Archive for Strategy

The Twins Gave Up on Pitching to Contact Before We Did

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 18th. 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.

Buying or Selling Carlos Gomez

What are you to do with a former fantasy superstar who hasn’t lived up to expectations? For some, the answer’s easy; Carlos Gomez has already been dropped in over 25% of leagues on both ESPN and Yahoo.

Now that I’ve driven half my audience away with my use of a semicolon, let’s start the real analysis. Gomez certainly disappointed his owners through the first month and change of the season, sporting a minuscule .486 OPS through May 15 before being placed on the DL. For reference, out of 324 batters with at least 100 plate appearances, just two (2) have a lower OPS as of June 24. Both are on the Braves (one hit fifth in the lineup as recently as June 21, while the other has batted second 13 times this season).

So yes, one could see why owners would have lost patience with Gomez. But this was also a player who hit 66 home runs and stole 111 bases while hitting .277 between 2012 and 2014. If anyone deserved patience, it was him.

So when he hit two home runs in his first six games back from the DL, it was hard to be too surprised. Since then, he’s put together five multi-hit performances, and has brought his season line back up to at least non-Atlanta-ish numbers.

While it’s obviously a small sample size, Gomez’s 76 plate appearances in 19 games since his return have shown immense improvement over his horrendous start to the season. To demonstrate this, take a look at each of the different areas in which he’s bounced back:

Plate Discipline
2012-2014 April 5 – May 15 May 31 – June 24
BB% 6.2% 5.3% 10.5%
K% 22.8% 34.8% 30.3%
BB/K .27 .15 .35
SwStr% 13.9% 19.4% 16.7%
O-Contact% 59.5% 42.4% 45.9%
Z-Contact% 84.4% 74.4% 80.5%
O-Swing% 37.4% 32.1% 35.7%
Z-Swing% 79.3% 79.9% 65.8%

I could bring up more player comparisons and show you just how bad the Atlanta Braves are this year, but that’s not the point of this article. Instead, let’s just focus on Gomez’s numbers and how they compare to earlier in the year and during his prime years. He’s nearly doubled his walk rate while striking out more than 10% less often than before, leading to a BB/K that is no longer painful to look at. He’s missing less frequently on pitches he swings at, both in and out of the zone, and has fewer swings-and-misses as a result. The one worrisome spot here is his swing rates, where the trend is the opposite of what we’d generally expect when we see favorable results. However, his O-Swing% is still lower than it was between 2012 and 2014, and it seems as though swinging less at pitches in the zone is leading to more walks and less bad contact, so it’s not truly a terrible result.

Batting and Power
2012-2014 April 5 – May 15 May 31 – June 24
AVG .277 .182 .294
BABIP .329 .293 .405
OBP .336 .238 .368
SLG .483 .248 .471
ISO .206 .066 .176
OPS .819 .486 .839
wOBA .356 .216 .364
wRC+ 123 28 129
HR/FB% 14.6% 0.0% 33.3%

I already referenced Gomez’s OPS above, but it’s still almost unbelievable to see that his post-injury slugging percentage is nearly as high as his OPS once was. Besides that, there’s improvement across the board. His average is up over 100 points, as his OBP, SLG, ISO, OPS, and wOBA. He’s gone from being 70% worse than the average hitter to 30% better. What’s good to see her is that he’s not outpacing any of his career stats by a noticeable amount — an indication that his current run is very much sustainable. Okay, maybe not the .385 BABIP, but as you’ll see next, keeping it over .300 shouldn’t be an issue.

Batted Ball Breakdown
2012-2014 April 5 – May 15 May 31 – June 24
GB% 39.3% 47.1% 44.2%
FB% 40.6% 35.7% 20.9%
LD% 20.1% 17.1% 34.9%
Pull% 42.7% 36.4% 62.2%
Cent% 33.9% 41.6% 13.3%
Oppo% 23.5% 22.1% 24.4%
Soft% 16.7% 29.9% 31.1%
Med% 48.0% 45.5% 28.9%
Hard% 35.3% 24.7% 40.0%

Let’s take this one at a time. First, Gomez has seen a drastic increase in his line-drive percentage, unfortunately at the expense of hitting fewer fly balls. While it’d be better to see him hit fewer ground balls and get some more balls in the air, he’s certainly making this approach work for him right now. He won’t hit 30 home runs with this approach, but with the increased line drives, he should have no problem continuing to hit for extra bases.

Then comes the confusing part. He’s increased both the percentages of balls he hits to the pull side and opposite of the field, now hitting just 13.3% of his balls to center. He was definitely spraying the ball better beforehand, although the bloated Pull% will undoubtedly help him to put up some better power numbers. If the numbers stay in this region, I’d definitely expect his BABIP to regress, but it’s more likely that they regress closer to his career norms. A lot of those pulled balls will end up going to center field.

Finally, there’s the stuff that’s easy to analyze. Hit the ball harder, get better results. Gomez apparently believes in that approach as well, now hitting the ball hard over a third of the time and showing over a 50% increase from his previous rate. He needs to work on hitting the ball soft less often, which should happen if he continues to be selective and wait for his pitch.

Statcast Data
2015 April 5 – May 15 May 31 – June 24
Exit Velocity (mph) 88.5 84.8 86.4
Exit Velocity on Line Drives and Fly Balls (mph) 92.7 91.2 96.4
Fly Ball Distance (feet) 315.2 309 359

Ah, Statcast. What would we do without your infinite wealth of knowledge? The data here was obtained through Baseball Savant, and confirms that Gomez is indeed hitting the ball harder than he was before his injury. His overall average exit velocity remains low, but his velocity on line drives and fly balls is actually higher than it was last year. He can hit all the slow ground balls he wants and still be successful, provided he can keep up this increased velocity on balls in the air. Of course, he’s not going to continue hitting his fly balls over 350 feet — that’s reserved for people like Byung Ho Park (and apparently Tyler Naquin?). But he’s at 323 feet for the season now, and which should easily suffice for him to begin putting up some rejuvenated power numbers.

If you’re looking for a tl;dr, here it is: Carlos Gomez is performing much better than he was earlier in the season. He’s taking more walks, striking out less, making more contact, and hitting the ball harder and farther (further?). It’s obviously a small sample size, and he may not put up another 20/40 season, but he’s more than capable of hitting 10 home runs and stealing 15 bases the rest of the way. While it’s not elite production, it’d be better than he did last year, which would be quite an achievement after his start to the season.

Lineup Construction is Changing

Lineup construction is a topic that comes up far more often in proportion to how important it is. But if you can save a few runs in a year by using the proper lineup, it’s worth it. Put your OBP up top, not your steals. The #2 hitter should be better than your #3.

With 14 going on 15 years of lineup splits available on FanGraphs, are any trends clear? Yes, actually. In regards to the two specific issues above, managers do seem to be getting better. Let’s explore. (Note: All “league averages” are non-pitchers. Pitchers aren’t real hitters, after all.)

The on-base percentage of leadoff hitters vs. the league average has climbed. In 2002, the league average OBP was .336 whereas it was just .332 for leadoff hitters. Ten years later, in 2012, league average was .324 but leadoff hitters hit .344. The gap has begun to decline since then, but the trend is still apparent, and in 2016 leadoff hitters have a .332 OBP vs. the league’s .324. Overall, here’s a simple chart of the league’s leadoff OBP minus the overall average OBP for each year since 2002:

Not everyone has caught on; either Dusty Baker or Ben Revere really need to figure things out soon for the Nationals, for example. But leadoff hitters are getting better at getting on base.

Meanwhile, managers have a longer way to go in their understanding of the fact that a #3 hitter will most often find themselves batting with the bases empty and two outs which, naturally, is not a good situation for scoring runs. However, just by comparing the wRC+ of the league’s #2 and #3 hitters shows that some teams are learning. In the dark days of 2002, #2 hitters had a wRC+ of 92, compared to 128 for #3 hitters. Since then, #2 hitters haven’t been that bad, but they haven’t been great, either. However, the last three years have been #2 hitters’ most productive since 2002: they had a 102 wRC+ in 2014, 107 in 2015, and currently a 105 in 2016. Teams haven’t moved their best hitters out of the three hole (this will be #3 hitters’ seventh straight year with a wRC+ of 120 or better), but they are starting to see the value of a good #2 hitter. This has led to the wRC+ gap between #2 and #3 hitters to exhibit a clear downward trend since 2002:


Even if you take out that 2002 season, the trend holds. It is still basically due to a change in the past two years, but the more hitters like Andrew McCutchen or Manny Machado, Corey Seager or George Springer bat in that second spot in the order and have success, the more we can expect out of the two hole. A lot of these #2 hitters, you might note, are young guys with a lot of career ahead of them with their current teams. It’s up to managers to keep them at #2 instead of moving them to #3 as these players continue in their careers. They may not, leaving 2015 and 2016 as anomalies so I can be wrong again. (Actually, I’m never wrong, because where’s the fun in that?)

But next time you lament the general failures of managers to put out the correct lineup, remember, things are getting better. Maybe it’s just your favorite team’s manager.

Waiting On an Ace: Jimmy Nelson

I love pitching prospects. Not that I can back this statement up, but I believe pitchers make a more immediate impact on a fantasy roster than hitters. So, each year I stack my “Watch List” with young pitchers that might get called up in September, have a good shot of getting called up in June and potential breakout sleepers. Four years ago, one such player was Jimmy Nelson. How could a man that stands 6-6 at 245 lbs. not be on the radar? I watched with eager anticipation at all those strikeouts. That was four years ago and not much has changed. Both the Brewers and I seem to be in the same boat — waiting on Jimmy Nelson.

At one point, Nelson was the number one prospect in the Brewers’ organization. His fastball and slider were scouted as plus pitches and as such, Nelson was touted as a middle-of-the-order pitcher with potential to move up with the development of a third pitch. He was drafted in the 2nd round, 64th overall and is still just 26 years old. His aforementioned size gives him the frame to tax his arm with 200-plus innings each year. Plainly put, Nelson has the pedigree to be a stud and clearly the Brewers thought so too. Why then are we waiting three years into Nelson’s MLB career?

About 16 months ago, Mike Newman wrote about Nelson’s rising stock. That was prior to a year when Nelson had somewhat of a breakout campaign, going 11-13 with a 4.11 ERA and a 19.7 K%. If you recall he seemed to put things together in July to the point of striking out 32 in 33 IP with a sizzling 1.61 ERA. That’s when everyone jumped on board and expected big things in my fantasy league (10-team mix league, five keepers, deep rosters, 12 years running). July ended, however, and Nelson fizzled with the fading temperatures in 2015. His stock was mixed heading into this year (ADP 211, Yahoo!). It’s a new year now and the temps are starting to rise again. Will Nelson resurface as the potential ace he showed last July?

Last year Jimmy Nelson introduced a curveball to his arsenal, and it was good. The story on Nelson is that he always lacked confidence in his third pitch, the changeup. In the early going Nelson rarely threw that pitch. In order to get lefties out and develop into an ace Nelson needed a third pitch he was not only confident in but that could develop into a plus pitch. Maybe the curve was just what the doctor ordered. His pitch distribution looks like this.

In 2015, Nelson offered his newly-found curve 21% of the time while keeping his plus slider around (17%). 2016 seems to be a different story to this point. Nelson is throwing his fastball much more often and his off-speed pitches less, basically ditching the change all together. This has had two results: hitters are swinging less and making more contact. Z-contact% is creeping up to scary levels (93%).

Worse, so far, hitters are being patient with Nelson. It seems when Nelson goes outside the zone, hitters are laying off.

To summarize, hitters are swinging less at pitches, both inside and outside the zone, and making more contact, both inside and outside the zone, than ever before against Nelson. This is not a good sign. Dating back to Nelson’s early days, he has displayed control issues. What happens when hitters become patient against a pitcher with historic control issues? His walk rate increases.

Jimmy Nelson is progressing in the wrong direction. Hitters have adjusted to his curve and slider, they are being more patient, and they are making more contact. While Nelson’s K% has not dropped dramatically, his BB% is trending in the wrong direction. As a result his K-BB% is at an all-time high (in both the major and minor leagues).

I have something to confess. Prior to researching Jimmy Nelson I attempted to trade him in my fantasy league. To multiple teams. Multiple times. Here were my selling points: Pedigree, development of a third pitch and progression. So far this year Nelson has a 3.46 ERA, a 3-1 record, and he is still striking guys out at 17.9%. On the surface it looks like he is pitching to more contact and inducing weaker contact when he does; his 24.7% soft-contact rate is up from 19.2% last year.

One could be optimistic about this. I am not, however. His ERA is being supported by a .225 BABIP and a crazy 90% strand rate. Worse, pitching to contact is not a good strategy when fly-ball percentage is also trending in the wrong direction; up to 35% from 29% last year.

To wrap this lengthy post up I have several concerns with Jimmy Nelson. He’s always been known for having control issues and it seems he has not improved that yet. He’s developed a third pitch but is refusing to throw his plus slider and curveball more often. He’s inducing more contact but that contact is in the air. I am not searching for a way to “fix” Jimmy Nelson. His velocity seems to be consistent, perhaps just a tick down. His mechanics seem fine. There are no injuries to report. Rather, this post is about waiting on the ace that the Brewers thought they had. If that ace is going to emerge, Nelson is going to have trust in his slider and curve as he did in July of 2015. He’s going to have to find a way to induce more swings outside the zone. As it stands now, he is living dangerously inside the zone and will eventually run into major problems when those stranded runners come around to score as his BABIP rises. As deep as our fantasy league is, he still might be able to be moved. More than likely, however, he’ll remain what he has been — a middle- to back-end-of-the-rotation arm both in fantasy and real baseball.

Does Payroll Matter? (Part I)

Money in baseball has been an infinite source of criticism. In MLB, there is no salary cap as in other major sports, and luxury tax is relatively recent. Media has made us believe that the small fish (e.g. small-market teams) will always be eaten by the big one (e.g. big-market teams). The Kansas City Royals’ performance during the last couple of years, along with the tricky and often misunderstood Moneyball concept, has brought back salary to the newspaper headlines even though it is safe to say the Royals were not even a low-end payroll team. In any case, this post is an attempt to see if popular beliefs regarding money, power and on-field performance pass the numerical test.

There are many interesting questions related to this topic. However I will limit myself to the following during two posts:

  1. Is there a relationship between payroll and wins? If so, how strong is it?
  2. Has this relationship changed over time? If so, where are the peaks? Where are we now?
  3. Will money buy you a ring or a post-season ticket? If so, how much should we spend?
  4. Are there truly big spenders? If so, who are they? Have they changed over the years?

Let me start off by stating what my data sources are, and laying out my assumptions so that we are in the same page. My sources for salaries are Baseball Chronology (1976-2006), Sean Lahman database (2007-2014) and Sportrac (2015). For wins and post-season appearances, my references are MLB and the Sean Lahman database. MLB revenue data is from Forbes.

My assumptions and caveats are the following:

  1. Payroll values are not adjusted for inflation. Time value of money has not been taken into account.
  2. The Houston Astros are considered an American League (AL) team. The Milwaukee Brewers are considered to be a National League team.
  3. 1994 strike-shortened season does not have playoff teams or a World Series champion.
  4. Payroll is considered to be Opening Day payroll. Payroll is assumed to be constant throughout the season for simplicity. Arguably this may not hold true as winning/better teams will likely be buyers at the trade deadline. Losing teams will likely be sellers.
  5. I have not tested for any confounding effect on the variables studied (payroll and wins).

Without further talk, I will get to it.

Question 1: Is there a relationship between payroll and wins? If so, how strong is it?

To answer this question, I found the correlation between yearly payroll and winning percentage for every individual season played from 1976 to 2015. Because payroll values have changed so much in 40 years, I used z-scores or standard scores, which allows us to compare different seasons, regardless of payroll differences.  A payroll number on its own does not mean much and should be compared to the pool of teams on a yearly basis i.e. it is the distribution of payroll in the league that matters. Here’s a link in case you are not familiar with the concept of z-scores; please keep in mind that correlation does not imply causation. Check out the correlation here.

A couple of interesting insights can be drawn from this graph. The first one, quite obvious, is there’s a positive slope there, implying that more money affects wins positively. The second point, though, is that payroll alone does not wholly explain the total number of wins. We inherently knew that. In 40 years, we are able to find teams that satisfied each situation: low-payroll teams that were awful (Houston 2013), low-payroll teams that played over a .600 win percentage (Oakland 2001 and 2002), high-payroll teams that unperformed (Boston 2012) and high-payroll teams that exceeded expectations and went on to win 114 games (NYY 1998). There is a mid-tier team that did extremely well (SEA 2001). These are all outliers, though people can (will?) use every one of these cases to support a preconceived idea e.g. “baseball is a sport and it is attitude and effort that matters,” “money will buy you handshakes at the end of each game,” “big-money teams won’t win because they lack camaraderie,” etc. Therefore, let’s focus on the big picture.

The third point I’d like to highlight is the R-square. The R-square measures how successful the fit line is in explaining the variation of the overall data on a 0-to-1 spectrum. In this case R-square is 0.1905 so it looks like ~19% of the total variation in wins can be explained by the linear relationship between payroll and wins. Also, the slope of the best fit line is 0.0302. This means for a one-unit increment in Z-scores, there is a 0.0303 win-percentage increment. Remember z-score increments are not linear e.g. going from -0.5 to 1.5 requires a different amount than moving from 2 to 3.

However, the potential drivers behind the total number of wins are complex (injuries, roster construction, plain luck, etc.) and the R-square, along with the F-test and P-value, shows that money matters but seems to be overrated. Again, remember that correlation does not imply causation.

Question 2: Has this relationship changed over time? If so, where are the peaks? Where are we now?

We have established that team payroll can predict win percentage with a low confidence level. However, has that always been the case? Was money more important in the 80s than now? The following graph shows the R-square value for every two-year period from 1976 to 2015. It is important to keep in mind that the higher the R-square value, the stronger the relationship between payroll and winning percentage.Check out the R-square of payroll and winning percentage for every 2-year period.

The answer to our question of whether the relationship has changed over time is definitely yes. There are noticeable peaks and valleys. There have been two periods (which I highlighted in green) when money was a better predictor of winning percentage: from 1976 to 1979 and from 1996 to 1999. The first period corresponds to the first four years of free agency. Team owners flooded the league with new money as they went after key players e.g. Mike Schmidt or Reggie Jackson, and payroll increased drastically (60% in 1977, 34% in 1978), as shown below. These have been largely documented (here, here and here). Click here for the payroll growth trend since 1976.

The second period (1996 – 1999) is linked to the Yankees, Orioles (though they dramatically underperformed in 1998), Indians and Braves’ successful expenditure (read: lot of won games) and to the lack of Cinderella stories (perhaps only Houston in 1998 and Cincinnati in 1999). This period was also characterized by, firstly, a league expansion sequel: Tampa Bay and Arizona joined the league in 1998 and, understandably, underperformed. Secondly, MLB revenues year-to-year growth averaged 17% from 1996 to 1999 (not adjusted), so probably teams redirected that surplus to the salary pool. Lastly, in the late 90s, MLB was increasingly becoming a rich-team game. The graph below will show the payroll coefficient of variation for the 1976 – 2015 timeframe. This number, which I will call payroll spread, is simply the standard deviation divided by the mean. This number allows us to quickly assess how spread is the payroll across the league over time. Do you see the trend after ~1985? By 1999, this number had increased continuously for almost 15 years and MLB has had enough. As the power money increased AND the gap widened, MLB commissioned the Blue Ribbon Panel to come up with initiatives to level the field A.K.A. a revenue-sharing program to increase competition. Entertainingly, the correlation of money and winning percentage has decreased steadily but the payroll spread has remained pretty much consistent. I am hesitant to attribute the decline in R-square to the Blue Ribbon Panel or to other factors (read: is this coincidence?). Check out the payroll spread here.

If we go back to the yearly payroll and winning-percentage correlation graphs, you’d notice that I highlighted two periods in red too — from 1982 to 1993 and from 2012 until last season. Those were moments when the correlation of salary power and winning percentage was remarkably low. The first period seems to be closely related to the collusion MLB crisis (check out this link as well). The lowest point was in 1984-1987, when the correlation was only 0.03 and the salary spread was 0.22.

The 2012-onwards period has brought down R-square to a 20-year low (0.06 in 2012-2013). While TV revenue keeps rising, the baseball landscape has changed and new variables are in the mix. There is a redefined revenue-sharing model, we have analytically-inclined organizations, an extended wild-card system and international signings – all these factors have added more complexity to the winning equation, effectively diminishing the relationship between payroll and winning percentage – even with the salary spread still at ~0.40. We are living in interesting times in baseball indeed: If investing money in players doesn’t lead to better on-field results, where do teams need to invest e.g. analytics, managers or front office?

Note: This analysis is also featured in our emerging blog

How the Shift has Changed the Game

The shift is one of the most discussed changes in baseball in many years. It is probably the biggest purely defensive change in decades (right?). Commissioner Manfred has publicly stated that he dislikes it. Players are actively working with hitting coaches to beat the shift. People are asking, how can we beat the shift? And some are starting to deny we can. FanGraphs comments predict that the shift will be bad for baseball, because less offense is less fun.

But just how big is the shift? Just how much has it changed the league?


Okay, “Zero” is too strong. It might have changed something, but if it has we can’t tell.

Okay, that too is too strong, but, the number of obvious statistical correlates of an effective shift, seen in terms of league wide stats, is zero. Maybe we can tell, but if so, it can only be told in some serious data-mining that goes beyond obvious results, like number of outs, even in splits, since teams started shifting. No evidence exists of a change in the league-wide stats you would expect the shift to change. BABIP is unchanged. Grounder BABIP is unchanged. Left-handed batter BABIP is unchanged. In fact, BABIP is higher today than it was 40 years ago, but BABIP inflated about .02 from the 1970s to the 1990s and hasn’t evidently changed since.

The shift is a defensive strategy whose intent is to depress run expectancy on balls in play. The likely effect of the shift, if the strategy works, would be in increasing outs on balls in play. Here is a table of BABIP since 1995, the last 20 years:

Year    BABIP
1995   0.298
1996   0.301
1997   0.301
1998   0.300
1999   0.302
2000   0.300
2001   0.296
2002  0.293
2003   0.294
2004   0.297
2005   0.295
2006   0.301
2007   0.303
2008   0.300
2009   0.299
2010   0.297
2011   0.295
2012   0.297
2013   0.297
2014   0.299
2015   0.299

The apparent trend is obvious, if something can be obviously non-existent.

We can look deeper: how have lefties, whom the shift allegedly affects more, been hurt by the shift? Well, in 2015 lefty hitters had their highest BABIP (.301) versus lefty pitchers in the last 13 years (as long as FanGraphs data goes for that split.) Against right-handed pitchers, left-handed batters tied their second-worst season (.299) in the last 15 years, for a whopping one hit in 500 less than the average during that time (.301).

You see, the problem is that we need to look at grounders: fly balls and line drives aren’t really being affected, but grounders are, so in the long run, the shift is slightly depressing hits. Except the obvious correlate isn’t there either.  In 2015, grounders had a .236 BABIP, .004 higher than the 13-year average.

2015 isn’t some sort of outlier. In every easy-to-research split you might choose, BABIP fluctuations in the last 13 years are within the range of random variation. The recent years of the shift era show not even a statistically insignificant decrease in BABIP: in many of those splits, BABIP has by a hair increased. (See tables linked below.)

Another source of evidence that the shift works might be found by comparing defense-independent pitching models with non-defense-independent stats. Maybe BABIP leaves something out, but we see that runs are down relative to DIPS predictions. If so, one possible explanation is the shift. FIP, a great DIPS, is equal to 3*BB+13*HR-2*K + C, where C is a constant that makes league-average FIP equal league-average ERA. If C is smaller now, that suggest (but does not prove) that BIP outs have changed. C is bigger now (by just .0053, or .048 runs per inning), suggesting that more runs are scored from balls in play. It’s no proof, but if balls in play were a lot more frequently outs, we wouldn’t expect them, overall, to account for more runs and ERA would be down more than peripherals imply.

We can’t infer from this data that some individual hitters are unaffected by the shift. Jeff Sullivan’s recent piece on adjusting to the shift is what brought me to the data (I was seeking to investigate just how badly lefty hitters have been hurt, and discovered something far more interesting), and he mentioned Jimmy Rollins’ attempts to adjust to the shift. I recall a lot of speculation about Mark Teixeira being hurt by the shift. Maybe those guys are. Maybe they aren’t. Maybe they aren’t, but others yet to be named are. Things which don’t have league-wide effect may interact with particular skillsets in hard-to-identify ways.

It’s possible that the shift has changed things by reducing the value of range up the middle, allowing more offensively-oriented players to man those positions. But that seems more like an effect that we would see in future, not one we have seen, because it should take years of player development for those sorts of changes to have a league-wide effect.

It is possible that the shift increases strikeouts and depresses walks. It would be hard to know this, though. It is also possible that the shift has reduced the value of certain defensive skills (e.g., range) and that the decreased need for range has allowed teams to play more offensively-oriented guys up the middle, effectively cancelling the BABIP effects. It sounds farfetched to suppose that two of eight hitters being more offensively-minded can cancel an effect of a shift that should apply to eight of eight of them, but we haven’t ruled it out.

Overall, league scoring is down. But DIPS suggest this is mostly the result of more strikeouts, with a little home-run and walk noise thrown in. There are some ways in which the shift might be having an effect — please offer further hypotheses below. All the evidence here is correlational and correlation doesn’t imply causation. Even anti-correlation doesn’t imply non-causation (if people who drink more exercise more — both are correlated positively with wealth — drinking might get anti-correlated with bad health because exercise compensates for the health impact of drinking). But when no correlation is found and no obvious counter-effects can be sighted, the lack of a correlation suggests weak influence at best.


League BABIP, 1975 to 2015

LHB v. LHP and LHB v. RHP, all available years

Ground Ball BABIP, all available years

How Much Is a “W” Worth in Major League Baseball?


Looking at the current landscape of Major League Baseball, it seems that the Moneyball concept is still alive and well (as exemplified by the Houston Astros and the Pittsburgh Pirates — two rather successful ball clubs in what are traditionally considered to be small markets!

Here in Canada, the Toronto Blue Jays’ recent playoff run in 2015 gave us a reminder of how exciting postseason can be when management, players, and fans all share the same goal and vision. Yet, as thrilling as playoff baseball can be, the true definition of success for a team comes down to it being able to win the last postseason game. Why? All teams that bow out of the playoffs — be it the League Division Series, the League Championship Series, or the World Series, ultimately lose their last postseason game. Only one team — the World Series Champion — ends its season by winning its last game in the calendar year!

Before we get ahead of ourselves about winning the last game in October/November, however, we must be reminded that a team cannot participate in the playoffs — let alone advance — unless it wins its division or a wild-card spot. Even with the newly-expended postseason format that saw both leagues (American and National) having two (as opposed to one) wild cards, it remains a challenge to secure one of the 10 playoff berths. One only needs to see how much obstacles Toronto overcame in the 2015 season, aided by then-GM Alex Anthopoulos’ fury of trade deadline activities (acquiring Troy Tulowitzki, LaTroy Hawkins, David Price, and Ben Revere within a span of four days from July 28th to July 31st) to bring an end to the Blue Jays’ 22-year postseason drought. To this end, the first order of business for a team should be getting into the playoffs.

Toronto Blue Jays Fans

Baseball is once again the talk of the town in Toronto (and even across Canada) after the Toronto Blue Jays ended a 22-year playoff drought by winning the American League East Division in 2015. The trick is can the ball club repeat, if not improve, on their success?

In the simplest form, there are arguably three ways to try to make the postseason. One way is to try to “buy” a championship by signing one or more (if not all) the elite unrestricted free agents on the open market. Of course, this approach requires an ownership that has deep pockets and is willing to spend (sometimes without limitations). Traditional big spenders that come to mind include but are not limited to the New York Yankees, the Boston Red Sox, and the Los Angeles Dodgers. An alternative approach, put on full display by Pat Gillick when he guided Toronto to four American League East Division titles, two American League pennants, and two World Series championships from 1989 to 1993, is to build the core of the 25-man roster through smart drafting and player development and then bolster the lineup, starting rotation, and/or bullpen through trade-deadline deals (including rentals if the cost of prospect capital is within reason). Perhaps the least popular method (at least from the fans’ perspective due to the long-term patience required) — albeit arguably just as effective as the other two means — is to rely on continuous and sustainable home-grown talents strictly, much like the Cleveland Indians (which managed to win an impressive six American League Central Division titles and two American League pennants from 1995 to 2001) and Tampa Bay Rays (which managed to win an American League pennant, two American League East Division titles, and two American League Wild Cards from 2008 to 2013 despite having a very modest payroll).

If money is no object, it would be logical to conclude that most baseball executives would opt for the first route given that it is the shortest avenue to get to the promised land, at least in theory. After all, the Yankees are the owner of 27 World Series championships, by far the most championships of any teams among the four North American major sports, i.e., Major League Baseball, National Baseball Association, National Football League, and National Football League. The greatest strength of “buying” a championship is two-fold. On one hand, by taking an elite talent off the unrestricted free-agent market and/or the trade market, you can prevent your rivals from acquiring that talent, meaning that you are strengthening yourself while simultaneously weakening your opponent. On the other hand, you can afford to “make mistakes” because if the player that you signed and/or traded for did not pan out as anticipated, you can always go out and sign and/or trade for another elite talent as a replacement until you find the right one!

New York Yankees World Series Trophies

Even with notable elite home-grown talents such as Derek Jeter, Andy Pettitte, Jorge Posada, Mariano Rivera, and Bernie Williams, one can argue that the New York Yankees essentially “bought” 4 World Series Titles (1996, 1998, 1999, and 2000) within a span of 5 years by outspending all 29 other teams in Major League Baseball.

Yet, there is no guarantee that being a big spender would necessarily get you a championship. In the 2015 season, the eight ball clubs with the highest payrolls — and I purposely limited the scope of my coverage to eight teams because there are only eight “true” playoff spots — as of the 2015 season are as follow: (1) Los Angeles Dodgers at $ 301,735,080; (2) New York Yankees at $221,256,867; (3) Boston Red Sox at $214,789,749; (4) San Francisco Giants at $187,088,630; (5) Washington Nationals at $165,655,095; (6) Detroit Tigers at $162,218,297; (7) Texas Rangers at $152,445,607, and (8) Los Angeles Angels at $151,348,162. As we can observe, among the eight teams with highest payrolls, all of which have a payroll in excess of $150,000,000, only three (3/8 = 37.5%) of the ball clubs — the Dodgers, the Yankees, and Rangers — made the cut! In other words, even if you spend money without reservation, it does not necessarily mean that success is guaranteed! In fact, based on this small sample, there is a (5/8 = 62.5%) chance that your team will be watching (as opposed to playing) postseason baseball even if your ball club has one of the highest payrolls in all of Major League Baseball.

Table 1: Teams with Highest Payroll in Major League Baseball: 2015 Season

Source of Data:

Conversely, having a modest or low payroll does not necessarily mean that your team is completely out of running for the grand prize. Even though the odds may stack against you, at least from the surface, recent history suggests that the probability of a low-budget ball club making it to the playoffs is actually not terrible. Below are the eight teams with the lowest payrolls — again, I deliberately limited the range of my coverage to eight ballclubs because there are only eight real playoff spots — in the 2015 season: (1) Miami Marlins at $63,590,525; (2) Tampa Bay Rays at $73,582,652; (3) Arizona Diamondbacks at $76,639,242; (4) Cleveland Indians at $77,404,413; (5) Oakland Athletics at $80,376,830; (6) Houston Astros at $81,450,835; (7) Milwaukee Brewers at $94,010,873; and (8) Pittsburgh Pirates at $99,435,606. As we can decipher, among the eight teams with lowest payrolls, all of which have a payroll south of $100,000,000, there are actually two (2/8 = 25%) ballclubs that managed to secure playoff berths. Indeed, the difference between the number of the “rich” teams from among the eight ballclubs with the highest payroll that made the postseason — three in total — and the number of “poor” teams from among the eight ballclubs with the lowest payroll that made the playoffs — two in total — is only one team.

Hence, in statistical terms, there is not a massive gap in the chances of making the postseason between being one of the “rich” teams from among the eight ballclubs with the highest payroll (37.5%) and being one of the “poor” teams from among the eight ballclubs with the lowest payroll (25%) as the difference is only a mere (3/8 – 2/8 = 1/8 or 12.5%). As a matter of fact, if we were to take the average payroll of the eight teams with the highest payroll [($301,735,080 + $221,256,867 + $214,789,749 + $187,088,630 + $165,655,095 + $162,218,297 + $152,445,607 + $151,348,162)/8 = $194,567,186] and subtract the average payroll of the eight teams with the lowest payroll [($63,590,525 + $73,582,652 + $76,639,242 + $77,404,413 + $80,376,830 + $81,450,835 + $94,010,873 + $99,435,606)/8 = $80,811,372], which yields ($194,567,186 – $80,811,372 = $113,755,814), and then divide this difference by 12.5, i.e., the chances of making the postseason between being one of the “rich” teams from among the eight ballclubs with the highest payroll and being one of the “poor” teams from among the eight ballclubs with the lowest payroll, we can deduce that for every additional one percent (1%) in which a team wants to augment its odds of making the playoffs, it would cost that ballclub just less than 10 million dollars ($9,100,465.11). While the math suggest that you are inching closer to the promised land (at a rather slow pace of one percent) for each additional nine million ($9,100,465.11 strictly speaking) that you are dishing out, I am not so sure that the trade-off makes sense from a value (or cost-benefit) perspective unless money is no object whatsoever.

Table 2: Teams with Lowest Payroll in Major League Baseball: 2015 Season

Source of Data:

If spending money blindly is not the way to go, then it seems logical that the second or third approach (perhaps even a combination of the two) is the preferred option. Recent trends in the baseball industry seem to back this rational strategy as more and more teams are demanding “value” for their investments, meaning that they want to get the most bang for their bucks. Below are the eight teams with the lowest average cost per win in Major League Baseball for the 2015 season, as calculated and ranked by dividing the total payroll of all 30 teams by the number of wins (“W”) they have in the 2015 season: (1) Miami Marlins at $895,641.20 per “W;” (2) Tampa Bay Rays at $919,783.15 per “W;” (3) Houston Astros at $947,102.73 per “W;” (4) Cleveland Indians at $955,610.04 per “W;” (5) Arizona Diamondbacks at $970,116.99 per “W;” (6) Pittsburgh Pirates at $1,014,649.04 per “W;” (7) Oakland Athletics at $1,182,012.21 per “W;” and Minnesota Twins at $1,282,311.06 per “W.”

Among the eight teams with the lowest average cost per win in Major League Baseball for the 2015 season, there are once again two (2/8 = 25%) ballclubs that managed to secure playoff berths. This means that the probability of teams that emphasize values for their spending making it to the postseason is the same as that of ballclubs with lowest payroll in Major League Baseball for the 2015 season. Better yet, the chances of teams that emphasize values for their spending and ballclubs with lowest payroll in Major League Baseball for the 2015 season making it to the playoffs are only slightly worse than teams with highest payroll in Major League Baseball for the 2015 season (3/8 – 2/8 = 1/8 or 12.5%).

Table 3: Teams with Lowest Average Cost Per Win in Major League Baseball: 2015 Season

Source of Payroll Data:
Source of 2015 MLB standing:

All things taken into account, I would opt for smart drafting and player development rather going for the shortcut of “buying” a championship if I were a GM, unless my budget is a bottomless pit. Bottom line, not only is there no absolute certainty that having one of the eight highest payrolls would mean a ticket to the playoffs, but as we have witnessed, the odds of making it to the postseason are not really that different for the eight teams with the lowest payrolls and for the eight teams with the lowest average cost per win in Major League Baseball for the 2015 season. Coupled with the unattractive fact that it would cost me nearly 10 million dollars to increase my team’s chance of making the playoffs by a mere one additional percent (and each percent thereafter), it seems obvious that smart drafting and player development is by far the most optimal plan.

Top Five Incoming Impact Prospects: NL Central

The NL Central was one of the most talked about divisions in the back half of last season. The Cardinals, Pirates, and Cubs surged forward to control the three best records in baseball. For the Cubs, eventual rookie of the year Kris Bryant helped his team grab the second wild card spot while taking the league by storm. And the merchandise industry. With 23 of the top 100 prospects being held by the NL Central heading into next year and many of those players with a 2016 ETA, it is only fitting to look at who might be the next Kris Bryant. Who will be called up in the next couple years and make an immediate impact that captivates the league?

With the Brewers and Reds in the midst of rebuilding, it is fair to say that although prospects like the Brewers’ shortstop Orlando Arcia (#6 prospect) and Reds outfielder Jesse Winker (#34 prospect) will likely have their shots in the Show, they will probably not have as big of an effect on the pennant race next season. For that reason, I did not include either team’s prospects despite them both having five top-100 prospects each. Fortunately, the Cardinals, Pirates, and Cubs all also have prospects knocking at the door who have the potential to impact the race for the NL central.

Willson Contreras (age 23) – C, Bats: R/Throws: R, Cubs (#1 C prospect, #50 overall prospect)

In Contreras, the Cubs have another young bat. With a smaller catchers fame of 6’1″ and 175 pounds, he led the Double-A Southern League in average (.333) as well as XBH (46). He also posted a strong wRC+ of 156. He began his 2015 campaign splitting time with Schwarber behind the plate in the minor leagues, but was seen as more likely to stay as a catcher with his above average arm. This allowed his former teammate to be called up as a left fielder while he continued developing his game in Double-A. He has the potential to be above average defensively if he can reach higher levels of consistency in his foot work, as noted by Dan Farnsworth at FanGraphs. His biggest step last year was improving his plate discipline and strength. Contreras ended the season with a walk rate of 10.9% ,higher than his previous year of 8.8 in A+, while cutting his strikeout rate down 8.9% to 11.9% in the process. He profiles as an athletic, contact hitting catcher who will provide many more doubles than homers. With more refinement, he could soon draw comparisons to Jonathan Lucroy.

The near future for Contreras is uncertain. He will more than likely stay in the minors next year, most if not all of it in Triple-A, to develop further due to the durable Miguel Montero and veteran David Ross holding down the backstop for the Cubs. This is not to mention Kyle Schwarber, who could very well still have a future as a catcher (there have been rumors of him being the personal catcher for Kyle Hendricks in 2016). However, the contracts for Montero and Ross are up in 2017 and 2016, respectively. With Montero showing signs of decline, Ross closing in on retirement, and Schwarber’s uncertainty as a long-term catching option, Contreras will soon have a window of opportunity to establish himself as the everyday catcher for the Cubs. The question is if it will be next year or the year after.


Tyler Glasnow (age 22) – RHP, Pirates (#2 RHP prospect, #10 overall prospect)

Outside of the 1-2 punch of Gerrit Cole and Francisco Liriano, the rest of the Pirates 2016 starting pitching does not look promising. Last year, the projected 2016 Pirates 3-5 starters Jeff Locke, Jon Niese,and Ryan Vogelsong had a FIP of 3.95, 4.41, and 4.53 respectively, all noticeably higher than the 2015 league average among qualified candidates (3.71). The Pirates farm system will be looking to fix this sooner rather than later in the form of two young pitchers: Glasnow and Jameson Taillon. For now, let’s focus on Glasnow. With his mammoth 6’8″ frame comes a high quality arsenal. His fastball and curveball both grade as plus or better pitches with an average changeup to compliment them. The issue with Glasnow is his command. In 41 IP in Triple-A during the second half of the season, Glasnow had a disturbingly high BB/9 of 4.83 (although his K/9 of 10.54 is also something to highlight). The problem stems from his mechanics, as his lanky body can sometimes make his pitching motion too long. An issue, but a fixable one. He draws comparisons to Tommy Hanson and, with projected improvements in his walk rates, looks to be on the verge to take his turn in the League.

It is more than likely that Pirates fans will get to see Glasnow get his turn this year. During the epic NL Central race last year, Pirates fans pleaded for Glasnow to be called up, but the Pirates decided to keep him in Triple-A to continue developing. A shaky back half of the starting rotation that also has questions of durability should allow the highly touted prospect to make his debut sometime this season. The timetable of this debut, however, is uncertain. GM of the Pirates Neal Huntington was quoted as saying that Glasnow and Taillon, the next prospect to be talked about, will appear in the second half of the season if not sooner.


Jameson Taillon (age 24) – RHP, Pirates (#54 overall prospect)

The former second overall draft pick has certainly has had a mountain to climb to regain his status as a top prospect. He was close to reaching the MLB until injuries set in. Following his 2014 Tommy John surgery, he missed last year as well after surgery to repair an inguinal hernia. With almost 30 months of not pitching in-game, he is now going through the normal pitching progression in spring training. Taillon features the same pitching arsenal as Glasnow, but with slightly less explosive stuff and better command. In 110 IP in Double-A in 2013, he posted a 8.7 K/9 and a mere 2.9 BB/9. These are strong numbers, but old ones. Regardless, Taillon is still projected to be a top of the rotation starter if he can stay healthy and show that his recovery is complete.

Depending on how well Taillon does in spring training and the beginning of the minor league season, he could be the first of these five prospects to make his 2016 MLB appearance. With the issues previously noted about the Pirates rotation, he has a big chance at seeing a good amount of innings at the major league level next year. If Taillon shows that he can pick up where he left off in 2013, he will be a strong presence in the Pirates rotation.


Alex Reyes (age 21) – RHP, Cardinals (#3 RHP prospect, #13 overall prospect)

Reyes is, in my opinion, the most dangerous man on this list. He is a young pitcher with explosive stuff in an organization that thrives in developing and refining young pitchers. And although I hate to admit it being a Reds fan, they have one of the better catchers in the game in Yadier Molina, who has been praised for working well with his staff. His fastball is his best pitch, hovering in the mid-90s, but has been clocked reaching triple digits (with spotty command) when he rears back. He also features a powerful curveball that he can use to throw for a strike as well as to get batters to chase. These two pitches are well complimented by his changeup, which although is just average, he knows how to use to make his other two pitches better. Reyes has been known to overthrow and lose command, but has the potential to settle as he is still only 21. He was handed down a 50-game suspension last season because of marijuana use that he will continue to serve at the start of next season. Before the suspension, he posted a 13.77 K/9 in 34.2 IP in Double-A after having a 13.71 K/9 in 63.2 IP of A+ ball. Yes, you read those numbers right. Oh, yeah, and he only gave up one home run all of last season.

Reyes knows how to pitch and, if he shows more development in his command in the minors next year, has a good chance at making his MLB debut. He may have even had a shot at making the Cardinals team out of spring training if he did not have to start the 2016 year under suspension. The Cardinals have a solid starting rotation that held up as one of the best last year, and one that added a good pitcher in Mike Leake, so there is no immediate rush for Reyes. However, do not be surprised if a mid-season call up of Reyes takes the league by storm in either the back end of the bullpen or even in the starting rotation itself.


Josh Bell (age 23) – 1B/OF, Bats: S/Throws: R, Pirates (#2 1B prospect, #49 overall prospect)

Bell was taken as a corner outfielder out of high school but, with the Pirates loaded outfield and Bell’s below average defensive capabilities, he was moved moved to the gaping hole in the Pirates organization: first base. At 6’2″ 235 pounds, most expected him to thump the ball. To this point the switch-hitter has failed to show he can produce more than average power. This is due to his swing, in which his bulky lower half is not fully utilized. His strong suits are hitting for contact and good understanding of the strike zone. Last year he posted 130 wRC+ with a solid 0.88 BB/K ratio through 426 PA in Double-A, only to one-up those numbers with a ridiculous 174 wRC+ and 1.40 BB/K ratio through 145 PA in Triple-A. Though in all 571 combined PA, he managed just 40 XBH. It is unlikely he will develop more pop which means the continued success of his contact hitting skills and development of defense at first are all the more important to watch.

Since the Pirates do not have a solid option at first base, the unspectacular Michael Morse and John Jaso will more than likely give way to Josh Bell sometime next season. He will, however, start in the minor leagues and be given some extra time to develop his defensive work before being called up. It is plausible to see Bell being plugged into the Pirates late season lineup to provide a team with a questionable pitching rotation (that may or may not have Glasnow or Taillon in it) a boost in offensive production.


2015 showed that former rebuilding teams could quickly emerge to be competitive by stacking their farm systems and having their young, talented players surge through the minor leagues. For the NL Central in 2016, I can see this trend continuing. With FanGraphs projecting the NL Central to have the Cardinals and Pirates chasing the Cubs for a playoff birth, prospects for these teams could mean the difference down the stretch between being a buyer and a seller, and getting a pennant or wild card birth. There’s a lot to be excited next season for these young players. With spring training games under way as I write this post, the wait is almost over.

The Sea Breeze Might Be Suppressing Homers at Petco Park

Land and water tend to do two different things when it comes to heat – the land retains it, while water repels it. The land’s retention of heat gives way by the afternoon, causing the rising heat to create a vacuum, which sucks in cooler air sitting on the surface of the ocean. Cool air rushes into the coasts by mid to late afternoon.

Petco Park is less than one mile from the Pacific Ocean, making it susceptible to these afternoon sea-breeze gusts, which tend to pick up in the spring time and fade in the summer. Fortunately, the ballpark is situated east of Coronado Island [1], which helps to buffer the would-be stronger sea breezes that might affect fly balls. The spring time gusts, the Coronado Island buffer, and the “effect” on fly balls are all hearsay. We’ll look closer at each of these, starting with the sea breezes at the ballpark.

The Wind Matters

Let’s take a closer look at how the wind affects fly balls at Petco Park. Not that the common word of the good people of San Diego can’t be trusted; it’s just a matter of science. Below is a graph of every home run hit at Petco Park over the last two years and the approximate wind speed while the home run was hit. It seems like there’s no correlation between wind speed and distance of home runs.

However, not all wind is created equal, so the directional changes of the wind might have some influence on the flight of the ball. In the 2014 and 2015 seasons, the directional path of the wind for 261 home runs was registered (the wind was either “calm”, “variable”, or “NNE” which registered in only one case).

Most home runs were hit while the wind was blowing in the west-northwesterly (WNW) direction. Given that center field is due north of home plate that would mean that a majority of wind is probably blowing over the Western Metal Supply Co. brick building. My guess (I’m not a meteorologist) is that the wind is drawn in from the ocean, over the top of Coronado Island. Here’s a bird’s eye view of Petco; the arrow indicates where the wind is coming from – it’s the WNW direction from home plate.

So, this begs the question: How does WNW wind affect the distance of home runs? If we only look at the 101 home runs hit while the wind was blowing from the WNW direction, we begin to see something going on (r = – .21, p = .04. For every 1.53 mph faster the wind blows from the WNW direction, 1 foot is lost from every home run hit (R2 = .04, p = .04, n = 101)

No other individual direction of wind registered a significant influence of the distance of home runs hit, nor did the combination of every other wind direction have any effect. So much for the Coronado Island buffer.

It’s a decent speculation that the direction in which a home run was hit (left, right, center) might be more or less affected by the WNW wind. However, the direction that the home run was hit had no effect on the relationship of the distance of the home run, with respect to the speed of the wind. Exit velocity (the speed of the ball off the hitter’s bat) is an obvious predictor of home run distance. Exit velocity did show the weakest correlation with home run distance when hit in the WNW direction as compared to every other direction [2]. It’s likely that lower exit velocity means that the home run hit spent more time spent in flight, and was thus more susceptible to WNW winds that suppressed its total distance, regardless of the direction that it was hit.

Addressing the hearsay

Wind direction and wind speed were recorded ten minutes before every hour of every home game for the last two seasons [3,4]. No surprise, WNW winds dominate during the course of every home game.

Wind speed does seem to be higher in the afternoon a compared to the evening, peaking in the late afternoon.

Additionally, May tends to have the strongest winds, but July and August have produced stronger winds than April. The theory that the spring is windier than the summer isn’t entirely true, but the spring does contain the windiest month of the regular season (May).

Why does this research matter?

Obviously, the pitcher and the batter are going to matter most. But, the WNW wind explains about 4% – 5% of the reason why the home run ended up where it did (R2 = .044). If you’re the Padres and you play 81 home games a year 4% – 5% might mean something to you [5].

Here’s a crazy idea: let’s say you’re the Padres and you’re playing an afternoon (3pm – 5pm) game and the winds are blowing in from the WNW (there are at least 22 home games this 2016 season that will be played between 3pm and 5pm). If it’s early in the game, start Carlos Villanueva, who has a career 40.4% FB%, and if it’s later in the game, use Jon Edwards who had a 67.6% FB% in 52 innings between AAA and majors last season. Meanwhile, give Matt Kemp a break (who has a career 36% FB%) and platoon rookie Travis Jankowski who showed a 27% FB% in 34 games last year with the Padres.


Why did I only choose the last two years? Wind patterns and sea breezes can change over time [6]. If we rewind the years, we may or may not see similar results. I felt that the last two years were a decent idea about what we could expect from 2016, any further back, and I might have run into a different profile. Don’t agree with these results? Add a few years, and let’s see if the trend holds — I’m all for more objectivity.

Yes, sea breezes could entail the “marine layer” which brings a body of cool and moist air into the ballpark, and I might take a look at that with my next article. However, it’s not the moisture that will suppress home runs — it’s the cool air. Warm air expands and lowers the air density, which results in less resistance on the baseball. Therefore the cooler the air is, the higher the density. Water (H2O) is less dense than atmospheric O2 and N2, therefore if there’s more moisture in the air, we’d see less resistance on the baseball [1]. Temperature, dew point, humidity, and pressure had no effect on the distance of home runs between 2014 and 2015.


[2] Of the 4 directions that reported significant effects: North Northwest (r = .674, p < .01, n = 16), Northwest (r = .473, p < .01, n = 45), West Northwest (r = .393, p < .01, n = 101), West (r = .591, p < .01, n = 36)



[5] Quality of batter and/or pitcher was not tested in a multiple regression model, nor were any other predictor variables beyond wind speed. 

[6] See Coors Field effect:

The Pirates and the Groundball

The Pirates are no stranger to losing. In fact, they went 20 straight years without making the playoffs before 2013. That is painful. Before 2013, the last time they had made the playoffs was when Ronald Reagan was in office. However, they are becoming familiar with a new friend to end this pain of losing: the ground ball. Almost a year ago, Travis Sawchik wrote an intriguing book entitled Big Data Baseball which shed light on the ground ball as well as defensive shifting. So the fact that they lead all of baseball in GB% over the last three years came as no surprise. The surprise came when I looked at how much they lead by. The Pirates are, as a staff, leading the second-place team in GB% by almost 3% over the last three seasons. (51.1 GB%) While this number looks insignificant on the surface, putting it into some context makes all the more astonishing. The second-place GB% leader is the Dodgers at 48.3%. The last-place finisher in this category, the Dodgers’ LA counterpart, finished at 41.8%. This means that the range between the second-place team and the last-place team is 6.5% while the difference between the Pirates and Dodgers is 2.8%. Their rotation this year is set to be comprised of (with their GB% over the last three seasons):

  1. Gerrit Cole (48.6 GB%)
  2. Francisco Liriano (52.0 GB%)
  3. Jeff Locke (51.6 GB%)
  4. Jon Niese (51.2 GB%)
  5. Ryan Vogelsong (41.1%)

These five average out to a 48.9 GB%. This is including the clear outlier in Ryan Vogelsong who was recently acquired via the Giants and who will post better ground ball numbers under pitching coach Ray Searage. These five will pair up with save machine Mark Melancon and steadily growing Tony Watson, and the Pirates are set to be the under-valued ground ball juggernaut that they have been accustomed to being over the last three seasons. However a steady flow of grounders is only a real weapon if there are infielders to stop them.

Jordy Mercer will likely accumulate much of the starting shortstop action as the absence of Neil Walker will make Josh Harrison slide into the second-base position, leaving room for Jung-Ho Kang at third, pending his return from knee surgery. Getting rid of Neil Walker may prove wonders for the defense of the Pirates infield.

Mind you this fielding arrangement is a tentative one — if it comes to fruition, it will improve the overall defense of the Pirates dramatically.

This is the Pirates’ most commonly started infield for the 2015 season (with UZR values from 2015):

1B: Pedro Alvarez (-14.3)

2B: Neil Walker (-6.8)

SS: Jordy Mercer (1.5)

3B: Harrison/Kang (0.7)

This comes out to an average UZR of -4.7 per position, not doing the starters any justice. Now, here is the Pirates’ projected starting infield for the 2016 season (with UZR values from 2015):

(Projected infield from

1B: John Jaso (???- Only played five innings at first over career)

2B: Josh Harrison (0.2)

SS: Jordy Mercer (1.5)

3B: Jung-Ho Kang (1.6)

Granted Jaso is a mystery as to what he will do at first; we can assume, or hope, that he won’t be as bad as Pedro Alvarez. Even if he is below average, the defensive improvement will be significant from Alvarez. While they are losing power in their lineup, the defense may make up for some of the home runs they are losing from Alvarez. With Kang and Harrison on the rise and pitchers that are keeping the ball out of the air, the Pirates could be poised to have a fourth straight good season. While the Cubs look like they’re going to take the division, Pittsburgh could have a potential Wild Card run in their future.

(I am 15 and this is my first article. Open for criticism!)