Slugging percentage (SLG), the preferred statistic of Jim Leyland, is simply the number of total bases, again not counting walks, divided by the number of at bats. Four bases for a homer, three for a triple, two for a double, and one for a single. Slugging percentage has been around at least since I was a kid, and there was a regular column for SLG in the stat charts listed in the Detroit News every Sunday. The problems with SLG are that a triple isn’t really three times as valuable as a single, and a base on balls is treated like it never even happened. If you want to "just knock em in," that’s fine, but a triple doesn’t put three guys on base to knock in. They have to get on base or you can’t knock em in.
Here's a quick example: Ichiro Suzuki had a record 262 hits in 2004. He also walked 49 times and was hit by 4 pitches. The sum is 262 + 49 + 4 = 315. He had 704 at bats, 49 walks, 4 hit by pitches, and 3 sacrifice flies on the year. That sum is 704+49+4+3=760. Dividing 315 by 760 gives the on base percentage of .414. That's not too bad, but it's not much higher than his batting average, which was an impressive .372. By comparison, Jose Bautista had a respectable batting average of .286 in 2014, but still reached base at a very strong .403 clip, helped by 104 walks.
Josh Donaldson hit .255 and scored 93 runs; think some of those 76 walks helped him out?  Brandon Moss was the man to own in the first half even with a .268 BA, but was dropped like a rock in the second half where he hit .173.  His OBP slipped from .349 down to .310, but at least he was still playable thanks in part to 14.8% walk rate.  Adam Dun hit .219 in 2013 and while he hit 34, owners cursed him.  Forget the 76 walks and .320 OBP though, it doesn’t count in fantasy.  In 2012 Dunn hit 41 home runs and scored 87 times, but a .204 batting average had him on America’s most hated list.  Using OBP you could have had .333 thanks in part to his 105 walks which batting average didn’t take into consideration.  Dunn’s value in 2012 using OBP was slightly above Adam Jones and his 34 walks.  Dunn had 71 more walks and Jones had 76 more hits, similar results but Jones is rewarded for being on base an equal amount of times. 
For small numbers of at-bats, it is possible (though unlikely) for a player's on-base percentage to be lower than his batting average (H/AB). This happens when a player has almost no walks or times hit by pitch, with a higher number of sacrifice flies (e.g. if a player has 2 hits in 6 at-bats plus a sacrifice fly, his batting average would be .333, but his on-base percentage would be .286). The player who experienced this phenomenon with the most number of at-bats over a full season was Ernie Bowman. In 1963, with over 125 at-bats, Bowman had a batting average of .184 and an on-base percentage of .181
Great article! Your explanation of what it means to “relax” is definitely something all hitters struggle with, including myself. I’ve always been a “power-hitter”, but I didn’t really start hitting HRs consitently until I started putting backspin on the ball. For me at least, focusing on my swing and trying to have a backspin-promoting cut helped me RELAX and take the focus off of trying to kill the ball. I would grip the bat way too tight and pull everything, which was really frustrating. Another thing that helped me keep my hands relaxed was I started using that Pro-Hitter thumb ring that I saw pro’s like A-Gon and J Hamilton using… Again, great article! Thanks for providing more insight on something that all of us wish we could do at every at-bat haha! One can only dream…

Traditionally, players with the best on-base percentages bat as leadoff hitter, unless they are power hitters, who traditionally bat slightly lower in the batting order. The league average for on-base percentage in Major League Baseball has varied considerably over time; at its peak in the late 1990s, it was around .340, whereas it was typically .300 during the dead-ball era. On-base percentage can also vary quite considerably from player to player. The record for the highest career OBP by a hitter, based on over 3000 plate appearances, is .482 by Ted Williams. The lowest is by Bill Bergen, who had an OBP of .194.
Comparing a baseball or softball swing to a car engine is something that I do almost everyday. It’s an easy way to help kids and parents understand how the system inside the swing works. For someone who doesn’t look at hundreds of swings a day, it can be difficult to identify or help a player become a more efficient swinger of the bat. A lot of times coaches will see a result like a pop up or ground ball and associate the weak contact with lack of effort. Most of the time, this is simply not the case. In the following article I hope to help players understand the importance of not making “early mistakes” and also help coaches and parents break down the efficient swing. To do so, we will break the swing down into three phases.  The three phases are 1. Acceleration/Angle Creation, 2. Maintain, 3. Release. They are illustrated in the picture below in a Playoff home run by Francisco Lindor.

Of course, on base percentage isn't the only important statistic to determine the effectiveness of a baseball player.  This is because all walks drawn only put the hitter on first base and will rarely drive in a run, while hits are capable of putting the hitter on 2nd or 3rd base, or even crossing the plate with a home run.  Along with that, hits are capable of driving in many more runs than walks.  Other stats are used to calculate these, such as slugging percentage and OPS, or on base percentage + slugging percentage.  These statistics and their impact on baseball will be examined in later articles.  In the case of on base percentage, it is a hugely underrated stat that pays dividends for individuals and teams willing to take pitches.  It allows teams such as the Chicago White Sox, despite an extremely low team batting average, to still compete and put up a lot of runs.  Although it can't necessarily be proven that on base percentage is more important in judging the effectiveness of baseball players, it can be nonetheless shown that a hitter without an extremely high batting average can still be a great contributor and table setter for a major league team.

But there are other, better methods for predicting team runs scored. Statistics like weighted on-base average (wOBA) and runs created assign different weights to different events (e.g., a home run and a walk), and correlate even better with team offense. What about individual performance? Maybe gOBP does a better job of predicting a batter's true talent level, and thus is less random from one year to the next.
An example is the Internet Archive, which uses the term in ranking downloads. Its "batting average" indicates the correlation between views of a description page of a downloadable item, and the number of actual downloads of the item. This avoids the effect of popular downloads by volume swamping potentially more focused and useful downloads, producing an arguably more useful ranking.