Profiling the ideal 2019 hitter

Data driven player development is getting bigger and bigger. Of course data isn’t everything and you still have to actually get to those things but having controllable target ranges is an important thing in all quality control systems, as they don’t guarantee success but they are showing you whether you are on the right track or moving in the wrong direction.

If you control for those variables and make sure that the values are in the correct range early in the minors you are making sure that you don’t have issues like Eric Hosmer where you have a talented hitter with his potential held back by a less than ideal batted ball profile.

So if I was a hitting coordinator of an MLB org I would first make sure I have the correct target ranges for my hitters because there a lot of ideas about what is are good ranges and some of them are even influenced by data and science but the best way to judge that is probably to look what the best hitters in the world actually do.

Now the question is what sample do you use. One option would be to look at the very best guys. Old school youth coaches used to say “that MLB hitter is a physical freak, don’t copy him” but I think that take is very bad, in the last years we have learned a lot of players have the potential to hit 25+ homers for example and this is partially due to the ball but also more hitters using a fly ball geared approach for power. However still it is probably good to look at a larger sample to correct for outliers in the profile.

I have chosen to look at the top33 hitters. There isn’t a super scientific reason for this except those are all above a 130 wRC+ and you create nice thirds from that number to set bottom and top of the range. What I did was looking at the top 33 hitters. I used wOBA though and not wRC+ since that is offered by statcast too and I was too lazy to import it in SQL and write a join, so I just used wOBA even if it has a disadvantage due to not accounting for park factors. Realistically this doesn’t really matter anyway as the correct ranges aren’t a matter of a couple tenths, i.e. it is totally irrelevant whether your GB% is 38% or 38.6%, what matters is that you are about in the right range.

To set the bottom and the top I have looked at the average of the bottom 11 and the top 11, with top and bottom not meaning the best and worst hitters but all values existing in that population sorted from top to bottom. Later I looked also if there is a trend within those top 33 to make a recommendation whether players should strive more for the lower or upper boundary.

Values I have looked at are the more outcome based metrics wOBA, AVG, OBP, SLG, HR and ISO, plate discipline numbers K, BB and K-BB% (could have used contact and o-swing% but didn’t because that isn’t available for every level). And finally I looked at batted ball data: GB%, avg LA, avg EV, max EV, Pull%, Cent%, and Air ball pull%.

So let’s go through most of those values starting with the basic outcome ones.

The top 33 hitters averaged 34 homers. The lower third averaged 26 while the upper third averaged 43. Power is clearly a need to be a top hitter in the modern game. Sorted by wOBA the top guys averaged 38 vs 31 for the bottom third, so more power is clearly a performance factor even within the top guys.

With ISO the average was .267 with a high end of .312 and a low end of .227. Again here we are seeing a performance trend with the top wOBA guys having a .296 ISO vs .244 for the bottom range, not unsurprisingly the better guys have more power again.

With OBP the average is .383 with  a bottom of the range at .361 and a top of .408. Again we clearly see a performance trend with .406 and .366 as the upper and lower ranges sorted by wOBA basically matching the OBP rankings.

Batting average is a controversial topic in baseball. Traditional fans think it is the holy grail while some sabermetrics fans say that it doesn’t matter. It is true that BA is a bad stat for judging hitters and high walk and power low AVG guys are better than many no power .300 hitters. However if you look at the top 33 hitters the average was .296 with the best 11 being at .318 and the worst 11 being at .276. By wOBA you still see some positive effect of BA with the top11 being at .305 vs .289 for the bottom but the effect unsurprisingly isn’t nearly as strong as with OBP which simply is a better indicator. Still only 3 guys in the top 33 were below .270 so if you want to be an elite hitter you better have at least a 55 hit tool (.270) and if you want to be an MVP caliber hitter you better have a 65+ hit tool. Yes it is true that BA alone is a bad measurement but it also isn’t true that .260 hitters with patience and power are dominating baseball like some traditionalists rant because the top guys have power AND hit for average. The hit tool isn’t everything but it still is probably the pivotal tool in player development because OBP and slugging are also hinging on it.

The next controversial subject is K rate. We don’t really see a strong correlation with K rate and performance across MLB because there is a selection bias, i.e. low power guys that strike out are weeded out in the minors. With the top 33 however we don’t see super low Ks but the 19% is clearly below the league average of 23%. Here the top of the range is 13.8% vs. 24.8% for the bottom. Sorted by wOBA here we are seeing a performance trend with the top 11 being at 17% vs 20% for the bottom. Here now we see the correlation because the top33 all have power and when you have power lower Ks is better, it is just not worth to sacrifice power for contact. In this article I stated already that lowering Ks while keeping the power is an untapped reserve for player development

With walks the average of 11% is clearly above the league average of 9%. This is partially due to plate discipline but also fear of the pitchers as the top 33 have both a lower chase rate (29.2% vs 31.6%) and zone rate (40.3% vs 41.6%) than the league average. Here the top range is 14.8% vs 7.7% at the bottom. We are also seeing a performance trend with the top being at 13% and the bottom at 10%, you can be elite without walking a lot (9 guys in the top 33 are sub average) still above average plate discipline is to be preferred especially since it also helps other metrics.

Another stat I like is K-BB%. It is used for pitchers but I like it for hitters too as high walk with low walks hitters are a risk (albeit not always a fail as we saw with Acuna and Tatis jr who both had very bad K-BB rates in the minors). Here the average was 7.9 which is way lower than the league average of around 14%. Here the lower range is 3.4 and the upper range 10.6 and there is again a strong performance effect with the top being at 4.2 vs 10.9 for the bottom. Having an elite (sub 5%) K-BB% is an extremely strong foundation for success in hitting whether it is 20/18 like Trout or 10/7 like Altuve type hitters.

Now we are getting to the more interesting part if you made it this far in the article. Those are values that can’t just be maximized but optimized making it more complicated for player development because it is possible to shoot past the target.

Let’s start with exit velo. Here of course again more is better. The top 33 average 90.3 MPH which is above the MLB average of 88.7. The top 11 are at 92.5 while the bottom are basically league average at 88.5. No performance effect is seen though in wOBA however the only one clearly below average was Jose Altuve at 85.7. For player development this means you need to avoid being clearly below average as guys like Billy Hamilton and Dee Gordon are at the bottom of those boards every year.

Even more interesting for player development is probably peak exit velocity. Here the top 33 average 112.6 MPH with the upper range being at 116 and the lower range at 109. There isn’t an effect on performance within the top 33 but those 110 are a baseline that you should be able to hit if you want to be an elite hitter, only 8 of the 33 weren’t able to hit 110 MPH this year. Even a better indicator would probably be an average of the top 10 hardest hit balls but that isn’t as easily available and like throwing 100 you can’t really fake hitting a ball 110 because the pitch speed only adds about 15 MPH to exit velo vs a stationary ball so the majority of the impact is batspeed and not redirecting pitch velo.

Now we must of course talk about launch angle as this is often called the flyball revolution. For GB rate the average is 39% which is below the league average of around 42. Lower  range here is 33 and upper range 45%. There is some performance impact as the best guys have a 37% GB rate vs 41% for the top. This means you should probably strive for a sub 40% GB rate albeit the very low GB rate guys around 30% tend to have low-ish BABIPs, so a mid to high 30s in most cases is probably better.

With LA the top 33 are again ahead of the league. League average has climbed steadily but average is around 12% still and the 33 are at 14%. Top range here is 18 and bottom 10. Even within the top 33 there is some performance effect of a higher LA with the top being at 15.2 vs 13.4. It seems like indeed the best average LA is around 15 degrees like I estimated a couple years ago

Higher Las above 18 degrees aren’t always ideal either as they can create lower BABIPs. Now Trout has a 22 degree LA this year and he is the best hitter but even his BABIP is down some compared to normally when he is around 15-16 degree LA, so that is probably the sweet spot for most. The biggest thing is to avoid super low Las under 10 degrees. JD Martinez for example was at around 10 degrees last year and raked but it shouldn’t get lower than that. Between 12 and 18 the difference is probably not huge but single digit LAs can limit your potential severely, only 3 guys in the top 33 averaged under  10 (Lemahieu at the bottom at 6.7). Player dev should really look to get average LA above 10 and really above 12 too, but above 19-20 is probably not good either (just 5 where above 19 among the 33). 12-18 is probably a good range here.

The next thing that is currently often discussed is pull rate. Pulling helps power but it hurts BABIP

The 33 here are at 43 which is slightly above the league average of 40.7% so the power benefit probably outweighs the BABIP hit. Here the top range is 47.6% and the bottom at 39%. Within the 33 there is no effect, the top 11 pull slightly less (42.4) than the bottom 11 (43.1). However what we see is that guys with a sub 35% pull rate lack power. Only Lemahieu is an extremely low pull guy under 38% out of the 33 and he is helped by Yankee stadium right field. I would advice to look at prospects not having a pull rate below 38% with the ideal probably being in the low 40s. Above 45% usually isn’t ideal either and leads to low BABIPs and shift susceptibility albeit some still make this work like for example Jose Bautista in his prime.

Last I looked at air ball pull rate because there are some hitters who hit air balls (LD+FB) only oppo and pull only grounders which is related to mechanical issues in the swing and is very bad. Here the 33 average 32% which is above the league average of 29%. High end here is 37 and low end around 27%. Low values below 25% are a concern and if you look at the names you find familiar Names like Lemahieu (who was dead last in MLB in pulled air balls at just 12%) and Hosmer and also guys who had a power collapse like Posey. Not being able to pull the ball in the air is a big red flag both mechanically or just declining batspeed. I would recommend here a range of 30-35% probably and definitely it must be above 25%. Above 40% is probably not ideal either, 18 guys in MLB pulled more than 40% of their FBs last year and only Suarez was in the top 33 in wOBA, quite a few low BABIP guys among those.

Now how you coach that is a different question of course. I wrote two articles if you are interested  in further reading about training and Mechanics  but this isn’t really the topic of this post. Those ranges do give you a baseline that you can work towards. Anyone who wants to develope players should at least occasionally control if their players are developing in the right direction. Especially those batted ball data like Pull%, air ball pull% and GB% are really low hanging fruit that can be optimized not guaranteeing success but at least give you a chance to not waste potential. There are also other data like batted ball spin and of course swing sensor metrics like attack angle, batspeed and vertical barrel angle that should be monitored but unfortunately those data are only available in a training setting and not in games, at least not publically.

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