Every writer and fan in America seems obsessed right now with Bryce Harper’s hypothetical-in-name-only free agency after the 2018 season. Clearly, to say Harper is an intriguing player and free agent is to break no new ground.

But three years in advance? There are more pressing concerns, such as: Is it possible to improve on a .330/.460/.649, 42 home run, 197 WRC+, 9.5 WAR campaign? This may strike some as an absurd proposition in many respects, but it is nonetheless a subject of discussion just before those glorious days whereon pitchers and catchers report to spring training, providing the first light at the end of the long, dark tunnel that is the offseason.

Federal Baseball has the goods on this prospect of Bryce Harper actually getting better this year, quoting the man himself:

I’ve always said every time I come into Spring Training or every time I come into the season, I can always get better, you can get better everywhere you play. [New first base coach] Davey Lopes definitely is going to help me on the bases, that’s going to be a lot of fun. Being able to pick the mind of [new manager] Dusty [Baker] if that’s outfield, if that’s hitting, if that’s with pitchers and things like that, and he’s a very good hitter. So, to learn from a guy like that is very exciting, very fun and just makes the game that much better.

This is clearly the correct approach for any player to be taking. Any player not looking to improve is setting himself up for decline.

Base running does seem like something that anyone can improve on, or at least work on to prevent age-related decline (not that Harper is worried about that to any significant degree yet). But while Harper’s base stealing has cratered since 2013, his base running is okay as he did put up +3.2 BSR in 2015; that could indeed get better, but it’s not bad and it’s also not what we’re really here for.

The real question is, can you really get better as a hitter after putting up a 197 wRC+? As mentioned at the outset, it seems highly unlikely. And if we mean statistical superiority (hint: that’s what we mean), rather than some nebulous, clubhouse-valued notion of driving in runs or advancing runners (which you always worry is what they mean), the numbers a better season would produce become only even more mind-boggling.

Paul Sporer’s player preview for Harper notes Harper’s “career-highs in homers per fly balls (27%) and batting average on balls in play (.369).” Harper did post a .352 BABIP in 2014 after .310 and .306 efforts in his first two seasons, so he does seem to be above-average on balls in play, but no one should bank on another .369 BABIP season. And if the goal is to improve Harper’s offensive numbers, the BABIP might have to grow to an even more significant degree. Were that to happen, using it to in turn project 2017 would only be the errand of an even greater fool than I.

Federal Baseball commentator d_c_guy also notes precedent to indicate that it will be hard for Harper to put up better numbers in 2016, or even similar ones: “Mantle did put up a wRC+ of 196 in 1961 and 192 in both 1962 and 1963. Mays never did it. Miguel Cabrera and Albert Pujols have never done it. Setting expectations at that level is cruising for disappointment.” Indeed it is.

With all of the evidence before you, I am not going to sit here and say Harper’s numbers will get better. That seems extremely unlikely.

There is another avenue, however, where Harper could theoretically improve, and that is the strikeout rate. 20% is certainly good enough these days, especially for a power hitter, but lower marks are possible. The thing is, cutting back on strikeouts while not losing power or walks is a tall task for anyone. Even Pujols, who managed K rates below 10% during some of his most successful seasons, had a hard time reaching Harper’s 2015 marks of 42 home runs and a 19 BB%, let alone the .369 BABIP, in those years. So, no, I’m not telling you to bank on this, either.

Otherwise, only even higher balls-in-play success (already discussed) or even more power could produce a better line for Harper in 2016, but we know he already set career-highs in those marks last year. Can those get better? Sure, but not in sustainable fashion. Better HR/FB rates are possible, but Harper’s 27% figure in 2015 was aided by 15 “Just Enough” home runs according to Hit Tracker Online. Or, if you consider Harper might hit more fly balls instead of more fly balls per home run, then you’re looking at a BABIP decline.

Everything is possible, but most things are unlikely.

The thing is, who really needs Harper to get better than he was last year? If you cut back his triple-slash marks by 10%, you still get a .297/.414/.584 season. I think everyone would take that…except Washington’s division rivals and their fans. Well, and perhaps Harper himself. After all, $600-million contracts don’t grow on trees; they grow on 10-WAR seasons in your early twenties.

So my question is, do rookie pitchers get a similar treatment from umpires with regard to called strikes as do veteran pitchers?

In order to evaluate this question, I first had to develop a strike zone to evaluate.  So using the PITCHf/x data from 2013, 2014, and 2015, I created a model of the strike zone which was broken down into tenth-of-a-foot increments and plotted the probability of a strike or ball being called when a pitch was thrown inside that range for all the balls and strikes looking over those three years.  I did separate strike zones for lefty hitters and righty hitters since umpires should have a slightly different perspective depending on the batter’s location.

The strike zones I arrived at are shown here:

Once the strike zones were determined, I was able to go through the PITCHf/x data and tag every pitch thrown which resulted in either a ball or called strike with the associated probability of a pitch in that location being called either a ball or strike.

This then allowed me to take any individual pitcher and calculate an average “strike” probability for his called strikes.  As an example, here were my 2015 top 10 pitchers in terms of average strike likelihood (minimum pitches of 750 that were either balls or called strikes).

The lower the percent, the better. This means that on average when Dallas Keuchel got a called strike over the course of the entire season, that pitch was only likely to be called a strike 73% of the time. To show the impact this could have, Stephen Strasburg in 2015 had 402 called strikes; however, his Strike Likelihood% was 86.5%.

So if Strasburg threw a pitch into a zone where there was an 80% chance of that pitch being called a strike, he was unlikely to get that call, while if Keuchel or Jon Lester or Gio Gonzalez threw that same pitch they were very likely to get that call.

Strasburg is particularly interesting due to the fact that both him and Gio are on opposite sides of the spectrum, since the first thing that would jump out to you is catcher framing as part of the delta. Looking at the top 10 list from 2015 for example you notice a lot of Pirates and of course Francisco Cervelli was loved by the catcher-framing metrics this year.

But catcher framing shouldn’t really be a major issue in the evaluation of rookie versus veteran pitchers. It’s unlikely rookies wouldn’t be caught by the primary catcher.

My next step was to calculate the Rookie Strike Likelihood% for 2013, 2014 & 2015 and compare it to the Non-Rookie Strike Likelihood% for those same seasons to see if there was any “bias”. I set my minimum total balls + called strike total to the 1st quartile value for that season. Remember the lower the SL% the better — this means a pitch can be “worse” and still called a strike.

2015 (135 minimum)

Non-Rookie SL% – 81.1%

Rookie SL% – 82.1%

2014 (114 minimum)

Non-Rookie SL% – 82.1%

Rookie SL% – 82.4%

2013 (166 minimum)

Non-Rookie SL% – 82.0%

Rookie SL% – 83.1%

So while the gap is not always huge, there is in each year a delta in the SL% which favors the veteran pitchers.

What does this mean? This could mean nothing. It could be entirely due to rookies just not working the zone in the same way veterans do, or it could be related to the specific pitch selection (fastball vs. curve vs. slider) and how those different pitches are typically located in the zone. It could be related to how often rookies are ahead vs. behind in the count against batters and what that means for their next pitch location.

Then again, it could just mean that there is some bias against rookies where they don’t get a sort of “Jordan” impact where your reputation gets you a call that maybe you wouldn’t have gotten without it. In all likelihood it is a combination of both. But given that this seems to be a real thing, it could also be used in the evaluation, again, of catcher-framing metrics. Catchers who catch an abnormally high amount of rookies in a season could see their framing “skills” negatively impacted due to their counterparts alone and not a diminishing skill on their part.

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

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

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

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

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

View post on imgur.com

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

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

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

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

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

Clearly, the system works to some degree.

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

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

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

We all know the story: Yoenis Cespedes is a bad defensive center fielder.  In 912 career innings in center field, Cespedes has rated miserably in both Ultimate Zone Rating (UZR), with a -17.6 UZR/150, and Defensive Runs Saved (DRS), with a prorated -23.7 DRS/150.  Based on those metrics, he should continue to be an awful defensive center fielder in 2016, right?

Not necessarily.  Let’s use a few different methods to estimate Cespedes’ defensive value as a center fielder and determine how effective he will be in the future.

Method 1: Regress past defensive data in CF

This is the simplest (and crudest) method of all.  If we average Cespedes’ center field contributions per 150 games by UZR (-17.6) and DRS (-23.7), we find that Cespedes is a -20.85 run defender per 150 games.  Because of the small 900-inning sample, we’ll regress that by 50% and estimate that Cespedes is a -10.4 runs per 150 games defender in center.  This is what many people in the analytical community roughly believe Cespedes’ defensive value in center field to be. Methods 2 and 3, shown below, illustrate why I disagree with this valuation.

Method 2: Combine Cespedes’ Range in CF with his Arm Throughout the Outfield

One thing everyone can agree on with Cespedes: he has a cannon of an arm.  Whether he’s playing center field or left field, we should expect his arm to be significantly above average, right?

Well, in his 912 career innings in center field, UZR and DRS seems to disagree.  They rate his arm at -0.8 runs and +2 runs, respectively.  Decent, no doubt, but not the arm that most of us are accustomed to with Cespedes.

Yet, if we look at his entire career in the outfield, including time in both center field and left field, his arm has been worth +28 runs by DRS and +26.5 runs by UZR in roughly 4300 innings.  When averaged and scaled to 150 games, the value of his arm comes out to roughly +9.5 runs per 150 games over a very large sample, much more in line with what we would expect.

Next, we must factor Cespedes’ center-field range into the equation.  In 912 innings, DRS pegs his range (they term it rPM) at -17, while UZR estimates his range (they use RngR) at -12.2.  When averaged and scaled to 150 games, his range comes out to -20.4 runs per 150 games.  Because of the small 900-inning sample, we’ll once again regress his range by 50%, getting us to -10.2 runs per 150 games.

Factor in his arm, worth +9.5 runs per 150 games, and suddenly our estimate of Cespedes comes to -0.7 runs per 150 games in center field.  In other words: his excellent arm makes up for his poor range, making him a roughly league-average defensive center fielder.

Method 3: Isolate the Value of Cespedes’ Arm, Then Use Positional Adjustments to Estimate Cespedes’ Range in CF

This is the most complicated of the three methods.  First, we must become comfortable with the idea of positional adjustments.  Essentially, the purpose of positional adjustments is to provide a run value for each position, using past data of players switching positions to estimate the defensive difficulty of each position.  For example, while shortstop is a difficult position to play — and hence has a +7.5 run positional adjustment (per 162 games) — first base is not, with a -12.5 run positional adjustment.  Theoretically, if a shortstop was to switch to first base, the theory of positional adjustments would estimate a 20-run improvement in defense per 162 games.

Of course, positional adjustments don’t always work so conveniently, a reality the Red Sox discovered the hard way after moving Hanley Ramirez from shortstop to left field backfired tremendously.  Indeed, the difficulty of learning a new position oftentimes overshadows the theoretical improvement that should come from moving down the defensive spectrum.

In the outfield, however, things work much smoother, simply because each outfield position requires roughly the same skill-set: speed, first-step quickness, and efficient route running.  Using the positional adjustments from FanGraphs, we’d expect a left fielder (-7.5 run positional adjustment) to be approximately 10 runs worse in center field (+2.5).

For this exercise, we’ll isolate Cespedes’ arm from his range, using the +9.5 runs per 150 game figure we got from Method 2 to estimate the value of his arm (or +10.3 runs per 162 games).  Why?  For the most part, throwing arm strength is something we don’t expect to change too much shifting from left field to center.  The main difference between playing center field and left field is the range required for each position.

Estimating Cespedes’ range in center field using positional adjustments requires some tricky math.  First, let’s examine Cespedes’ range throughout his entire outfield career.  In 4295.33 innings combined between the two positions, Cespedes’ range is estimated at -13 runs by DRS (rPM) and -4.3 runs by UZR (RngR), or an average of -2.9 runs per 162 games (FanGraphs’ positional adjustments are scaled to 1458 innings, or 162 games).

Next, let’s calculate the percentage of his innings in left and center.  3383/4295.33 shows us that 78.76% of his innings came in left field, and, by extension, that 21.24% of his innings came in center.

Now, the tricky part: algebra. If “x” is his range in CF, “x+10” is his range in LF, and +10 is the positional adjustment per 162 games from LF to CF, we solve for x with the following formula:

0.2124 * x + 0.7876 * (x+10) = -2.9

Wolfram Alpha, what say you?

x = -10.8, or -10.8 range runs per 162 games in CF.

Now, factor in Cespedes’ +10.3 runs per 162 games from his arm, and you arrive at his defense being worth -0.5 runs per 162 games.  Just as in Method 2, it appears that the value of Cespedes’ throwing arm essentially counteracts his poor range, making him once again a roughly league-average defender in center

Method 4: Use Positional Adjustments to Estimate Cespedes’ Total Value in CF

While Methods 2 and 3 are certainly improvements over Method 1, there are some minor flaws in the methodology for each of the two methods. In Method 2, we arbitrarily regressed Cespedes’ range in CF by 50%, when in truth we don’t know exactly how much his range needs to be regressed.  In both Methods 2 and 3, we assumed that the value of Cespedes’ arm wouldn’t change significantly by moving from LF to CF, when in reality it may be more difficult to accumulate value via throwing as a center fielder.

To address these concerns, let’s do the same Method 3 Calculation except instead of attempting to find Cespedes’ range in CF, we’ll try and estimate Cespedes’ total value in CF, using nothing other than positional adjustments, UZR, and DRS. Rather than breaking down those metrics into their individual components, we’ll simply use the positional adjustments on the metrics themselves, a more traditional calculation.

First, let’s average Cespedes’ total DRS (15 runs) and UZR (20.7 runs) and scale it to 162 games, arriving at +6.1 runs per 162 games between left and center. Then, let’s do the same algebra we did in Method 3, with “x” representing his UZR/DRS in CF and “x+10” representing his UZR/DRS in LF.

0.2124 * x + .7876 * (x+10) = 6.06

We’ll head over to Wolfram Alpha one last time, with x = -1.8 runs per 162 games.

This might be the most accurate estimation of his value in CF of all, as it doesn’t rely on the raw value of his arm (like in methods 2 and 3) or a regressed version of his range in center (like methods 1 and 2).

Conclusion

Don’t believe the skeptics.  While Cespedes has rated terribly in roughly 900 innings of data in center field, it’s silly to limit yourself to such a small center field sample size when we have more than 4000 innings of data, separate range and arm ratings, and positional adjustments at our disposal.  Using some basic arithmetic, we’ve proven that Cespedes should probably be no worse than a hair below average defensively in center field, as his extremely valuable arm (+10.3 runs per 162 games) makes up for his below-average range.

A few days ago on Baseball Prospectus, Rian Watt wrote a piece entitled “What Comes After Sabermetrics?“. In his article, Watt discusses the next era of baseball writing and speculates that exploring the social side of baseball will rise in prominence. The next generation of great baseball writers will be those who link baseball to social sciences — from politics to people. It will be the human side of America’s Pastime.

Social understanding is not only important for storytelling; it can also lead to interesting analysis. Social understanding helps us realize who people root for and why, as well as explains many of the not-so-obvious factors affecting fandom. Whereas statistical analysis can assist in complicated problems within the structured game, social analysis can help in off-the-field complex problems such as marketing and fan base development.

Which leads us to perhaps the most complex problem in sports marketing today: the fan base of the Tampa Bay Rays.

Last year, I wrote a piece on FanGraphs that discussed a major reason why the Rays struggle with attendance. My conclusion was that the amount of fans living near the ballpark had a huge impact on a team’s weekday attendance. The Rays were dead last in MLB in local population and had the widest difference between weekday and weekend attendance. In 2014, the Rays averaged 14,297 fans Monday through Thursday. On Friday through Sunday, with fans given more time to get to ballpark, their attendance increased 51.7% to 21,692.

In 2015, the Rays again struggled to draw fans during the week. Last season, however, their difficulties at the gate extended to the weekend, specifically Fridays (only 14,887 fans per game). Still, their difference remained well over the 2014 MLB average weekend/weekday difference of 20% and far above the Giants’ weekend/weekday difference of 0%.

• Mon-Thurs average attendance: 12,688
• Fri-Sun average attendance: 18,328
• Increase: 30.7%

Since my last article, I have continued to research the complexities of the Tampa Bay baseball market. With the team finally able to explore the region for a possible new stadium location, I want to know if a new stadium is going to matter. Is the amount of money taxpayers are inevitably going to spend worth the trouble? Will the Rays see an increase in attendance if they build a stadium in Tampa or on east side of Pinellas County? If we are sure the Tropicana Field site is wrong, which of the front-running locations is better?

And what about some of the other social variables? It is a well-established fact that Florida has a high amount of non-natives. In 2012, only 36% of people living in Florida were born in Florida. We can probably assume that number is higher in the metro areas and lower in the rural regions. The Tampa Bay area, for example, has a high population of people from New York and other Northeast states.

According to the New York Times, 50,000 New Yorkers a year move to Florida. According to the Tampa Tribune, roughly 10% of those move to Hillsborough, Pinellas, and Pasco Counties — the Tampa Bay area.

That’s 5,000 New Yorkers a year moving to Tampa Bay. If 50% are baseball fans, that’s 2,500 fans per year not rooting for the local team. In the case of the Yankees, these fans are rooting directly against the local team. With a metro population of 2.8 million, that’s a nearly 1% increase per year in opposing fans moving to the area. So any research we do has to keep that population in mind.

In order to attempt to untangle the complex mess that is the Tampa Bay baseball market, I’ve started to deep-dive into census data, distances, and fan preferences. For population I use census.gov; for distance I use Google Maps; and for fan preference, I use the New York Times/Facebook 2014 interactive map of baseball fandom.

Currently, the Tampa Bay area has 239 zip codes assigned. Here are the 11 most populated:

The reason the list goes to 11 is not just a Spinal Tap reference — it is because the 11th-most populated zip code is the current location of Tropicana Field and the only Pinellas County mention on the list. If I were to extend the list to 12 we would see one additional Pinellas County entry. However,  number 12, zip code 34698, is Dunedin, Florida, spring-training home of the Toronto Blue Jays. So we will keep the list to 11.

Unfortunately, as you can probably guess, none of the top 10 are near Tropicana Field. As a matter of fact, the average distance from the center of the 11 most populated zip codes to Tropicana Field is 29 miles.

On my site, I’ve written how the four minor-league teams in the Tampa Bay are a closer Mon-Thurs alternative for baseball fans in the Tampa Bay area. They are not only cheaper, but also more convenient. Here are the average distances of the 11 most populated zip codes to Steinbrenner Field (Tampa Yankees), Bright House Field (Clearwater Threshers), Florida Auto Exchange Stadium (Dunedin Blue Jays), and McKechnie Field (Bradenton Marauders).

• Avg distance to Steinbrenner Field: 16.5 miles
• Avg distance to Bright House Field: 24.2 miles
• Avg distance to Florida Auto Exchange Stadium: 27.3 miles
• Avg distance to McKechnie Field: 49.6 miles

Turning to the social aspect, we next add the Facebook “like” data to our chart. Here we see the Rays don’t have an overwhelming amount of fans anywhere in Tampa Bay area. Even in the Tropicana Field zip code less than 60% of baseball fans root for the home team, although 33713 does have the lowest percentage of Yankees fans on the list.

By comparison, in the similarly-sized Pittsburgh area, 70-75% of fans are Pirates fans and Yankees fans are roughly 5-7%. There are nearly 3x more people rooting for the Yankees in Tampa Bay than in Pittsburgh. Granted there is a longer tradition of rooting for one team in Pittsburgh, but that culture is easier to develop when there is only one team in the area.

So will building a new stadium help the Rays? Here is the population chart with the Rays fandom and distances to two front-running new stadium locations: Toytown and the Tampa Park Apartments.

By average, the Tampa Park Apartments location is 12 miles closer to the top 11 populated zip codes. The Toytown location splits the difference.

• Avg distance to Tropicana Field: 30 miles
• Avg distance to Toytown: 24 miles
• Avg distance to Tampa Park Apartments: 18 miles

Both the Tampa Park Apartments and the Toytown location have another advantage the Tropicana Field location doesn’t have: both are within 15 miles of Steinbrenner Field and Bright House Field, meaning territorial rights can be exercised. While the MLB team has priority and can force the MiLB team to move, doing so might require compensation. For the Rays, removing the competition might be worth the extra cost, even if means paying the high ransom of a division rival.

(Note: territorial rights does not apply to Spring Training currently. If I was the Rays, I would fight that based on the precedence set by the Yankees and Orioles, who moved out of the Miami area before the Marlins began play in South Florida. I would also claim lost local revenue to Spring Training competition. Local fans who go to Steinbrenner Field could just as easily wait a month to see the Yankees at Tropicana Field.)

After a new stadium is built, after the competition is cleared out, and after the Rays have a monopoly of their small market, then they can finally attempt to win the hearts and minds of the region as other small-market teams do.

Rian Watt is absolutely correct. Social understanding is the next great baseball unknown. Knowing the story of fans, where they live, and what motivates them to support teams will be essential as we move from solving baseball’s complicated problems to finding solutions to its most complex problems.

As a whole, Elvis Andrus’s 2015 season was quite unremarkable. In his seventh year in the bigs, he set career lows in batting average and OBP while finishing with his second-worst wRC+ season of his career. He also stole his second-fewest amount of bases while scoring fewer runs than ever before.

One thing that he can hang his hat on, though, was his power output. Andrus finished 2015 with the second-highest ISO of his career, setting a new career high for home runs in the process. Now, he still only hit seven, but we’re talking about the player who hit zero in 674 PA in 2010. Elvis Andrus hitting seven home runs in a season is like Barry Bonds hitting 85, or Ben Revere hitting three.

Reaching seven home runs was actually quite an extraordinary feat for Andrus, not because of the total itself but because of how it compared to his 2014 season. Andrus hit just two home runs that year, which tied him for second-fewest in the MLB among qualified batters. By hitting seven the next year, he more than tripled his previous total. Only three hitters who qualified both years achieved the same feat:

What’s even more impressive is that two of those players, Carpenter and Andrus, had fewer plate appearances in 2015 than 2014. So how did they manage to do it?

I’ve been focusing on Andrus, so let’s continue with him. His HR/FB% went up a little in 2015, but it was only 1% higher than his career average and lower than his output in two of his previous seasons. Since that clearly wasn’t the change, it must’ve been something else. Looking at his batted-ball breakdown, something shows up.

Andrus finished 2015 with a 31.8 FB%, the highest of his career. This was an increase of 10.9% from 2014, which represented the largest increase in FB% of any player between the past two years:

Eaton and Carpenter also both make this list, explaining their power outburst (at least partially). Some of these players aren’t very surprising, only making this list because their 2014 FB% was much lower than their career norm and they were simply regressing to where they should be (see: Pujols, Albert). Others, like Altuve, are only just beginning to explore their power potential.

Regardless of the reasoning, the most important question that comes from this list is whether or not those on it can duplicate their performance. Without looking at individual swings and searching for differences, I decided the easiest way to determine this was by looking at historical data. Since batted-ball data became available in 2002, there have been 19 different qualified players to increase their FB% by 10% or more between consecutive seasons, and then play another qualified season the following year:

Only twice did the player make even further gains in their FB%, and the average regression among all 19 of the players was 46.01% toward their first-year numbers. With this in mind, it’s difficult to envision players like Andrus and Frazier repeating their performances from last season. And even if that means we won’t be seeing a double-digit home-run season for Elvis Andrus anytime soon, I think that we’ll be all right without one.

If you’re a baseball fan, you want to see the game succeed around the world. (Note: If you’re not a baseball fan and you’re reading this post, then a cruel and capricious Fate has once again sent your life’s tormented journey careening badly astray.) Baseball is the national sport in Japan and Cuba. It is played avidly in great swaths of Latin America. Korea, Taiwan, and even Australia have popular leagues. Baseball spans the globe and it was invented here. That’s pretty cool.

But a game that has colonized the land of the marsupials has struggled to gain a foothold at the major-league level in one place right here in the U.S. of A., and that’s Denver. Since their birth in 1993, the Colorado Rockies have never had four consecutive winning seasons. They’ve been to the postseason just three times, failing to get past the divisional series twice.  During their 23 seasons, the Rockies have finished last or second to last in their division 18 times.

The Rockies’ recent puzzling trade of Corey Dickerson for Jake McGee has renewed existential discussions about baseball at altitude: Can the Rockies ever win? If so, how? One aspect of this broader inquiry looks at the statistical anomalies associated with Coors Field. Another branch, the limb I’ve crawled out on here, looks at the Rockies’ roster construction problems.

We are fortunately not completely bereft of evidence bearing on the question of how to assemble a winning Rockies roster. Dan O’Dowd did it, taking the team to the World Series in 2007 and establishing the franchise’s only semi-sustained run of non-futility from 2007-2010. His success was somewhat fleeting, which is why he’s working for the MLB Network now. But he did at least momentarily succeed where all other have failed, so it’s worth sifting through those old Rockies to see if any useful artifacts can be found.

The 2007 Rockies were a career-year team. A lot of things went right for a lot of players at exactly the same time. Matt Holliday and Troy Tulowitzki had MVP caliber seasons (though fWAR liked Tulo a little less than bWAR did). Holliday, Kaz Matsui, Jeff Francis, and Manny Corpas all had career years under either version of WAR, and bWAR says 2007 was Tulo’s best year. Those were five of the Rockies’ six WAR leaders in 2007, the other being Todd Helton. Except for Matsui and Helton, they were under 28 years old.

These Rockies could pick it, especially in the middle infield. Regardless of defensive metric used, Tulowitzki and Matsui had outstanding defensive seasons. Using Fangraphs Def rating, Tulo at 22.2 runs above average was behind only the magnificent Omar Vizquel (30.2) at short. Matsui lacked enough innings to qualify, but he would have been third (13.0) behind only Brandon Phillips (19.4) and Chase Utley (14.5). The metrics split significantly on two other Rox, Holliday and Helton. Total Zone loved ’em, Def did not.

The Rockies took advantage of that iron curtain infield with a heavy ground ball pitching staff. The Rockies staff was first in the NL in the ratio of ground balls to fly balls, and in ground outs to air outs. They were better than the league average in WHIP, H/9, and BB/9. Their ERA and FIP were mediocre, but the park-adjusted figures were much better. Their ERA- of 90 was good for third in the NL, while their FIP- of 97 tied them for fifth. The one thing the Rockies pitchers didn’t do was miss bats – they were 14th of the then-16 NL teams in K%.

One more thing about the 2007 Rockies: they were young. Pitchers and hitters averaged about a full year younger than the league. Helton and Matsui were the only starting position players over 30, and Rodrigo Lopez was the only semi-regular rotation denizen over 30. The bullpen had two key contributors over 30: Brian Fuentes and LaTroy Hawkins, but also two under 26 (Corpas and Taylor Buchholz).

Today’s Rockies lack most of that 2007 vibe. The 2015 lineup had just one player, Nolan Arenado, who had a breakout season, and he was the only player from whom such a season might have been expected. The middle-infield defense was was almost exactly average, with Tulo (-1.6 Def) and DJ LeMahieu (2.6 Def) mere shadows of the 2007 keystone combination. The pitchers still get a lot of ground balls, but they don’t do anything else well. In 2007 the Rockies staff tied for 7th in the majors in average fastball velocity; last year they tied for 17th. And the team is older; the hitters are at just about the league average, and the pitchers roughly four months above it.

Career years, stellar defense, hard throwing: these are the components of a younger man’s game, and this is especially true in Denver’s lung-busting altitude. Whether by design or accident (or more likely some combination of both), O’Dowd found a winning recipe for Coors that exploded into relevance in 2007. Assemble a roster of mostly younger players with high ceilings, and hope a decent quantity of them hit those ceilings at the same time.

Not only is this easier said than done, it is also a strategy freighted with risk. A roster built like this may well still fail more often than it succeeds, though the successes can be very sweet. The expanding competition for young talent puts a premium on the Rockies’ ability to find players deeper down prospect lists that have promise and have not yet come close to achieving it. It’s harder to sell tickets when the team isn’t relentlessly successful.

But an overlooked aspect of Coors Field is its positive impact on team revenues. It is simply a fabulous place to watch a baseball game, particularly on a sun-drenched Denver summer afternoon. The Rockies put a craptastic product on the field last year and still managed to rank 8th out of 15 NL teams in attendance. This is the worst they’ve done in the last five years, despite fielding teams that have evaded victory with alarming regularity.

Denver fans are enthusiastic and patient. This is exactly the kind of fan base for which a gambling, win-in-the-window and then rebuild strategy might work. This is the fan base Billy Beane wishes he had.

The Rockies may or may not be poised to implement a plan like this. They now have six prospects in MLB Pipeline’s top 100, headlined by Brendan Rodgers, a player that may have the glove to stick at short with a bat that would play at third. They have some young hard (or at least harder) throwing arms. They have David Dahl, a center fielder who might have the enormous range to make fly balls slightly less dangerous to the pitching staff.

But there are some dragons on the map. Forrest Wall, the Rox’ top second base prospect, is more bat than glove, a combination that may be less helpful at Coors than elsewhere. Ryan McMahon is a third baseman and Trevor Story may profile best there, but this is the one major-league hole the Rockies have already filled. They have a glut of low-ceiling outfielders only slightly alleviated by the McGee trade.

That trade looks less puzzling seen in the light of a young, high-upside strategy. As David Laurila recently noted, the most favorable to way to interpret this trade from the Rockies’ standpoint is that GM Jeff Bridich intends to flip McGee for one or more promising prospects. Corey Dickerson is a decent player, but he doesn’t really fit with this kind of plan. It’s reasonable to expect a similar trade involving Carlos Gonzalez before the trade deadline. You will definitely need a scorecard to identify your 2017 Rockies.

The wildest of cards here is the Rockies’ erratic ownership group, at whose behest the team held onto Tulo for too long, and may have done the same with CarGo. If the Rockies want to follow the strategy outlined here, they will need to constantly and relentlessly purge their roster of older players when the career-year potential is behind them and their defense (or velocity) starts heading south.

Owners often want to hang on to the old, familiar names. The Rox would be better off having hearts as cold as their ballpark’s beer.

It’s not the qualifying offer, stupid — it’s the hard cap on bonus pools.

You have to hand it to the owners.  The players’ union has had a long history of sticking it to players who are not yet part of the union, so when it came time to negotiate the latest CBA, the owners took advantage of that fact to pump the brakes on what was previously a runaway free-agent market.

How are these two concepts linked?  You need to look at the history of the draft and the behaviour of wealthy teams to understand what is going on.

Scott Boras has been doing a lot of whining lately about how free-agent compensation is making it hard for his clients to get paid.  The thing is, there has always been free-agent compensation, so this is not the problem.  The previous CBA had quite a bit more compensation that the current one — any pending free agent that rejected a team’s offer of salary arbitration would entitle the team to a compensation pick from the team who signed him away from you.  The Elias rankings (e.g. A, B) and standings-based pick order dictated the quality if the pick received.  For a team losing a Type A player, they would even get a extra “sandwich pick” for their troubles.

The thing is, the rich teams who were losing all those draft picks didn’t really care.  Why, you may ask?  It’s because they had other ways to sign talent that did not require a high draft pick:

(a) draft a “hard to sign” player and offer them a big, “over slot” bonus.
(b) spend aggressively on international free agents.

The latest CBA has plugged both of those holes.  Teams have both an international spending limit and an amateur draft spending limit (based on “hard slots” for each pick they have).  Exceed either of those limits, and the penalties are steep.

Suddenly draft picks are a whole lot more valuable, because when you lose a draft pick you cannot replace it with the aforementioned methods.

The owners did concede a minimum “qualifying offer” for pending free agents, which is set based on the salary of the top 125 players in the previous season.  As long as salaries continue to rise, then this number will rise as well.  However Boras has noticed that the growth of this figure has slowed in the past few years.

Once owners succeed at instituting an “International Draft”, they will plug the remaining source of uncontrolled spending — teams have shown a willingness to sit out a whole year of International signings as long as they can sign enough talent in a given signing period.

The players have struck back by some degree by introducing the “opt-out” concept, to allow them to re-enter the FA market 1-3 years after making a long-term commitment to a team.  One wonders if that type of contract will be on the table when the next CBA is negotiated.

It really is a great system for the owners:

• Owners control the the size of the bonus pools
• non-star free agents no longer receive rich multi-year offers (well, except Ian Kennedy)

And it’s working.  The players’ percentage of MLB revenues has been in steady decline.  So much so that the players are considering (for the first time) the idea of a salary cap linked to league-wide revenues.

Well played Rob Manfred, well played.

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Consequently, Greg Luzinski is listed on the Phillies roster for the duration of his career while the Browns / Orioles declare Steve Finley and the Padres claim Derrek Lee. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

Assessment

The 1906 Chicago Cubs          OWAR: 58.8     OWS: 362     OPW%: .518

Based on the revised standings the “Original” 1906 Cubs finished fourth in a tight battle with the Giants, Cardinals and Pirates. Chicago topped the National League in OWS and OWAR.

Frank “The Peerless Leader” Chance supplied a .319 BA and led the circuit with 103 aces and 57 thefts. Frank “Wildfire” Schulte pilfered 25 bags and slashed a League-best 13 triples. Johnny Kling manufactured a .312 BA while the famous keystone combination of Johnny Evers and Joe Tinker collectively swiped 79 bases.

Hugh Duffy ranks twentieth in the All-Time Center Fielder rankings according to Bill James in “The New Bill James Historical Baseball Abstract.” Teammates listed in the “NBJHBA” top 100 rankings include Bill Dahlen (21^st-SS), Chance (25^th-1B), Evers (25^th-2B), Tinker (33^rd-SS), Bill Bradley (46^th-3B), Kling (48^th-C), Schulte (60^th-RF) and Jim Delahanty (81^st-2B).

Bob “Dusty” Rhoads (22-10, 1.80) delivered personal-bests in every major pitching category. Jack W. Taylor (20-12, 1.99) and “Tornado” Jake Weimer (20-12, 2.22) also surpassed the 20-win mark for the Cubbies. “Big” Ed Reulbach (19-4, 1.65) paced the Senior Circuit with a .826 winning percentage. Carl Lundgren added 17 victories and fashioned a 2.21 ERA.

The “Original” 1906 Chicago Cubs roster

Honorable Mention

The “Original” 1945 Cubs      OWAR: 50.4     OWS: 307     OPW%: .654

The Cubs (101-53) eclipsed the century mark in victories to secure the pennant and amassed a comfortable lead in OWAR and OWS. Phil Cavarretta (.355/6/97) merited 1945 National League MVP honors while topping the circuit in batting average and OBP (.449). “Smiling” Stan Hack scored 110 runs and supplied career-bests with a .323 BA and 99 bases on balls. Augie Galan (.307/9/92) coaxed 114 walks and registered 114 tallies. “Handy” Andy Pafko (.298/12/110) established personal-bests in RBI and triples (12). Hank Wyse (22-10, 2.68) completed 23 of 34 starts and Harry “The Cat” Brecheen (15-4, 2.52) contributed a league-best .789 winning percentage.

On Deck

The “Original” 1980 Astros

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

The last couple years, I have calculated the Mayberry Method for the NL from the Baseball Forecaster Book from Ron Shandler. The Mayberry Method is based on speed, power, batting average, and role (PAs). A score from 0 to 5 is assigned for each of the three skill categories and added and multiplied to the role score. After the “raw” score is determined, it is multiplied by three reliability factors.

When I determine the total score for each player, I split them up into positions. Next, I take the 30th percentile score (widely considered as the replacement level) and subtract each score from the replacement-level score for that position.

For example, Christian Yelich had a 2 score for power, a 4 score for speed and batting average, and a 5 score for role. One would add (2+4+4+5)=15. Then I multiplied 15 by 5 (the role score) and got 75. Next I multiplied 75 by the three reliability scores which were 1.05, 1.05, and 1.1 to get 90.95 as the total score. Because, Yelich was an outfielder, his score was subtracted by the replacement level score for outfielder of 63. So, Yelich’s final score was 27.95, good for 25th overall. Without much further ado, here are the rankings.

The first column is the total score, the second column is the name and position, the third column the number equivalent of the position (Catcher 2, 1B 3, 2B 4 and so forth), the final column is the position adjusted Mayberry score. Notice four out of the five players are second basemen (the other is Starling Marte an elite power-speed-BA player). Note for fantasy players in NL-only leagues, second basemen make up seven out of the top-20 but are far-and-few-between lower than that.