Archive for BABIP

The infield shift is a much-maligned defensive strategy, hounded as one of the worst analytics-based changes to baseball. Multiple times each season there will be some conversation about banning the shift, and each time pros, ex-players/managers, commentators, and analysts will chip in with their two cents. But for now, the shift is here, and it is as popular (with the fielding teams) as it has ever been. Just under 26% of all pitches were thrown with some form of infield shift in place in 2018, 22% of at-bats had a shift for the entirety of it, and 30% had at least one pitch shifted.

As you can see, left-handed hitters are far more likely to be shifted than their right-handed counterparts, with 46% of left-handed ABs seeing a shifted pitch versus 19% for righties. This makes rudimentary sense as the shifted players for a left-hander are closer to first base, so they have a greater chance of impacting the play to first and therefore stopping a potential single.

I have taken players who have 100-plus at-bats in 2018 both against a shifted and non-shifted infield, then I compared the outcomes. There were 132 such players, and their combined number of at-bats was 72,389 (39% of the seasons total). I have split these up into four categories based on the handedness of the batter and the pitcher.

Read the rest of this entry »

First things first, I would like to credit my friend Elling Hofland for coming up with the main idea of this piece. He’s the one who provided me with his thoughts and theories that allowed me to expand on this topic in the first place. Give him a follow on Twitter for sports and stats-related banter; his handle is @ellinghofland.

BABIP, or batting average on balls in play, is an incredibly useful stat. It does a fantastic job at using both luck and quality of contact to give a better grasp as to how a player actually performs during batted-ball events. These batted-ball events only take up a certain percentage of a player’s plate appearances. BABIP rate focuses on how many plate appearances a player has relative to the number of batted-ball events they have. To calculate BABIP rate, you take at bats minus strikeouts and home runs, plus sacrifice flies, and divide that by plate appearances. For example, if a player has 600 PA during a single season along with a 300 batted-ball events, they have a BABIP rate of .500.

Now, if you look at the three variables taken out of that equation, you’re left with walks, strikeouts, and home runs, otherwise known as the “three true outcomes.” These are called true outcomes due to the fact that none of them (for the most part) involve defense on the field. A shortstop can’t screw up a strikeout, walk, or a home run. You can take these three true outcomes and turn them into a rate as well. If you add up a player’s strikeouts, walks, and home runs and then divide them by plate appearances, you get TTO rate.

Let’s look at Mike Trout. In 2017, Trout’s BABIP currently sits at .369. However, he has a BABIP rate of .550 along with a TTO rate of .435, meaning that 55% of his at bats end with a ball in play, while 43.5% of his plate appearances result in a strikeout, walk, or home run. Both BABIP rate and TTO rate are useful stats, as they essentially show how well and how often a player makes contact. While BABIP itself is useful, it can be hard to tell how luck is involved in a batted-ball event when it isn’t hit over a fence for a homer. BABIP rate attempts to bridge the gap between BABIP and the three true outcomes.

Miguel Sano is a well-known slugger. In his three seasons in the majors, he’s smashed the ball when he’s hit it, boasting exit velocities of 94.0 in 2015, 92.3 in 2016, and 93.1 in 2017. Despite these consistent EVs, his BABIP has fluctuated from 2015 to 2017, with marks of .396, .329, and .385, respectively. If we look at his BABIP rate from 2015-2017, they look like this: .429, .478, and .473. Despite the difference in his BABIP from 2016 to 2017, his BABIP rate has stayed nearly the same, meaning that he’s still making the same amount of contact with the ball despite fewer balls falling for hit in 2016. Looking solely at BABIP, it could be argued that 2016 was his “regression” to where he should be after sporting an incredibly high BABIP in 2015. In 2017, one could say his high BABIP is a cause for concern, as he may just be getting lucky. However, his BABIP rate shows that isn’t the case.

Let’s look at another player, Brandon Phillips. Phillips’ BABIP has been incredibly consistent during his past three years, sitting at .315 in 2015, .312 in 2016, and .305 in 2017. Additionally, his BABIP rates have been .820, .816 and .802. Phillips puts the ball in play nearly 80% of the time on a regular basis.

So, as you can imagine, there is a real link between BABIP rate and TTO rate. The more contact a player makes, less they tend to walk or strikeout. Thus, a high BABIP rate equals a low TTO rate. This is exactly what we see if we attempt to correlate these two stats. Below is a snapshot of a graph that shows TTO rate vs. BABIP rate.

Players names aren’t included because, A) it clutters the graph, and B) they aren’t necessary at this point. Accompanying this graph is a trend line with an R squared value, otherwise known as a correlation coefficient. Essentially, an R squared value measures how well your model fits your data, or in this case, how closely correlated  TTO and BABIP rate are to each other. It turns out that the R-squared value is .991, which means that the relationship between BABIP rate and TTO rate fit very well together: in fact, you’ll find that TTO rate and BABIP rate are almost the exact opposites of each other. The players with the top 10 lowest BABIP rates in the MLB all have TTO rates of .437 or higher, meaning that their at bats result in an outcome of a walk, home run or strikeout 43.7% of the time. Inversely, players with the lowest BABIP rates all have TTO rates of .225 or lower.

We can also derive more information from these numbers using this correlation. Players who have a low BABIP rate have a very high OPS. Remember, these players also have high TTO rates. The top 10 players, Judge, Sano, K. Davis, Souza Jr., Reynolds, Morrison, J. Upton, C. Santana, Lamb, and Stanton all have an OPS of .841 or higher. The players with the highest BABIP rates (or lowest TTO rates) have an OPS of .798 or lower.

BABIP rate can tell us a lot of about a player. Just by glancing at a player’s BABIP rate, you can have an instant idea of how often the player walks, strikes out, or hits dingers. Not only that, but it you can tell you a lot about their offensive production. High TTO rates usually mean high hard-hit rates along with high exit velocities. BABIP rate also helps understand BABIP itself better and teaches that you can’t judge a player by BABIP all the time. In most cases, players with an over-inflated BABIP (relative to past performances), just tend to mash the absolute heck out of the ball, as told by their low BABIP rates and high TTO rates. On the opposite end, players with a steady BABIP will have very high BABIP rates and tend to be contact hitters that put the ball in play and don’t hit for power. BABIP rate, along with its correlation to TTO rate, has the potential to be a powerful, tell all offensive stat.

If you take a look at the leaderboards on FanGraphs for all triple-A pitchers this year, you’ll find a surprising pitcher in the lead in FIP who is above two Rays pitchers, MVP of the Futures Game Brett Honeywell and Yonny Chirinos, along with surprising pitcher Buck Farmer. It’s Andrew Albers, with a 2.58 FIP in triple-A in 77.1 IP, 20 appearances, and 11 starts, with a less impressive 3.61 ERA, along with a sterling 2.77 xFIP.

What’s driving this 2.58 FIP? A strikeout rate of 9.54 per 9, with a measly 1.40 walks per 9 and .58 homers per 9, which is shockingly low, even for him. The home runs will likely increase as he isn’t getting too many ground balls; 46.2% is all right, but not elite. He is also getting a ton of infield pop-ups, with a shockingly high 21.9%. He has had very high infield pop-up numbers in the minors before, which make it easier to do as well as he had, although some negative regression should be expected.

Why his ERA is too high: He generally runs a high BABIP as it has usually been above .330 in the minors since 2015. This year his BABIP is a ridiculous .372 which is inflating his numbers above where can can truly perform at. It should regress to normal levels, maybe even a .320 BABIP perhaps, since minor-league defenses are worse than big-league defenses are (even the A’s pitiful defense).

His strikeout and walk rates are exceeding previous levels; last year in triple-A his walk rate was a good but not great 2.17 per 9, while his strikeout rate was a disappointing 6.08 per 9. I think he’ll likely negatively regress in his K/9 to around 7.5 per 9, walks to around 1.9/9.

But, there’s a chance that Albers could just be a second coming of Jamie Moyer, which could be useful for a big-league team looking for a cheap player to be their fifth starter, since he wouldn’t cost much on a minimum MLB contract or in prospects, and for all intents and purposes is a poor man’s Jason Vargas, who has been surprisingly good this year and is a Comeback Player of the Year candidate. It seems like Albers has made a serious adjustment in performance. Quite an interesting buy-low opportunity for a playoff hopeful that is tight on prospects (Angels, Royals), or tight on cash (Brewers, Rays, Twins, Royals). The Braves should have an extra selling chip that they didn’t know about before. Granted, they might get a lottery-ticket prospect for him, but the Braves are rebuilding, so they need prospects to try out at the big-league level eventually since a lot will flame out. Another pitcher who is similar to Albers is Wade LeBlanc, who I feel should be a starting pitcher for the Pirates, especially considering their rotation issues. But it seems like the thought of him starting is scarier to them than being in a saw trap.

It’s an idea that teams like the ones above should use to get underrated players cheap, while teams that have players like that should sell them for more value than they invested in the player. His best comp is of a right-handed pitcher who is with the Blue Jays: Marco Estrada. They have similar velocities, similar lack of performance till they got older, and get lots of pop-ups. Essentially, he is a left-handed version of Marco Estrada, and Marco Estrada received $26 million over two years after the 2015 season — quite an interesting thought. Especially considering his unimpressive stats in the majors so far. Let’s see if anyone will be willing to give him a chance as a swingman, as he could be an amazing fit on the Nationals; way better than Jacob Turner, and he could start in place of Joe Ross if he performs the way he has so far.

All stats from FanGraphs as of 7-13-2017. I do not own any stats or pages used to help me write this article.

No, you shouldn’t. Well, that was easy. I’ll be moving on to my “Why Haven’t You Bought Jeff Samardzija Yet?” article now.

OK, so it’s not quite that simple and I suppose you want some things like facts, charts, numbers, etc, etc.  You FanGraphs readers are all the same.

Zack Cozart has been a bit of a fantasy darling early this year. Writers have pointed out his 13%+ walk rate to begin the year. His improved .230+ ISO. His .340+ batting average (I hope this is still valid by the time we go live, because it probably won’t be). Because you’re FanGraphs readers, I also know you’ve already looked at his .400 BABIP and processed the fact that he’ll likely regress, but how far? To what level? Will he be 12-team mixed relevant? 10-team? I’d like to take a shot at answering those questions.

First of all, it’s not all bad news with Cozart. As Travis Sawchik would say, Cozart has joined the merry band of fly-ball revolutionaries, as evidenced by his increased fly-ball rate from 2013 to 2016, and he was on my list of possible value picks coming into auction season. His overall value in home-run leagues is capped by his HR/FB%, but I play in quite a few TB leagues so I wanted to keep an eye on him.

I have a tool I like I built in Excel years ago to monitor BABIP-inflated statistics, and to regress the triple slash lines based on expected normalish-BABIP for ROS.

While Cozart is currently sporting a triple slash line of .348/.428/.585, his .394 BABIP says that he should have approximately 10-13 fewer hits than he’s accumulated this far. It’s ~10 hits if you assume a league-average BABIP and ~13 hits if you assume his career .281 BABIP. What this means for you is that Cozart’s talent level right now is only supporting a .251/.331/.528 triple slash, or .274/.354/.541 if you believe he’ll overachieve his career BABIP.

You may be thinking, okay, that’s great, sign me up, but there’s just one more outlier caveat on Cozart’s amazing start to this season. Did you spot it?  He has four triples already! Unless you’re an extremely speedy player, and Cozart is not, triples basically come down to batted-ball or fielding luck. Hit it in just the right spot, or have a fielder take a bad run at a ball, and voila, you’ve got a triple (when you’re not fast).

If we were forecasting Cozart’s triples for the rest of the season, based on his lifetime triples output, he might accumulate three more triples over the course of the final ~125 games, and we should probably have expected him to have only one or two thus far this season. If we correct for this we can adjust his SLG to somewhere between .485-.495, or another way to look at it is via his ISO which I’d forecast to be somewhere around .170-.185.

Overall, if we’re projecting Cozart out over the rest of the season, I think it would be safe to bank on something in the range of .260/.340/.490, which isn’t a bad player and allows for some of his HR/FB% luck to stick in his projection. For those of you playing in OBP leagues, you can monitor the walk rate and perhaps you’ll get some new-found value there this year. With the growth we’ve seen in Cozart’s fly-ball rate, along with his corresponding doubles and home-run output over the past three years, he should safely set career highs in SLG and WAR.

Is pitch velocity an indicator of a good pitcher?

Over this past summer, the Twins struck a deal with the Boston Red Sox to send specialist Fernando Abad to Boston for prospect Pat Light. Light, 25, first pitched in the majors in 2016, where in two innings with the Red Sox, he had allowed 8 runs (7 earned). After the deal, he has spent the rest of the season with the Twinkies. His numbers do not look much better, with an ERA of 10.22 in 12.1 innings pitched. Over his minor-league career, he has posted a 4.35 ERA in five seasons. Why did the Twins want this guy? He was 25, fully established as a reliever, and has only dominated the minors in 2016.

One of my theories is that the Twins saw that Light is a flame-thrower. Recently, he hit 101 miles per hour on a pitch. Are the Twins fixated on his high velocity? Looking at the Twins’ bullpen, another below-average pitcher, Ryan Pressly, is also touted for his high velocity.

I am not saying definitively that the Twins are focusing on pitchers’ velocities to value prospects and players; previously I wrote about how teams have focused on batters’ exit velocities, so perhaps the Twins have tried to apply this mentality toward pitchers.

Either way, I decided to delve into this topic, seeing if a pitcher’s velocity indicates a lower ERA, FIP, and BABIP, or a higher strikeout rate and walk rate. Using MLB’s Statcast, I was able to parse their data to record a pitcher’s average velocity. Using these data, I tried to establish the skill set of a flame-thrower.

To do this, I performed linear regressions between these different factors, seeing if any of these values are highly related to or influenced by faster pitching.

First, I looked at FIP and velocity. Below are the results:

Not a strong relationship, yielding an R-squared of 0.09. This relationship does show that as velocity increases, FIP tends to decrease, but again, not a very convincing relationship.

Next, I looked at ERA and velocity:

It yielded a similar result, a weak negative relationship, if any.

While the results for ERA and FIP were disappointing, I figured BABIP might look better. If a pitcher can throw faster, it would make sense that the batter would have a tougher time making contact, leading to weaker contact and a lower BABIP. Did the results agree? Have a look:

Disappointing. No relationship at all.

On to strikeout rate and walk rate.

I immediately thought of Aroldis Chapman. He has the fastest heater in the league, and his strikeout rate is above 40%, nearing the top of the league. I was much more optimistic for these metrics.

Here is velocity to strikeout rate:

Not a great relationship, yielding an r-squared of .13. It is a little stronger than anything else we have seen, but that is not saying much at all.

Finally, here is velocity and walk rate:

Not much going on here as well.

What does this all mean? Well, for starters, it shows that there are other factors that determine how effective a pitcher is. These data show that these metrics are not the end-all-be-all of a pitcher’s skill. Velocity is not a key indicator of an effective pitcher. Sure, the fastball probably needs to be upward of 85 miles an hour, but speed is not the most important factor. Rather, other skills, such as control, deception, and quality of breaking pitches might be just as important, if not more important, than velocity.

I don’t know if the Twins specifically targeted Light because of his velocity, but in his stint with the Twins, he’s averaged 10.9 walks per 9 innings. What good does his speedy fastball do if he cannot get it over the plate?

After my analysis, I’ll admit I’m a little surprised. I would think a higher velocity would mean a higher strikeout rate. But I am wrong. I guess for every flame-throwing Aroldis Chapman, there is an equally effective Andrew Miller, who does not posses the 105 mile-an-hour heater, but has a higher strikeout rate.

For many Minnesota Twins fans, the recently vintage dominance of the AL Central that spanned seemingly the entirety of the first decade of the 2000s had been taken for granted. I, for one, am guilty of this, and like many fans, am starting realize that winning is not easy, although the Twins made it seem as easy as Torii Hunter made robbing home runs look effortless. Nostalgia aside, the Twins, and their fall toward mediocrity, are an interesting topic to look into. To some, they seemed a similar team to the Oakland Athletics (perhaps aiding in the creation of a post-season rivalry). The Twins, who were not quite as much of a small-market team as Oakland, seemed to develop from within. They had a deep minor system, so deep that when Johan Santana or Torii Hunter deemed it time to cash in, the Twins were able to find a quick replacement and continue their success. Santana, and Hunter, as well as Joe Mauer and Justin Morneau (who have both had their careers altered due to more recent concussions) and many other corner pieces, all made their debut in a Twins uniform and became cornerstones, yet they could never win the big playoff series.

They did not have the ability to flex the financial muscle that the Red Sox, Yankees, and even division rivals Detroit Tigers were capable of; however, they still managed to win the AL Central six out of the 10 years in the previous decade, including a loss in a playoff game to decide the division winner in 2008. The success carried into the Target Field era, represented by a beautiful ballpark that fans spent what seems like an eternity waiting for. After another disappointing playoff loss to the hated Yankees, the Twins entered 2011 looking to improve, with a similar roster and the intrigue of Japanese second baseman, Tsuyoshi Nishioka. That year was filled with injuries, and despite a post-All-Star Game push, the Twins ended the year with the worst record in the American League. Since then, the Twins have failed to reach the playoffs, and are currently battling with the Atlanta Braves for the worst record in baseball. Not to mention, long-time general manager Terry Ryan, the one credited with building the farm system leading to the team’s prior success, was fired on July 18^th. Time to find out where the Twins went wrong.

Those successful Twins teams were always credited for their small-ball and defensive skills. With Joe Mauer behind the plate, Torii Hunter (replaced by Carlos Gomez, who could also flash some leather) and many other solid defenders manning the diamond, a lot of the Twins’ success was credited to this defense.

Yet the Twins were far from a one-dimensional team. The Twins had a solid pitching staff, including, most famously, Johan Santana, who was a two-time Cy Young winner with the club, before being sent off to New York. The Twins also produced one of the most exciting pitching prospects at the time in Francisco Liriano. Liriano’s career was marred by injuries, which led to his inconsistency. Despite Johan’s departure and Liriano’s ineffectiveness, the Twins’ pitching was still an effective unit. The Twins raised their pitchers not on the attractive strikeouts, but on “pitching to contact.” The premise behind this was that pitchers would attack the lower half of the strike zone, induce weak contact, and show excellent control to give up few walks. It seemed to work, as pitchers with low to average strikeout rates were able to be effective pitchers, such as Scott Baker, Nick Blackburn, Kevin Slowey, and Brian Duensing.

Before I delve into my research, I should point to Voros McCracken’s ideas about Defense Independent Pitching for those less sabermetrically inclined (if you are sabermetrically inclined, feel free to skip the next few paragraphs). If I were to give a brief summary of his work, I would say McCracken’s main point is that if a pitcher does not give up a home run or strike out or walk a batter, then he has little control of what happens to the batted ball in play. A lot of what happens can be credited to luck, sequencing, and how good his defense is. For those unaware of sequencing, it is the idea that if a pitcher gave up three singles and a home run in an inning, there are many different possibilities of what could happen. The three singles could come in a row, followed by the dinger, for a total of four runs, or, two singles could come early, the pitcher gets a double play or some other way to get out of the jam, then gives up a home run with the bases empty, followed by another single and an out. In that scenario, only one run was surrendered, despite an equal amount of hits. McCracken suggests there is randomness in this effect, which combined with the quality of defense behind the pitcher and a good deal of luck, can make ERA a poor indicator of a pitchers true skill.

McCracken looked at defense-independent pitching stats (HR, BB, K) and defense-dependent stats (ERA), and noticed that the defense-independent stats correlate much better from year to year, and are a better indicator of how a pitcher will perform, since a pitcher does not have control of what happens to balls in play.

While McCracken did not actually create FIP, his work was a building block for modern pitching analysis. FIP (Fielding Independent Pitching) tracks what a pitcher’s stats would look like if he played behind a league-average defense and experienced league-average luck. It is a much better indicator of future performance than ERA. All the data I used was from 2007-2014. Over that span, for pitchers who pitched more than 100 innings in at least a two-year span, a pitcher’s ERA from one year to the next (tracking how consistent the stat is in tracking performance) had a correlation coefficient of 0.338. FIP, conversely, had a correlation coefficient of 0.476. Clearly, FIP performs better when predicting future performance, as McCracken suggested.

To end my digression on McCracken’s importance, if I had to sum up its importance to this article, it is that pitchers have little or no control over what happens to a ball in play.

When I was talking Twins recently with some recent, justifiably uneasy Twins fans, they attributed the Twins’ recent troubles to injuries and inconsistent pitching. This was when I was reminded of the “pitch to contact” philosophy heralded by the Twins. Since the days of recently past successes, the Twins have changed management, and hopefully have let go of this ideology. Anyways, I thought to myself that McCracken’s work and subsequent furthering of the topic do not go along with the pitch-to-contact philosophy. Sure, if a pitcher can prevent walks and home runs, then it does go along with part of McCracken’s ideas. But, if the goal is to induce weak contact, yet the pitcher does not have control of what happens to a ball when it is contacted, then there is a bit of a discrepancy.

So, like any other statistically-oriented college mind looking for how to spend the rainy days of my summer break, I decided to run some regressions to test if “pitch to contact” actually succeeded and the Twins were able to induce weak contact, or if the relative success of the pitching staff is related to luck and a good defense.

To reiterate, the data I looked at came from the seasons of 2007-2014. To sum up the Twins’ pitching through the period, the period starts with solid pitching from guys who lack the ability to post high strikeout rates, excluding the one season Santana pitched in the study. Guys like Scott Baker and Nick Blackburn had solid seasons early on, but Blackburn and many others faded once things went downhill for the team. From the outside looking in, it may seem like a chicken-or-the-egg scenario, whether it was pitching that caused the downfall or some other factor that caused the pitching to fail.

I gathered data for Twins pitching over this span, and compared it to the rest of the league. The pitch-to-contact philosophy was easily visible, as over this eight-year span, only five Twins pitchers had higher strikeouts per nine innings than league average (Johan Santana, Phil Hughes, Scott Baker, Francsico Liriano, Kevin Slowey). At the same time, only four pitchers had a walks per nine innings above league average (Nick Blackburn, Boof Bonser, Sam Deduno, and Liriano), and most of those seasons came in that pitcher’s last season with the team. The data shows that despite few strikeouts, Twins pitchers found some success in limiting numbers of walks. However, for those pitchers who struggled with control, their combined ERA in those seasons was 4.82, with a FIP of 4.60. Clearly, if a pitcher struggled with control, their success was hindered by the high walk rate.

Much of the Twins’ pitching was inconsistent over this time as well, as pitchers such like Blackburn or Brian Duensing seemingly went from quality starters to below-average pitchers. For the most part, I found this to be a team-wide theme. For pitchers with multiple years with the club, I correlated year-by-year ERA and FIP, to see if any consistent trends arose. Amazingly, there was no correlation from ERA from one year to the next, as the R-squared value was 0.002, stressing no relationship at all (graph). FIP, on the other hand, showed an R-squared value of 0.15; so while not a concrete relationship, a weak relationship exists (graph).

Why this lack of consistent ERA and FIP? This is where I think BABIP comes into play. Since FIP does not take into account BABIP, it did produce more reliable data. A few outliers threw off the data, and since it is not a large sample size, those outliers did affect correlation. By the nature of the relationship, this probably did more to affect the FIP correlation than the ERA, but nonetheless, the small sample size of pitchers from this period did affect the relationship. Interestingly, but perhaps not surprisingly, I performed a regression graphing FIP to ERA, and a solid relationship exists, with an R-squared of 0.36 (graph). This would be even better of a correlation if I took out seasons by Phil Hughes and Liriano, as in those two seasons their FIP was almost a full point lower than their ERA, respectively. This shows the validity of FIP as a metric, as it accurately predicts how a pitcher likely will perform based on independent factors.

Nonetheless, there is a clear difference here in the two pitching metrics. FIP implies a relationship, while ERA does not. How can this be? My theory is that it has to do with the pitch-to-contact philosophy. If pitchers are constantly relying on luck and defense to produce outs, rather than getting batters out themselves, then random variation will play much greater of a role in a pitcher’s effectiveness. Additionally, a team’s defense will play much greater of a role in pitching.

How much can a defense affect pitching? Well, I graphed the total WAR produced by the various Twins defenses against the team ERA from the 2007-2014 seasons. I additionally graphed BABIP against team defense. Amazingly, an ERA to defense regression produces an R-squared of 0.47 (graph), while a Defense to BABIP regression produces a 0.37 R-squared value (graph). Team defense clearly has a relationship with team ERA and team BABIP, as when the Twins defense was in its prime (2007, 2010), pitching performed well. Similarly, in the defense’s worst two seasons, the team also had its highest BABIP (2013, 2014). For those wondering, FIP to team defense produces no correlation (as we expect, since it does not account for a team’s defense) with an R-squared of 0.003.

What does this all mean?

Putting it all together, we notice a few trends. After 2010, the defense took significant steps back, along with pitching (ERA). As we expect, the team’s BABIP was affected by the defense’s regression. FIP, on the other hand, remained fairly constant through the span, showing how the defense must play a role in team ERA. For example, we will look at 2014. This was the defense’s worst year in the span, with a defensive WAR of -46.5. Team ERA was second-worst in this year, at 4.58. FIP, conversely, showed the team had its second-best year in pitching, with a value of 3.97. This shows that if the Twins would have had an average defense, their ERA would have been much lower.

As team ERA ballooned, the quality of the Twins’ defense fell. Since Twins pitchers were taught to rely on their defense through the pitch-to-contact ideology, this relationship was amplified. Pitching to contact, although relying on luck and defense, may have had some merit when the Twins’ defense was in its prime. If the team could get to more balls, produce a few more outs, then as long as the pitchers kept batters from getting on for free via the walk, the team would succeed. The pitcher would not need to strike out as many batters since the defense would make more outs than the normal team. This sounds nice on paper, but as the team defense decayed, the pitching regressed. This is most evident in 2014, as a solid pitching staff was marred by the defense behind them.

If the Twins were to truly focus on pitching to contact, then they should have looked at the defense, not the pitcher. At the same time, pitching to contact is flawed in a way. Why should a pitcher rely on a defense if he can just get the batter out himself? Teaching a pitcher not to use his natural talent to strike out a batter is counter-productive. I am not saying the Twins’ coaching staff directly did this, but when only four pitchers in an eight-year span have above-average strikeout rates, it raises the question. Perhaps the Twins looked for pitchers who were undervalued because of their low strikeout rates, and used these undervalued pitchers in their pitch-to-contact system. Yet, this does not seem to be the case, as the Twins pitchers with the lowest ERAs and FIPs were the pitchers with the highest strikeout rate, excluding Brian Duensing, whose downfall could have been predicted by his 3.82 FIP (to a degree), as it showed is 2.62 ERA would be much closer to 4.00 with an average defense. Even in a pitch-to-contact system, the pitchers with the best ability to get the batter out without putting the ball in play were the best pitchers.

If pitching to contact were to have a textbook year, it would be 2007, where a team with a 4.37 FIP had an ERA of 4.18. Yet, soon after, the defense plummeted, bringing the team pitching down with it. Clearly, through the team’s porous defense, the Twins gave up on pitching to contact, too. They just hadn’t realized it yet.

Hopefully, with the new management in place, pitching to contact is forgotten. While it is also important to keep a viable defense behind the pitcher, I still can’t trust the pitch-to-contact ideology. It had a good run, but seriously, when was the last time the Twins were able to produce a consistent pitcher out of a highly-praised prospect? Liriano wasn’t consistent, Kyle Gibson has yet to dominate, and Jose Berrios has looked shaky is his brief appearances. I think Scott Baker might be the answer to my question, but if not him, then maybe Johan Santana?

Clearly, the Twins need a new philosophy for grooming pitching. It’s a team riddled with questions, and this is not the lone answer, but it can be one step in the right direction for the team currently pegged at the bottom of the AL barrel.

Well, here we are: Over 100 games into the season and Bryce Harper has yet to break out of his slump. When Bryce came to the Majors back in 2012 he was one of the most hyped prospects since Alex Rodriguez broke into the bigs as a 19-year-old shortstop. The pressure, I’m sure, was immense, and through his first three seasons Harper had put up good numbers, but had yet to establish himself as the superstar we all thought he’d be. Something clicked in 2015 though, as he posted an amazing 9.5 WAR, 197 wRC+, and 0.461 wOBA, all best in the MLB by a fair margin. We all thought he’d done it, he had exceeded expectations and was ready to join Mike Trout as one of the most exciting, talented, and productive players in the game. His 1.5 WAR, 180 wRC+, and 0.443 wOBA through April of 2016 merely affirmed this sentiment.

Here we are. 2.8 WAR, 115 wRC+, and 0.346 wOBA. To be fair, these are by no means terrible numbers. He is still creating runs at a decently better rate than the average MLB player, with much of the credit going towards his MLB-leading 18.2 BB% and his 0.214 ISO. His defense has also been very good this year, helping to raise his WAR to 41st in the MLB. No, I am not saying Harper is a bad player, I’m just saying he is worse than the Bryce Harper we saw in 2015. We were all ready to call him a superstar (heck, we even voted him into a starting spot at this year’s All-Star Game), but now he’s taken this step back and we have no choice now but to start questioning his superstar status. Let’s take a look both at what might be causing this slump, and what Bryce could do to bust out of it (if anything at all).

The stat that jumps out at me most is his BABIP. The MLB average is exactly 0.300 this year, and Bryce has a career mark of 0.317. Bryce isn’t too far into his career, and while it’s possible that his 0.369 BABIP last season was an anomaly, it’s certainly safe to say that Bryce is definitively above average in this area. This season his BABIP has dipped down to 0.234, good enough for second to last in the MLB, ahead of only Todd Frazier (0.203). BABIP has a great degree of luck involved, in that some hitters with higher BABIPs might just get lucky (e.g. hit a little bloop into shallow right field that drops for a hit), or might be playing poor defenses (e.g. Jason Heyward would have caught that little bloop, but Jose Bautista was in right field and missed it by a foot). I believe, though, that going from 0.369 in 2015 to 0.234 in 2016 is enough of a differential to at least form the hypothesis that Bryce is struggling beyond just facing better defenses and getting less breaks.

One of the keys to figuring out this drop in production is figuring out what has changed from last year. Obviously his BABIP has declined, but why? For the  most part, pitchers are throwing him the same types of pitches at the same rates, and are throwing pitches in/out of the zone at the same rates as well. He has almost the exact same swing% on pitches outside the zone, but there’s about a 5% decrease in his swing% on pitches in the zone; nothing monumental, but something we ought take note of. The greatest changes that may be observed are in his batted ball numbers, shown here:

We can almost construct a narrative from these numbers: He’s hitting balls soft significantly more often, and he’s also hitting less line drives. Soft ground balls and fly balls are easier to convert into outs, and his infield fly ball% increase implies that he is hitting fly balls with less power. This explains why his home run rate is down. Where he was previously hitting hard line drives and grounders, and turning fly balls into home runs, he is now hitting softer, more easily-fielded grounders and popups, resulting in a steep decline in BABIP.

But this isn’t a cause, it’s a symptom. Again, we are forced to ask why it is that Bryce isn’t hitting balls as hard, and why he’s hitting less line drives? Bryce has been known for having great plate discipline, something that generally hasn’t changed over the last two years. At the surface, we see that he still has a very high walk rate, lays off pitches outside of the zone, and is one of the more patient hitters in baseball. However, one stat that caught my eye was his contact% on pitches outside the zone (and even inside the zone). His O-contact% went from 60.9% to 67.4%, and even his Z-contact% increased from 84.4% to 87.7%. This can be visualized here:

For the 2015 season

And for the 2016 season

There are two ways to look at this: The types of pitches Bryce is seeing, and the counts he’s getting himself into. All of this revolves around where pitchers are throwing pitches, where he’s swinging, and where he’s making contact. As you can clearly see, Bryce has been making a tangibly higher amount of contact this season. Logically, it makes sense to say that he is taking more pitches in the zone, and making weak contact where he used to just swing and miss. But that can’t be the whole story, can it? In attempting to find differences between this season and last, I merely found that regardless of what the count was, Harper was always making more contact; it didn’t matter if he was ahead, behind, or even. He was also making more contact regardless of what pitches were being thrown.

Let’s start with the types of pitches Bryce sees. We’ll split it up into fastballs (which includes 4-seamers, 2-seamers, and cutters), and secondary pitches (curveballs, sliders, and changeups). With secondary pitches, pitchers have begun to come into the zone a bit more than they used to. These charts show where pitchers are throwing Bryce non-fastballs:

2015

2016

It is by no means a huge difference, but it’s still there. Obviously, pitchers are still mostly throwing him non-heaters down and away, they’re just getting them in the zone more frequently. How does Bryce respond to this change? Well, he’s been laying off the low pitch a bit more, and instead has attempted to hit the inside pitch. These are his swing percentages on secondary pitches:

2015

2016

This also means that those non-fastballs are being called as strikes more frequently (assuming that umpires are generally going to call pitches in the zone as strikes). As we can see in his contact% charts, this season Bryce has been making contact at an extremely high rate on those high and inside pitches, and softer pitches have been absolutely no exception. In fact, he’s been making contact with the high and inside non-heaters more than he is with high and inside fastballs. What are the implications of this? Let’s look at his slugging% against secondary pitches:

2015

2016

Slugging% is by no means a perfect measure of a hitter’s ability. Yet, in this case, it gives us a decent idea of which locations a hitter is making solid contact. In his 2015 campaign he was able to get his arms extended and drive curveballs with great power. This season he is attempting to pull the ball more, and it’s resulting in weaker contact. While he is able to drive the inside breaking ball at a pretty decent rate, I suspect that he’s opening up his stance, which can occasionally result in a hard-hit ball, but will often result in a weak fly ball to the opposite field, or a weak grounder to the pull side. The fact that he’s swinging so much more frequently at inside pitches would also be reason to guess that as he’s swinging at breaking balls out over the plate he is still attempting to pull them, as opposed to going with the pitch. Further evidence of this comes from looking at how he hits breaking balls from lefties (curving away from him), versus how he hits them from righties (curving towards him).

2015

He even seems to do better against lefties. Against both of them, however, he clearly is able to see the pitch that will eventually break across the middle/outer half of the plate, and drive it with power. Let’s head over to 2016:

While his production has decreased against both righties and lefties, it is clear that the disparity is much larger when it comes to lefties. This is because Bryce is able to get away with trying to pull the ball against righties, as the ball is curving towards him. This makes pulling the ball a much more natural motion. Against lefties, the only breaking balls he is hitting are the ones that start inside and break right to the inside part of the plate, and the pitches that break to be right down the middle. It is the non-fastballs that are low and on the outer part of the plate that he is unable to drive, especially the ones being thrown by lefties. He’s opening up more, which also explains why his pull% hasn’t gone up (in fact it’s gone down). When he’s open, it’s hard to drive the outside pitch even if you make contact with it intending to hit it to the opposite field. Instead, he’s making that weak contact that results in outs.

Looking solely at secondary pitches, the narrative becomes: Bryce is taking the pitches that are out over the plate, and is instead swinging at pitches that are high and inside. He has a tendency to attempt to open up to the ball, and while he can sometimes get away with it against righties, lefties have been able to essentially shut him down. He is also making much more contact with all of these pitches, meaning that he’s putting more balls in play, yes, but they are weak balls that are easy to field, and are thus resulting in outs. With this mindset, even trying to hit the ball to the opposite field becomes more difficult, and all of this culminates in a lower BABIP.

Next, let’s look at how he’s handling fastballs. One thing that quickly becomes evident is the fact that Harper has been swinging at fastballs a lot less this year, especially ones up in the zone.

2015

2016

His swing% on fastballs in other areas of the zone is roughly the same; it’s really just those high and down-the-middle fastballs that he’s suddenly laying off of more. And yet, just as with non-fastballs, Harper still has been managing to make more contact this year, especially on pitches high and inside, as well as pitches low and out of the zone. How has that translated in terms of his slugging%?

2015

2016

What immediately jumps out at you is the large hole in the top part of the zone this year where Bryce is generating virtually no production. His production on low fastballs is closer to on par with last season, but up in the zone (the same area where he isn’t swinging nearly as often) he can’t get anything going. Why is this? With fastballs it’s a little more simple than with breaking balls in some aspects: For whatever reason he’s laying off fastballs in the zone, and he’s making weak contact with fastballs both high and inside, and down and away (which is where pitchers throw him fastballs most frequently). He’s giving pitchers more opportunities to throw fastballs out of the zone too. The big question mark comes at why he can’t do anything with those high fastballs specifically?

The answer isn’t too straightforward, but I do think that a large part of it is what types of pitches Bryce swings at in which counts. See, there is a very large differential in Bryce’s swing% in counts with no strikes between last year and this year, whereas in two-strike counts his swing% is about the same. He is taking more pitches when he has no strikes against him, especially the high fastball:

2015

2016

He seems to be swinging at less pitches overall, and his focus has shifted from the top of the zone to the inside part of the zone. It should be noted, too, that he is swinging significantly less at breaking pitches with no strikes as well, which highlights something that’s a little less tangible. With fastballs the narrative becomes this: Bryce is taking more fastballs early in the count, which means he isn’t capitalizing on those fastballs. Once he has two strikes on him, it would reason to guess that he would have more trouble making square contact, right? Well, not quite…

Against fastballs in two-strike counts Bryce is actually hitting decently, but he’s still missing the ones across the middle of the plate. One thing I noticed is that, in two-strike counts, he’s getting thrown more breaking pitches than before, and less fastballs. In 2015, 258 out of 719 two-strike pitches were breaking balls (36%). In 2016, the mark has been 189 out of 448 (42%). With two-strike pitches in 2015, 383 out of 719 were fastballs (53%), whereas 2016 has only seen 220 out of 448 (49%). Bryce has become more aware of the outside pitches, both fastballs and breaking balls, and this has something to do with it.

With two strikes, Bryce is swinging at around the same rate in 2016 as he was in 2015. The pitches he is hitting successfully are: High and inside fastballs, away fastballs, away breaking pitches from righties, all breaking pitches in the middle of the plate, and high and inside breaking pitches from lefties. Ok, that’s pretty tedious. Let’s show all of that visually, looking just at 2016:

First, fastballs with two strikes

Again, he’s gearing up for away pitches, and he’s swinging at almost anything, so he has success against away fastballs. We know that he’s been very keen on high and inside pitches of all kinds and in all counts this year, and that is also the easiest pitch to see. He has a reactionary eye for that pitch, and is able to catch up and drive it. High fastballs out over the plate can be somewhat easy to react to but he a) isn’t as keen on hitting them, b) isn’t seeing them that often in two-strike counts anyways, and c) isn’t expecting them. Thus, he’s most likely popping them up, which explains his high increased infield fly ball%. This is supported by the fact that his ground-ball rates on high fastballs with two strikes is quite low:

Next, let’s look at slugging% against breaking pitches from righties with two strikes

Again, it appears that because the ball is curving towards him it’s going to be easier to drive. He is then able to pull the breaking pitches that are up and out over the plate, and is able to drive the low and outside pitches with authority. His lack of success on up and away pitches is a little perplexing, but could be attributed to anything from bad luck, to him possibly not seeing that exact pitch as well, to the fact that the sample size here is pretty small and he hasn’t seen a ton of pitches in that area.

Finally, let’s check out breaking pitches from lefties

Nothing monumental in this aspect, especially as it’s not too different from how he hits breaking pitches against lefties in all counts. Regardless, it still fits our narrative as the up and inside pitches are right in his wheelhouse, and the pitches that breaking out over the plate are easier to hit than any other breaking pitch coming from a lefty.

Whew. That is a lot of heat maps to take in. Let’s review a bit: Bryce has a tendency to take more pitches early in the count, something he hasn’t done before. He’s also opening up, which makes him susceptible to breaking pitches and causing him to make weaker contact. The fact that he’s making more contact on pitches outside of the zone doesn’t help much either. He’s taking more fastballs as well, and once he has two strikes on him he’s seeing less of them, and is most likely expecting them less. He then begins to swing much more frequently, which actually reaps pretty good rewards, though there are some holes in his swing against certain pitches. He can’t get the high fastball, and struggles with breaking pitches against lefties. The result of all of this? Lower BABIP, lower wRC+, lower wOBA, you name it.

Obviously there are factors involved with this that go far beyond what heat maps and stats can show us. Baseball is an incredibly mental game, and once you realize you’re in a slump it can sometimes just drive you deeper into that slump. Statistics also can almost never tell the whole story, and as I mentioned earlier the sample size here is small enough that none of this is much of a predictor for future behavior. There’s a good chance that, on many of the situations mentioned above, Bryce has just gotten unlucky (or heck, maybe even lucky) and thus the heat map doesn’t reveal much. Overall though, when looking at everything in a holistic manner it allows us to construct an idea as to why Bryce is failing where he previously succeeded. We can never know everything for sure, but we know more than we did.

I’ve been hearing for months now that Bryce will be just fine, slumps happen to everyone, he will soon return to form, etc., and I’m not here to disagree with that. Although, I will ask (and I ought add that I am a big supporter of Bryce’s): What if he doesn’t break out of it? Odds are his 2015 will be one of the best seasons of his career, and 2016 (if it continues like this) will be one of his worst, and he will find himself somewhere in between for the rest of his career. It’s just that the deeper he drives himself into this rut the more compelled I am to find the source of problem as best I can, from a purely analytical standpoint.

Love him or hate him, the more that Bryce (and the many young superstars like him) thrives, the more baseball thrives.

(Note: All statistics and heat maps taken from Bryce’s page on FanGraphs.com)

View post on imgur.com

On June 25, this was David Price’s tweet to family, friends and fans.  It was a clear signal that he knew the patience of the Boston fans and media was wearing thin.

Fast forward to the All-Star break and his “Made for TV” stats (those that casual fans know best) are underwhelming: a 9-6 record with a 4.34 ERA, which is worse than the MLB average of 4.23.  It’s not so much his ERA that’s the problem to fans, but more his inability to be consistent from start to start.  Price has three starts of six-plus innings allowing two or fewer runs, but also has four starts of allowing six or more runs.  With the rest of the rotation producing an atrocious 4.86 ERA, the Sox desperately needed Price to be the one to stop the bleeding, something he hasn’t been able to do.  But that doesn’t mean his underlying skills have deteriorated and all of a sudden he’s become a league-average pitcher.  In fact, the advanced metrics say he’s been extremely unlucky and that he’s due for a big second half. 

View post on imgur.com

* Rank is solely being used to establish a baseline for Price as a top 10 pitcher.

In 2014 and 2015 combined, Price was ranked in the top 10 of all pitchers in four of the skill-based statistics: K%, BB%, xFIP and SIERA (the latter two being ERA estimators with a weighting towards more pitcher-controlled outcomes).  Through the 2016 All-Star break, Price has maintained or improved his top-10 rank in K%, xFIP and SIERA but dropped a few spots in walk rate.  Despite the move from 9th to 10th in K% rank, his K rate is actually up from 26.2% to 27.1%.  The reason for the drop in rank is that 2016 newcomers to the list Jose Fernandez, Noah Syndergaard and Drew Pomeranz did not meet the minimum innings qualifier for the 2014/2015 combined list.  On the flip side, Price’s xFIP and SIERA are higher than they were the past two years, but he has improved his ranking versus his peers.  This is because xFIPs and SIERAs are both up 10% league-wide versus last year (due to all the home runs being hit) while Price’s increases are smaller.

So what is happening?  If his base skills are fine, why is his ERA so high and his performance so inconsistent?

View post on imgur.com

So everyone is familiar with ERA and can easily infer that 4.34 is no bueno for a $217-million pitcher.  But there is a reason these stats are labeled “Non Skill-Based” — that’s because these stats are influenced by factors outside of the pitcher’s direct control (defense, luck, sequencing, variance, etc…) and therefore have wide variability over small samples.  Three of these stats (HR/FB%, BABIP and LOB%) explain why David Price is a great rebound candidate for the second half.

HR/FB%

Price’s current HR/FB (home runs per fly ball) rate is 15.2% — which is good for being ranked 76th out of 97 qualified starting pitchers.  The past two years combined he ranked 19th.  To put this in context, Price’s career average is 9.4% while the 2016 league average is 12.9%.  Price has never recorded a full season (>150 IP) HR/FB rate higher than 10.5%.  Also, on balls hit into play against Price this year, 31.3% of them are fly balls, the second-lowest rate of his career.  The only season in which he allowed a lower fly ball rate was in 2012 when he won the AL Cy Young award.  Price is giving up fewer fly balls this year, but of the fly balls he is allowing, they are going over the fence at the highest rate of his career.  Those that remember Price giving up a HR in 10 consecutive starts this year are nodding violently right now.  His HR/FB% will regress towards his career norm (9.4%) and this should be the main reason for a big second half.

BABIP

Price is also suffering from an unsustainable BABIP (batting average on balls in play).  His current mark of .321 is well above his career rate (.289) and even above his highest full-season rate (.306).  Once a ball is put into play it is out of the pitcher’s control what happens from there.  This is why defense and luck influence this stat more than skill.  And with that said, statistical outliers here tend to regress towards career norms.  Even though Price is allowing ground balls at a higher rate than the past two years, his 2016 GB% is still lower than his career average.  BABIP can be influenced by the number of ground balls a pitcher allows, but he’s not allowing vastly more than his career average.  His BABIP should have some positive regression in it, which is another predictor of improved second-half performance.

LOB%

Price’s Left-On-Base% (percentage of runners a pitcher strands over the course of a season) is currently 70.9%, which is also below his career rate (74.7%) and would be his second worst full-season rate (70.0%) if the season ended today.  Similar to HR/FB%, he is ranked 73rd out of 97 qualified starting pitchers.  The past two years he ranked 22nd.  A pitcher with a higher than average strikeout rate should be able to sustain a slightly higher than average LOB%, but it’s playing out the exact opposite way for Price.  This is partly due to his inflated BABIP and HR/FB%; as these statistics continue to regress towards his career norms, the LOB% will creep up to expected levels.

Much has been made of Price’s velocity being down this year compared to any point in his career.  At the start of the season, his velocity was over 2.0 MPH lower than his career average (94.1).  He has since closed this gap almost entirely.  Here is his average fastball velocity by month (with number of starts):

April: 92.0 (5)

May: 92.5 (6)

June: 92.9 (6)

July: 94.0 (2)

If this upward trend in velocity stabilizes somewhere at or above 93.5, then nearly all the performance metrics within his control — velocity, K%, BB%, xFIP and SIERA — will be at or near his career norms.

Let’s dive a little deeper into that early-season velocity issue.  Below are two charts.  The first shows combined performance of 2014 and 2015 for ERA-qualifying starters while the second chart is the same data for the 2016 season through the All-Star break.  The orange circle is David Price.  The red circle (if shown) represents Price’s career average.  The blue circles are a hand selected peer group of the top 10 pitchers in the game (Kershaw, Sale, Arrieta, Scherzer, Bumgarner, Greinke, Strasburg, Syndergaard, Salazar and Fernandez).  Remember those rankings where Price was right around the top 10 — these are the guys usually outperforming him.  The gray circles represent everyone else.  Note: For these first two charts the top-right quadrant is Good, and the bottom-left quadrant is Bad (unless you’re a knuckleballer).

2014-2015 K/9 vs FBv

View post on imgur.com

2016 K/9 vs FBv

View post on imgur.com

The first graph shows David Price clustered where you would expect him — right at the middle-to-bottom of his top-10 peer group, with a healthy average fastball velocity and K/9.  The second graph (2016) shows Price in a similar relationship to his peers, but with slightly lower velocity and a higher K/9.  Note the gap between the orange (Price’s 2016) and red (Price’s career average) dots depicting his improved strikeout numbers this year despite the slightly lower velocity.  This graph also shows what freaks Noah Syndergaard, Jose Fernandez and (to a lesser degree) Jered Weaver are.

The final two graphs show the relationship between ERA and xFIP where xFIP is the more predictive estimator of a pitcher’s skill.  The bottom-left quadrant is Good (think Kershaw) and the upper-right quadrant is Bad (think Buchholz).  Anyone in the upper-left quadrant (Price in 2016) is a candidate for positive regression.

2014-2015 ERA vs xFIP

View post on imgur.com

2016 ERA vs xFIP

View post on imgur.com

The first graph again shows Price in his usual place — at the tail end of the top 10.  In 2014 and 2015 combined he had a very similar ERA (2.88) and xFIP (2.98).  The second graph (2016) shows the disparity between his ERA (4.34) and xFIP (3.16).  Pitchers with this large of a gap between ERA and xFIP are great candidates for regression.  The important takeaway is that his xFIP, relative to his peers, has stayed in that top-10 range.  This supports the point that some bad luck is the main element depressing his ERA.

David Price can easily be the best pitcher in the American League over the next two and a half months.  He already owns the lowest xFIP in the AL at 3.16 — the next-closest is Corey Kluber, at 3.34.  The skills above show he can sustain the xFIP level, but with some change in luck and maintaining his improved velocity, he doesn’t need to “pitch better”; he just needs to keep pitching — and the results will follow.

We’ve come to the end of the 2015 regular season and it’s time to start looking towards the playoffs. As with every year there have been surprises and disappointments. One of the most anticipated events of each season is the debut of rookies and how they will perform throughout the year. Big things were expected from Kris Bryant this year and he definitely did not disappoint. Originally drafted by the Blue Jays in 2010 in the 18th round (546th overall), he was committed to the University of San Diego and the Jays didn’t offer enough to sway him. In 2013, the Cubs drafted him 2nd overall and he did nothing but climb the ranks until he made his MLB debut on April 17, 2015. His first game didn’t go as well as he hoped, going 0-4 with 3 K’s, but debuts mean nothing except for a little extra media hoopla. He cruised the rest of the way through the season on his way to one of the most impressive rookie seasons in recent memory, posting the 3rd highest WAR of any rookie since 2001. Only Mike Trout (10.3 WAR in 2012) and Albert Pujols (7.2 WAR in 2001) posted higher better WARs in their rookie campaigns.

I was looking over Bryant’s stats and his K% really jumped out at me. Although Bryant hit 26 home runs on the year, I began to wonder if there were any comparable seasons. Now the only criteria I used for comparison was: (1) as many or more home runs (26) and (2) equal or greater K%. Only 13 other players met this criteria since 2001 and they are listed in the table below.

Besides an awfully high K%, for which he ranks 23rd overall since 2001, out of all the players on this list, he posted the most impressive WAR. He’s also in some pretty elite company with respect to power hitters. There are four 40+ home run seasons on that list and many 30+ homer seasons. In addition to providing value with his bat, he also provided a positive UZR rating at a highly demanding defensive position. This combination is what made Kris Bryant so attractive to teams since the 2010 draft.

Using the same player list as above, I looked at their seasonal BABIPs, and I found one particular season of interest. Bryant’s 2015 season. Bryant posted a 0.381 BABIP this year, and the next-closest player on the list was Mark Reynold’s 2009 season at 0.338 which is still quite a difference. Looking at Mark Reynold’s seasonal stats from 2008 to 2011, his batting average follows the same pattern as his BABIP.

And a plot showing the relationship between AVG and BABIP (data from 2001 to 2015). There is an increasing relationship between the two, but there is some pretty wide variation. Nonetheless, I’ve highlighted Bryant’s data point from the 2015 season in red and it’s pretty clear that it represents an outlier for his batting average.

If we consider that the players listed in the tables above are from the same pedigree, their career BABIPs average out to around 0.298. Now I’m not saying Kris Bryant is going to follow the same trend, but based on the strikeout rate he posted this year he’s very aggressive at the plate and I know we are going to expect that inflated BABIP to come back down to Earth so I think we can expect some regression next year. As a reference Danny Santana posted a BABIP of 0.405 in 2014 only to drop down to 0.290 this year which saw his WAR plummet from 3.3 to -1.4. I looked at the relationship between HR, SB and a few other stats and batting average showed the highest correlation with BABIP from the stats I looked at. Based on this I expect his batting average will be the most likely to be affected with a downfall of BABIP. I really don’t think the home runs are going to go anywhere, but I think we can likely expect to watch that batting average fall. It remains to be seen how this will affect his peripheral stats, but as long as he continues providing solid defense at the hot corner he is going to provide lots of value on a major-league roster. I’m sorry to say Cubs fans I think you should expect some offensive woes next year.

To a casual baseball fan, Stephen Strasburg’s numbers are not pretty. The owner of a 4.76 ERA and a 1.38 WHIP, Strasburg is clearly having the worst season of his career. But how bad has he been, really? Not as bad as you think. Take a look at these 2015 stats:

Player A: 3.48 xFIP, 22.8 K%, 5.5 BB%
Player B: 3.31 xFIP, 24.1 K%, 5.3 BB%
Player C: 3.18 xFIP, 24.9 K%, 6.0 BB%

Player A is none other than Johny Cueto, recently traded to the Kansas City Royals. 12th in ERA among qualified pitchers, Cueto is widely considered among the best, and perhaps deservedly so with five straight years of a sub-3 ERA. While he has consistently outperformed the above metrics, they are still indicative of general pitcher performance and should not be overlooked when comparing the quality of different pitchers.

Player B actually has the fifth lowest ERA among qualified pitchers and was also traded at the deadline. He’s been one of the most reliable pitchers over the past five years and has been an ace on every staff for which he’s pitched. Player B is David Price.

Player C is obviously Stephen Strasburg, and as you can see, his peripheral stats stack up against the best in the game. In addition to these 2 players, Strasburg also compares positively to others like Sonny Gray and Scott Kazmir, both of whom have better ERAs but a worse xFIP, K%, and BB%.  Strasburg is pitching like an ace, and xFIP shows that, so why have his results been so poor?

Well, first of all, there’s his .345 BABIP. Not only is this high compared to the league average (.296), it’s well above his career mark of .302. Considering he’s not giving up any more line drives or hard contact than usual, his BABIP should fall back to around the .300 mark and bring his ERA down with it.

Not only is his BABIP at an all-time high, his LOB% is at an all-time low. Currently at 65.3%, it figures to inch back up to his career 73.2% mark, or at least to the league average of 72.4%. Considering his strikeouts have not dropped off, there’s no reason for his drop on LOB%, and it can simply be chalked up to bad luck, something that he’s had plenty of this year.

Looking at these stats, there’s nothing that suggests Strasburg is anything but unlucky. However, as Jeff Sullivan pointed out here, Strasburg’s problem could stem from the injury he suffered in the spring. He had apparently adjusted his mechanics to compensate for the discomfort, and even though it appears as though he has fixed this, it’s possible that when pitching from the stretch and in higher leverage situations, he returns to this altered motion by default. When looking at the difference in Strasburg’s stats between pitching from the windup and the stretch, this is what we see:

Evidently, this claim has some ground. Strasburg is clearly having some problems with runners on base, particularly in striking batters out. Before we deal with the strikeout numbers, let’s take a look to make sure that he’s not just getting killed during the at bats that don’t end in strikeouts.

Strasburg is actually generating more ground balls and weaker contact with runners on base. His infield hit percentage is triple what it is when the bases are empty, something that can be attributed to luck. With such weak contact, it’s safe to say this isn’t the problem. So it must be the strikeouts. If we take a look at his whiff rates, the results are intriguing:

OK, so there’s definitely a problem here. With runners on base, he’s only whiffing batters at half the rate he’s done previously in his career, as well as half the rate that he does with the bases empty. So what’s the issue? Well, it’s not his pitch velocity: