A little over a month ago, I published this piece detailing the methods that I had created to alternately assess hitter performance. I highly recommend glancing at that article before reading this one; it will make a whole lot more sense. For the lazy, here is a brief primer: I focused on using rates (contact, hard%, etc.) to create rough estimates of what would happen on any given pitch. What is the probability that Mike Trout hits a hard line drive on a pitch in the strike zone? The more a player does that, is he more likely to be a successful hitter overall? One of the advantages of this approach is that it helps to remove the actions of a hitter from his circumstance; a hard line drive is a hard line drive, but the placement of it will greatly affect whether or not the player reaches base. Poor defense, such as one may find in the minor leagues or college ball, is made less important in judging a player.

On of the questions remaining was whether or not I could apply some of these same methods to evaluating pitching. So far, the answer is a qualified yes. We already have a number of metrics to determine pitching value without regard for circumstance, but these methods still provide useful insights. Using the existing methods, such as xFIP, we can determine which rate stats are strong indicators of success.

There is one result that emerged above all else: there is no such thing as a weak-contact pitcher. There is a significant amount of talk about pitchers “keeping the ball in the park” or “getting weak ground balls.” However, this method indicates no such thing. By simply multiplying contact rates with “Soft%” for all 2015 qualified pitchers and therefore creating the “SoftXCont” statistic, I was able to search for any correlation between this rate and xFIP. Judge the results for yourself:

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Clearly, almost no correlation. However, remember that this only examines the aggregate; perhaps some specific pitchers can leverage this so-called skill to great effect. But, it appears that at least on average, generating weak contact is a poor indicator of overall pitching success.

The opposite is absolutely true. Pitchers who allowed less hard contact saw substantial increases in xFIP, as measured by my “HardXCont” number.

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The correlation is relatively strong, especially compared to the correlations seen in other baseball metrics. Clearly there is something going on here; pitchers who allow less hard contact per pitch get better results. Duh. For an even more clean-cut view of this, we can look at GoodXCont, which uses a combination of “Hard” and “Medium” contact.

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That correlation is excellent, and indicates that measuring GoodXCont would be a significantly powerful way of evaluating pitchers.

So, we see that pitchers who limit hard contact and good contact are more successful than their peers. We also see that allowing a large amount of soft contact is not indicative of overall success. The “weak contact” type pitchers (think Rick Porcello) are not necessarily succeeding thanks to any particular ability to generate soft contact; any corresponding ability comes more from being able to allow less hard contact.

For scouts, this means finding pitchers who both limit total contact and allow only poor contact. By using these metrics, rather than the outdated ERA or a radar gun, they can get a strong impression of future big-league success.

In a future piece, I plan to dive deeper into research on “soft contact” pitchers. While these initial results indicate that soft contact is not a good indicator of overall success, there is further work to be done. Stay tuned.

Sophomore year of high school, I was the statistician for the Junior Varsity baseball team. By that, I mean that I was not good enough to play and spent my bench time coming up with new ways to evaluate our players. But, JV baseball is a brave new world in terms of statistical analysis. Sample sizes are too small to properly determine much of anything, and fielding is so shoddy that offensive value is shockingly overestimated. So, I had to create an entire new suite of measurements.

I had a fair amount of data on contact quality, although it was subjectively assessed. But, I was able to cobble together some rate statistics to roughly determine hitting ability.

In doing research on MLB players, I thought that perhaps I could rely on my JV toolbox to identify top prospects. By simply multiplying “hard-hit rate” and “contact rate,” I am able to estimate the probability of a given swing resulting in hard contact. It neglects many factors, of course; for instance, contact in the zone may be more likely to result in a hard hit than contact elsewhere. But, this “hard contact rate” gives a reasonable approximation of the desired probability.

So, how does this statistic perform in evaluating players? Quite well, in fact. Looking at all qualified players in the 2015 season, there is a strong correlation between hard contact rate and wRC+.

So, hard contact rate is a fairly good predictor of overall offensive success. But, is it a repeatable skill? How consistent is it? To answer that question, let us look at the same qualified hitters in the two halves of the season.

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Once again, we see a relatively strong correlation. Although the sample size is not massive, it seems that hard contact rate stays more or less consistent. It is not subject to the constant fluctuations of something like batting average or BABIP. Thus, prospects with strong hard contact rates are likely to maintain that ability. As an indicator of offensive success, this statistic has proven quite strong.

In order to use hard contact rate to identify top prospects, we have to examine how it changes over time. Then, we can use the aging curve to spot those players who are performing better than their age mates. Here is that aging curve, drawn from all qualified hitters between 2011 and 2015.

Looking at players between the ages of 25 and 32, we see a clean curve predicting average hard contact rate over time. We must omit the players on either end of this 25-32 range, since that sample size is too small and characterized by exceptional players. There are not many league-average 21-year-olds, nor are there many under-performing 36-year-olds who still have a job.

But, we can still use the averages for those young players to identify truly exceptional talent. By filtering 2015 data to find players under the age of 23 whose hard contact rate is above average for a 23-year-old, we find the following list:

Harper, Machado, Sano, Correa, Schwarber, Bird, Conforto, Betts, and Odor.

Clearly, the system works to some degree.

I am particularly fond of the Odor pick. While he was a highly regarded prospect prior to his major-league debut, his freshman and sophomore seasons largely disappointed. However, I see a bit of Bryce Harper in him. Like his predecessor, true achievement is likely in his future; as the aging curve shows, hard contact rate peaks later in a player’s career than many other stats. Therefore, he is my pick for breakout candidate over the next few years.

By expanding this research, hard contact rates could be used to identify prospects and breakout candidates. I have yet to examine how the stat predicts success among minor leaguers, for instance.

In a future article, I will examine just that. Also in the pipe is an exploration of contact stats in predicting home runs. Whether or not hard contact rate holds up under further scrutiny remains to be seen.