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.
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
In terms of detailed analysis, looking at a player's ability as a power hitter often involves using statistics such as someone's 'slugging percentage' (a function that's calculated by evaluating someone's number of moments at bat in relation to the nature of their hits and strikes). 'Isolated Power' (ISO), a measure showing the number of extra bases earned per time at bat that's calculated by subtracting someone's batting average from his slugging percentage, is another statistic used.[2]
```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 – baseball stats!
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To do this drill, you stride over the outside stick, and while completing the swing, work on getting the back foot over the back stick, really focusing on weight transfer. Note how in the pictures, she has stepped past the stick and to the tee, and in the next frame, began shifting her weight from her back leg to the ball. This drill is excellent for weight transfer and using the lower half to drive the ball.
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.
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 an individual level, I'm partial to OPS+ because it's a clear upgrade over traditional measures and, unlike oWAR (which I think is more accurate in a vacuum), it's not quite as off-putting to the uninitiated. I'll happily lean on oWAR when appropriate, though, as it contains a base-running component. On a team level, I tend to stick to runs scored and OPS with on-the-fly adjustments made for ballpark effects.
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Henry Chadwick, an English statistician raised on cricket, was an influential figure in the early history of baseball. In the late 19th century he adapted the concept behind the cricket batting average to devise a similar statistic for baseball. Rather than simply copy cricket's formulation of runs scored divided by outs, he realized that hits divided by at bats would provide a better measure of individual batting ability. This is because while in cricket, scoring runs is almost entirely dependent on one's own batting skill, in baseball it is largely dependent on having other good hitters on one's team. Chadwick noted that hits are independent of teammates' skills, so used this as the basis for the baseball batting average. His reason for using at bats rather than outs is less obvious, but it leads to the intuitive idea of the batting average being a percentage reflecting how often a batter gets on base, whereas in contrary, hits divided by outs is not as simple to interpret in real terms.