5 IFFB, 6.0 IFH%

Both of these players sported BABIPs that were much higher than the league average. However, based on their batted ball profiles, only one of them possesses the skill set to maintain a high BABIP, while the other is due for regression next year. Can you figure out which is which?

## Conclusion

Now that we’ve covered slash lines, plate discipline, and batted ball data, that about does it for the hitting side of advanced baseball stats, but before I wrap up, I need to mention one important thing. All of the statistics that I used were from players who had a qualifying season (3.1 plate appearances per team game which is roughly 500 PA). The pre-mentioned stats are most effectively used when you have a good sample size of data to work with, and you should watch out for stats that are skewed by small sample sizes. Make sure that when you are evaluating a player’s skill set, they have accumulated enough plate appearances (usually you want to aim for a minimum of 100) to make the data you’re working with relevant.

A good resource to find all of the statistics mentioned here (and much, much more) is FanGraphs.com, as they have all of these stats on every one of their player pages, as well as superâ€“convenient sortable tables and charts.

Hopefully this overview of these advanced baseball stats was helpful for you, and if you have any questions or would like a further explanation on anything I covered, feel free to let me know in the comment section below or reach out to me on Twitter @ZachPincince.

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