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.
Like any stat, OBP is not without its flaws. For one, OBP does not tell us how a player reached base. A home run and a walk count the same when computing OBP. Obviously, a home run is far more valuable than a walk. In addition, OBP is context neutral, meaning that a single with the bases empty and no outs counts the same as a single with the bases loaded and two outs.
In cricket, a player's batting average is the total number of runs they have scored divided by the number of times they have been out. Since the number of runs a player scores and how often they get out are primarily measures of their own playing ability, and largely independent of their teammates, batting average is a good metric for an individual player's skill as a batter.
The conclusion of the GIIB article shows that team gOBP has a correlation coefficient of 0.95 with R/G, a slight but meaningful improvement over the correlation coefficient of 0.93 between team OBP and R/G. This first test is straightforward: using Retrosheet, I collected team R/G, OBP, and gOBP for all 1,482 team seasons dating back to 1955. I then fit a linear model to these data and computed the correlation coefficients for each pairing. The results are below.
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.
Runs scored (R): Aside from when a player hits a home run, he'll need some help from teammates -- most of the time -- to score a run. Still, players can do a lot to increase the number of times they score. Getting on base frequently, stealing bases, taking extra bases on singles or doubles, taking a base on a passed ball right in front of the catcher or even being such a scary baserunner to draw a balk are ways to spin around the bases better than others. Guys who score the most runs are great players, not those simply lucky to have good teammates driving them in. Here are the top 10 all-time in runs scored: Rickey Henderson, Ty Cobb, Barry Bonds, Hank Aaron, Babe Ruth, Pete Rose, Willie Mays, Cap Anson, Stan Musial and Alex Rodriguez. Marginalize that group at your peril. - Snyder

On-base percentage, or OBP, measures the frequency with which a batter reaches base. OBP is expressed as a decimal rounded to three places, as in .300. Thus, OBP looks like batting average. However, instead of expressing the number of hits per at-bat, OBP represents the number of times on base per opportunity. The formula is simple, and one needs just five basic counting statistics to calculate OBP. These five stats are hits, walks, hit-by-pitch, at-bats, and sacrifice flies. The formula is:
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.
Psychologists have observed that human babies as early as eight months of age already have expectations about the movements of objects within their field of view, which they cannot possibly have learned from experience, and which therefore must have been wired into their brains by the processes of evolution. And by the age of one year, human babies already have acquired some ability to catch and throw objects, an ability which improves with practice and neurological development.
Overall, 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.
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Just before the pitcher pitches the baseball, you should be standing in a perfect stance so that you can hit the ball right. A good stance includes planting your feet firmly on the ground, slightly wider than your shoulders and your weight should be balanced on the balls of your feet. Such a stance will give you the rapid swinging freedom which you need when swinging the bat at the incoming ball.
Powerful Legs that are trained through various movement patterns and skill sets. For example, you create a ton of power by super setting (performing these two exercises one after the other with little rest, then repeating) an exercise like a squat and a box hop. This combination of a strength development exercise and a plyometric exercise create explosive power.
Moving a single group of muscles may require the interaction of numerous nerve cells and involve multiple synaptic delays, as the body receives sensory information (sees the ball), processes it (makes a decision), and coordinates muscle action (swings the bat). Practice eliminates wasted time by speeding up the decision-making -- somehow the obvious mistakes and fruitless actions a novice spends time thinking about are simply ignored by the practiced player, and his brain saves the time needed to consider them. But basic reaction time due to nerve conduction and synaptic delay remains an irreducible constant of the game.
How do these players create such a powerful swing? Rule number one; do not confuse POWER with strength. These are two very different dynamics. Power is an explosive movement. It is created through a combination of speed, and strength. Strength is created through maximal force. This is the biggest learning lesson here; you do not create power for hitting by lifting maximum weight. Power lifting like bodybuilders and muscle heads do, does not translate into softball power hitting. Power lifting is mostly all for show and not athletic performance. If you have the biggest arms, traps, and chest in the world, how are you going to swing the bat? Power lifting and Power hitting are two totally different things.

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.
While watching a New York Mets game early this season, before one plate appearance the broadcast graphics displayed that catcher John Buck had a batting average (AVG) of .400 and an on-base percentage (OBP) of .396. Several games later I saw a similar phenomenon for Buck, whose OBP was again several points lower than his AVG. (In other words, the stats presented were very likely to be actual and not an on-screen typo.)
The strongest man doesn’t always have the hardest hit, technique determines many factors and in most cases is more important than your strength or power levels. But if you’re somebody who has your batting technique in check, adding strength and power to the muscles responsible for improving your performance in this area can dramatically alter your presence on the plate.
"Graham," you're probably thinking, "I know the rules of baseball. Why is this relevant?" Well, teams that are better at avoiding outs score more runs than teams that make outs frequently. This is intuitive when you think about it. For one, when a player doesn't make an out, he reaches base. At the simplest level, scoring runs is a function of reaching base and advancing runners. A player who reaches bases also hasn't used one of those precious 27 outs, thus giving batters behind him the opportunity to advance him and others around the bases. This is why sacrifice bunts do not make sense in several situations. It is rarely a good idea to attempt to use an out. We look at OBP in order to determine which hitters and teams reach base with regularity, and thus preserve a team's outs.
Batting average (AVG): Right off the bat, we've got to say that batting average is not the best stat to judge offense. There's too many things wrong, starting with the fact that there are a lot of variables involved such as not including walks, or the subjective nature of awarding hits and errors. Still, what batting average does have over all the other statistics is history and context. We all know what a .300 hitter is, we know how bad a .200 hitter is and how great a .400 hitter is. We still celebrate the batting crown, and although not as much as we once did, it still means something and probably should. -- C. Trent Rosecrans
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:

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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.
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