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
The human ability to estimate trajectories of moving objects is difficult to explain. Good fielders begin their movement just as the ball is hit, without wasting even half a step. An outfielder instantly begins running toward the spot where he thinks the ball will fall. Sometimes, he will make a running catch without losing a stride, thrusting his glove into position at the last second.
Prior to 2010 we only listed the top 100 and monitored those who made and slipped off the list. Here is that original fast fact preserved and now useless due to the list including 1000 names: Modern superstars are making the list as they meet the one-thousand minimum games played threshold: In 2001 Jeff Cirillo & Manny Ramirez met the requirements and joined the top one-hundred. In 2002 Cirillo slipped off the chart and Jason Giambi made it while Chipper Jones & Alex Rodriguez missed the cutoff by less than 2/1000 of a point. In 2003 Jason Giambi slipped off the chart, Chipper Jones just missed it once again (his career average is .30870), and Vladimir Guerrero vaulted onto the list at forty-first — higher than any other active player, that is until 2004 when Todd Helton launched into the top 20 all-time.
If you're looking for distance, commit this tip to memory: Your weight should move in the direction the club is swinging. When the club goes back, your weight shifts back; when the club swings through, your weight shifts through. A common fault with amateurs is the reverse pivot, where the weight stays on the front foot during the backswing and often falls to the back foot on the downswing. That's about the weakest move you can make.
Equal power and equal run scoring abilities, yet using batting average, Dozier is inferior.  It doesn’t seem fair that two players of equal skills are ranked so far apart in fantasy, but player X had 31 more hits while Dozier had 31 more walks with the same results.  If you’re a numbers guy you might have guess who player X is, but for those that haven’t figured it out, it’s Anthony Rendon.  Rendon is shooting up draft boards while Dozier is left waiting until the mid-early rounds.  If there was a poster boy for using OBP over BA, it’s Dozier. 
Fossil evidence indicates that early humans hunted and ate other animals, and this seems to suggest that our catching and throwing capabilities may well be rooted in our development as hunters and tool users. Hitting or catching a moving animal requires an ability to estimate its path in advance. These are the basic skills required for every game of catch and throw, but for our ancestors, they may have been requirements for survival.
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. 
Certainly this feat also involves an almost instantaneous ability to estimate trajectories. In the case of the frog, researchers have been able to locate specific nerve cells in the frog retina and in the brain which are excited by small, dark, moving objects. The frog apparently pays no attention to these objects until their images begin to grow bigger on his retina, indicating that they are moving closer to him. If all other visual cues are right, out goes the tongue.
Some fantasy scoring systems count on-base percentage in lieu of batting average. But regardless of a league's offensive-rate stat of choice, OBP tends to correlate with runs scored. And because Major League front offices value OBP highly, low-average hitters often receive their ample share of playing time -- and, thus, opportunities to accumulate fantasy counting stats -- as long as they walk enough to post satisfactory OBPs.
Recall that we can use the batter's OBP, the pitcher's OBP, and the league OBP to find an expected OBP for a given matchup using the odds ratio. Since gOBP is still a proportion, we can use it to perform the same analysis. To determine which is more accurate, we first group the batters and pitchers into bins with width five points (.005). We then find an expected OBP for all pitchers and batters in that bin, and compare this to the actual results of those matchups. As an example, consider the first pair in our database: David Aardsma and Bobby Abreu, who faced each other once in 2010.
Getting on base is an important skill, so you want to use OBP to determine if the player in question is a good offensive performer. However, OBP can only take you so far and it should only be used in the context of other statistics because OBP weights every time you reach base equally, whether you hit a home run or an infield single. If used in conjunction with slugging percentage or isolated slugging percentage, OBP is a very useful tool. In general, something like wOBA or wRC+ will tell a more accurate story, but if you’re looking for something extremely simple OBP is a much better bet than batting average.
Shoeless Joe Jackson is the only other player to finish his career with a batting average over .350.[1] He batted .356 over 13 seasons before he was permanently suspended from organized baseball in 1921 for his role in the Black Sox Scandal.[4] Lefty O'Doul first came to the major leagues as a pitcher, but after developing a sore arm, he converted to an outfielder and won two batting titles.[5] The fifth player on the list, and the last with at least a .345 BA, is Ed Delahanty. Delahanty's career was cut short when he fell into the Niagara Falls and died during the 1903 season.[6]
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.
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.
Professional instructors at Winning Pitchers Academy and Research Center have been marking up our training lanes and mound turf for 12 years with large pieces of side walk chalk. Using this visual training process of instruction is essential to increase students skills and performance. Now Winning Pitchers Academy and Power Drive Performance brings you the best designed training mats ever made for professional training at home, high school, college or pro levels.
Barry Bonds, who set the record for the most home runs in a season than any other player in Major League Baseball history, is often cited as a power hitter. His career was later bogged down by issues regarding performance enhancing drugs. However, he managed a total of 762 home runs while also earning a comparatively high ISO compared to his rivals, with the publication Business Insider labeling him #3 in a list of the greatest power hitters of all time.[2]
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:
Runs Batted In: "The guys that knock em in," as Leyland calls them, do make the big bucks in the baseball market. But RBI are, to a great extent, a function of opportunities. You can’t drive in runs, other than solo home runs, unless there are runners on base to drive in. A typical lineup should be arranged so that the big RBI guys follow guys who frequently get "on base, on base, on base." Leyland happened to be talking about Jhonny Peralta, his 80 RBI, and his value to the team when he launched into his philosophical discussion of on-base percentage. What he didn’t mention was that Peralta led the league in at bats with runners in scoring position the previous two seasons. It should be understood that players who get hits tend to also get hits with runners on base, or in scoring position, at about the same rate, averaged over time.
This is where the magic happens. Players who are able to immediately accelerate the barrel  and in turn get the barrel on plane “early” (in front of the catchers mitt) in the swing will continue to play for a long time. This is the phase of the swing that is barely seen by the naked eye in real time. Phase 1 happens so fast in most big league swing that all most people see is contact and the release, thus making it look “effortless”. In reality there was a lot of effort in the swing, it was just the right kind of effort.
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.

This new formula, which they referred to as gOBP, both credits the batter for reaching on errors and penalizes the batter for sacrifice bunts. They argue first, that any baserunner gives his team a chance to score, regardless how he reached base; second, that the batter can influence whether a batted ball becomes an error*; and third, that if HBPs (which are basically mistakes by the pitcher) are counted as positive events in OBP, then errors (mistakes by the fielders) should as well. To support these arguments, they show that team gOBP correlates better with runs per game (R/G) than the traditional team OBP.
My intention here is not to criticize Leyland, nor is it to promote sabermetrics as a healthy lifestyle for all baseball fans. I’d just like to share the location of a very comfortable place that I’ve found in the world of statistics, that has a pretty good overall viewpoint and doesn’t make me dizzy when folks start speaking in saber. I’d also like to make this a place that even a casual baseball fan, one that is intimidated by "advanced metrics" can get to rather easily, without getting queazy. It’s really not a steep climb.

Last season, the average hitter who belted between 20 and 24 home runs provided 2.9 wins above replacement, similar to what Asdrubal Cabrera (.280 average with 23 home runs and .810 OPS) gave the New York Mets in 2016, for which he was paid $8.25 million. A 40-home run hitter, like Nelson Cruz (.287 average with 43 home runs and a .915 OPS), averaged 4.5 fWAR but was paid $14.25 million. In other words, you could have two Cabrera-type hitters for a little more than it would cost to sign one like Cruz and get slightly more value overall.
On-base percentage plus slugging percentage (OPS): Yes, this is a made-up, smashing together of two useful stats to make a mega-useful stat. Or, somewhat useful stat. I'll say this, since I also had the batting average and slugging percentage entries, I'm a big, big fan of the slash line, it gives you a basic idea of what kind of hitter a player is with three simple stats. Of those three, really, the batting average is the least important, I want to know how much a guy doesn't make an out and how much power he has. OPS tells me that, and despite different ways to get the job done (high on-base, low slugging speedy guy or big slugger who doesn't get on as much) a certain OPS gives me an idea, at least, that no matter what he looks like, he's productive. - Rosecrans

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.


Brian Dozier is another low average players the batting average purists love to hate.  He hit .242, but the rest of his numbers were superior to most players at second.  We complained about his average but nobody took into account that he walked 89 times and scored 112 runs.  If you’re going to count all those extra runs he scored because of the walks you should count the walks as well, and that’s something batting average doesn’t do.  While looking for a comparable player to Dozier, one interesting names came up.  Look at these two batting lines.
On base plus Slugging (OPS): Somewhere, half way between traditional statistics and sabermetrics is what Fox sportscaster Joe Buck called "that new OPS statistic." Yes, he actually said that, during the 2011 World Series broadcast. (Notice that I resist the strong temptation to go off on a rant tangent, here, in an effort to stay on topic.) On base plus slugging, or OPS, is just that. Take a player’s on base percentage and add his slugging percentage, and voila, you get OPS. Now, I think that OPS is a very useful statistic ... for sluggers. But it’s still very much a slugger’s stat. OPS gives one base for walks, two for a single, three for a double, four for a triple, and five for a home run. We’re used to seeing OPS being discussed in conversations now when discussing the MVP awards for each league and it's commonly used in baseball discussions these days.
The longest delays probably involve the nerve cells that make the decision to swing. These decision-making cells receive their input from the eye by way of the brain's visual cortex. It takes at least 43 thousandths of a second for information about the velocity and trajectory of the baseball to be sent from the retina to the higher visual cortex. What happens during the actual "decision" is a neurological mystery — but once the decision is made, a signal is sent to the cerebellum initiating a series of pre-programmed, reflex-like actions (for a practiced batter).
Of course, OPS is not new to those that have been paying any attention at all to the ever evolving world of baseball statistics. In fact, you can go on websites such as Fangraphs and Baseball-Reference and you’ll see OPS+, which adjusts OPS to the league average and adjusts for the ballparks where the players compile their numbers. I suppose we should be thankful that the mainstream media has gotten that far, but we’re just not prepared to leave it at that.

I do not consider loading the body to be a part of the actual swing, because it isn’t. However, there CAN NOT be an efficiently powerful swing without a proper loading sequence. The loading sequence for a any hitter is fundamentally the same but it may change due to the size, strength, and talent level of the individual. Players who are limited in size need to think about a more obvious momentum builder move like Jose Bautista shown below.
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:
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