The best way I can explain “Hitting for Average” is that this tool is not just solely focused on a person’s batting average. This tool is more about having the ability to have a consistent swing, the ability to keep the bat on-plane for a long period of time, and the ability to square up baseballs on a regular basis. I wrote another article about having the ability to “Repeat Your Best Swing.”
In Phase 2, the hitter may continue to accelerate but hopefully has already reached top speed. They will maintain top speed as they continue to rotate their hips and shoulders. Contact can be made in Phase 2 before Phase 3 is ever needed. This is demonstrated when players like Mike Trout will maintain bent arms well past contact on inside pitches. If Phase 1 and Phase 2 are executed at a high level, theoretically Phase 3 is not needed.
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.
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.
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.
Back in the day when fantasy baseball was in its infancy, the standard 5×5 categories many leagues still use today seemed like a good thing. Well times they are a changing. Stats have evolved over the years, especially with the introduction and advanced use of sabermetrics throughout our real and fake teams. So if things have come this far, then why are we still using the same archaic scoring methods that were instituted by our founding fathers?
When trying to hit for power one of the most important aspects is having a solid bat path. If the swing gets long, you are not going to be able to keep up with fastballs. If the swing gets too short, you are going to pull off and roll over to your pull side. How can we fix this? A simple drill that we use is the interlocking throws drill. The purpose of this drill is to build muscle memory of a good path to the ball and through the zone. Looking at the picture here, she is already loaded with her hands in a good launch position. From here, you are going to want to go through your swing, and release the ball at an upward launch angle. The ball should come out of your hand around where the point of contact would be.
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.
Batting average simply takes hits into account. If we’ve learned any one thing from Moneyball it’s that guys that get on base are important regardless of how they do it. Now I know I’m not going to convince you of anything without some numbers to back things up. Let’s compare players in the top 20 for batting average to the OBP leaders. I’ll exclude players like Andrew McCutchen, Jose Altuve and Miguel Cabrera who appear on both lists.
There’s a very good chance that you’ve heard these phrases at some point, “that was effortless” or “kid’s got easy power”. If you’re unfamiliar with “effortless power” you might not understand what I mean. Simply put it means that a hitter will display great power but visually it doesn’t look like they tried to swing hard. Perhaps the more scientific way to describe and “effortless swing” would be, efficient.
By the time the ball has traveled a dozen feet from the pitcher's mound, the batter has a good visual fix on it. In a thought process much too quick for deliberation, he has decided whether the pitch is a fastball, curveball, slider, knuckleball, screwball, or whatever -- yet a good deal of data has gone into this instantaneous and non-verbal decision.
That's a difference of about one error every two games. This seems insignificant, but we can use Tom Tango's run environment generation program to see what kind of effect those extra errors would have on offense. Plug in the 2013 MLB batting statistics (counting HBP as BB and ROE as hits) and the program estimates a run environment of 4.8 R/G*. But double the amount of errors, and that number jumps by half a run to 5.3 R/G.
We’ve looked at the players with the higher batting averages, now let’s look at some of those players with low averages who were cursed at and ignored in fantasy. We’ll start with last years whipping boy Carlos Santana and his .231 batting average. We all loved his power and RBI numbers, but he dragged our averages down like the Titanic. It might surprise you to know that Santana had a .365 OBP thanks in part to his 113 walks. As a catcher we can tolerate low averages if a player hits for power, but not from someone who plays first (or third). Using OBP though, Santana’s numbers were equal to Morneau in 3 categories and he had 10 more home runs.
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.