Dee Gordon is a polarizing player. His all-speed, no-power approach on offense has both fans and projection systems divided on what to make of his bat. Is he an elite offensive second baseman? Is he a one-hit wonder that won’t be able to repeat his numbers from 2015? Reasonable people can really disagree on Gordon’s bat.

Reasonable people can also really disagree on Dee Gordon’s defense, and that’s where I intend to focus my analysis today. Dee Gordon led all second basemen with a 6.4 Ultimate Zone Rating (UZR), which means he was worth roughly six runs on defense compared to an average second baseman. That doesn’t sound too unreasonable, right? Here’s where things get interesting. Gordon, despite his obvious athleticism, had previously been considered a below-average defender, coming in with a -3.4 UZR last year at second base. He had been a massively below-average defender at shortstop (where he played a few years ago before moving to second base full-time in 2014), so there are years of data painting him as a minus defender relative to other middle infielders.

In 2015, Gordon’s advanced defensive metrics took a massive jump forward. Dee Gordon improved by exactly 10 runs according to UZR, which is roughly an entire win difference thanks to his defense. Which defender is the real Dee — the one that flailed around in 2014, or the elite defender from 2015?

Let’s find some historical comparisons, and see what they can teach us about the repeatability of Dee Gordon’s defensive statistics.

We know Dee Gordon improved 10 runs defensively at second base to become one of the best defenders in the league at the position. Let’s take a look at the past 10 years, and find all second basemen that improved by at least 10 runs in UZR from year to year and had a UZR of at least 5 in the improved year. There are 16 player seasons that fit this criteria. Excluding those that didn’t play enough innings to qualify at second, 11 player seasons were left fitting the criteria. The numbers are presented below, along with the UZR that the player recorded the season following his improved year.

Among the second basemen in the last 10 years that made a big jump into the elite of the defensive statistics, on average those players lost almost nine runs of UZR the following season after the leap. The group lost about 60% of the improvements they had made the following season, indicating that a big jump in UZR for a second baseman is unlikely to signal a new level of performance. Among the qualifying group, not a single second baseman improved their UZR the following year again and only one member of the group, Placido Polanco in 2009, regressed by less than four runs.

However, there is a slight bright side. Only one member of the group had a UZR that was lower the year after “the leap” than before the improvement, indicating that taking a leap of over 10 runs of UZR means you almost certainly have improved as a defender. It’s just not by nearly as much as you would think from the leap-year UZR, but the players kept about 40% of the improvement they made in their improved year.

What does this mean for the Marlins’ speedy second baseman? While Dee Gordon’s huge jump in UZR this year means he’s almost certainly a better defender than he was two years ago, the improvement to his talent is likely only modest and not nearly what you would hope for after his great 2015 defensively. To those who pointed to Dee Gordon’s greatly improved UZR this season as a reason to believe he’s made big strides as a defender, I’ll sadly have to point out that we can expect Dee Gordon to return much closer to the mediocre defender he was in 2014 than the star he was in 2015.

To say the market disappointed Ian Desmond would massively undersell the circumstances. After rejecting a 7-year, $107 million contract extension from the Washington Nationals prior to the 2014 season, Desmond now settles for a reported $8 million pillow contract with the Texas Rangers. Having been tagged with the qualifying offer, Desmond joined Yovani Gallardo and Dexter Fowler, among others, in witnessing their market evaporate due to the associated draft pick compensation. A career-long shortstop, Desmond attempted to work around this hindrance by marketing himself as a “super-utility” type, and indeed signed on with a club set at shortstop. Now the former Expos prospect hopes a shift to left field will recoup the value lost during a disastrous 2015 campaign.

Indeed, disastrous accurately portrays Desmond’s terminal season in the nation’s capital. Having posted three straight 4+ fWAR seasons from 2012-2014, Desmond appeared in line for a massive payday this offseason. Instead, a 1.7 fWAR, 83 wRC+ campaign left Desmond with minimal market appeal, at least at his initial asking price. Perhaps more worrisome – his continuous decline. After peaking at 128, Desmond’s wRC+ fell each of the last three seasons while his strikeout rate catapulted to nearly 30% the past two seasons. Similarly, Desmond’s hard-hit rate dropped nearly four percentage points in 2015, with the difference transferring to soft contact, while his groundball percentage rose each of the last two seasons. If you make a career out of slugging the ball, softer contact and more groundballs is just about the worst combination of progressions to make.

At his peak, Desmond stood among the premier power-hitting shortstops in the game – his .188 ISO from 2012-2014 ranked third among qualified shortstops, behind Hanley Ramirez and Troy Tulowitzki. Now, after shifting to left field, he provides more of an average to below-average bat while learning a new position. Furthermore, that pitchers altered their approach against him likely dissuaded some interested parties. Since his powerful peak, Desmond has seen an increase in sliders with an accompanying decrease in pitches thrown within the strike zone. During this time, Desmond suffered a precipitous drop in contact rate on pitches outside the zone. Perhaps pitchers discovered a weakness against sliders out of the zone, a point only accentuated by the fact that Desmond’s pitch value against sliders in 2015 rated at -5.8, the 18^th worst value in MLB. Even during his peak, however, Desmond consistently ranked among the league’s highest swinging-strike rates, perhaps indicating an inevitability to the skyrocketing strikeouts. Either way, Desmond’s penchant for swinging and missing surely concerned any club contemplating a long-term investment.

The Rangers appear not overly concerned with the strikeouts, at least not at the current cost. Between his salary and the draft-pick compensation, Texas seems to be expecting only about 2 WAR from Desmond, an entirely average forecast. Steamer pessimistically projects Desmond to accrue 1.4 WAR over 585 plate appearances, while ZiPS estimates a more fortuitous 3.1 WAR in 623 PAs. Averaging the two, you glean a smidge over 2 WAR in roughly 600 PAs – a figure almost perfectly in line with his acquisition cost.

Of course, your personal perception of Desmond depends largely on how you see him transitioning to left field. As an athletic shortstop with solid defensive history, one might expect Desmond to convert at least reasonably well. However, ask any Red Sox fan about Hanley Ramirez’ move, and you’ll understand some apprehension. With Rougned Odor, Elvis Andrus, and Adrian Beltre locking down the other infield spots, Desmond will occupy left the majority of the time. Desmond could additionally work at 1^st base to rest Mitch Moreland against tough lefties, although that would squander his athletic ability, as well as encroach on Justin Ruggiano’s role even further. Moreover, this signing insinuates that the front office holds little hope for Josh Hamilton staying productive and healthy this season — an entirely fair position considering he has taken the field for only 139 games the past two seasons combined — as well as a damning assessment of Ryan Rua’s ability to contribute to a contender. As defending division champions, the Rangers aim to maximize what’s left of the Yu Darvish/Adrian Beltre window, and bridging the hole in left until Nomar Mazara or Joey Gallo arrives certainly occupied a spot on their to-do list. But for a team anticipating to contend, was adding the uncertainty of a position switch truly the best path to take?

At the given contract, Texas should be ecstatic they picked up a recently All-Star caliber shortstop. It’s been accepted for a while that Texas is “in the range of where [they]’ll end up payroll-wise”, according to Jon Daniels, hence cost-prohibitive acquisitions of Justin Upton, Yoenis Cespedes, and Jason Heyward simply weren’t on the table. That understood, the market provided other, more cost-efficient options without the risk associated with the position swap. Recently signed Dexter Fowler only cost $5 million more than Desmond (both having draft-pick forfeiture attached to them), and is projected for a similar WAR output while making a less imposing transition from center to left. Furthermore, Fowler would have provided a back-up for center fielder Delino DeShields that Texas sorely lacks.

Along that same line of thinking, the still unemployed Austin Jackson would have provided a slightly lower projection, but without the relinquishment of the 19^th overall draft pick. My personal favorite option this offseason, Steve Pearce, signed for less than $5 million to play part-time for the Rays. Surely Texas could have offered him a similar contract at the time, where he could have provided right-handed power both in left and at first base. I would have thought Pearce or Jackson the more frugal acquisition for a cash-strapped Rangers ballclub, but palpable potential exists for Desmond to recapture his past success and make this deal quite the bargain. At one guaranteed year, this acquisition carries minimal risk while providing real talent to a contender; it’s difficult to dislike, even if you believe that more cost-efficient options exist.

The Indians have signed veteran third baseman Juan Uribe to a one-year deal. On the outside this looks like a very modest move, but looking more in depth it’s quite the improvement to the Indians lineup. Uribe will most likely take the lion’s share of at bats at third, taking over in place of youngster Giovanny Urshela. Urshela became the Indians everyday third baseman last year, after moving Lonnie Chisenhall to the outfield. Urshela’s defense was pretty good at third — he had a total of one defensive run saved, and his FSR was a +5, ultimately grading him as an above-average defender. However, his bat never was quite up to major-league caliber. He performed nothing like the player who slashed .302/.350/.503 with 21 homers in 155 minor-league games in 2014. For starters, his wRC+ was just 68…in other words he was completely abysmal at the plate. In 81 games Urshela barely hit his weight, hitting a measly .225 to go along with a miserable .608 OPS and just six homers. All things considered, the Tribe had to make a move at third. With their only other options being Chisenhall — an absolute fielding liability at third (-7 career DRS) — or Jose Ramirez, who has not been a very effective hitter either (.631 OPS, 75 wRC+ in 2015), Juan Uribe makes a ton of sense.

When looking at Uribe, the first thing that should be considered is his experience. He has a total of 14 seasons and 89 days as a major-league ballplayer. Uribe, who will be 37 on Opening Day, could be a great mentor for youngsters Urshela, Ramirez, and of course Francisco Lindor. Next, consider Uribe’s defense at the hot corner. Over the last three seasons alone he has 33 DRS, making him one of the very best at fielding his position. Uribe’s defense could even be considered an upgrade over that of young Urshela’s. Combined with Mike Napoli (the new Tribe first baseman) and Lindor, the trio have a total of 63 DRS since 2013 (Lindor had 10 in 2015, his only major-league season). His contributions to the Tribe defense, a defense that ranked third in all of major-league baseball in 2015, could be a major factor going forward. With the pitching staff already looking very solid, the upgrade Uribe provides to an already stellar defense could put the Tribe among the very top teams in the league at preventing runs.

Aside from defense, Uribe’s bat is a big upgrade from that of Urshela. Uribe has had fairly respectable numbers at the plate over the last three seasons. Since 2013, he has slashed .281/.329/.432 and has a combined WAR of 10.5. When comparing his previous season to Urshela’s, the upgrade becomes more evident. Urshela’s wRC+ was 37 points lower than Uribe’s (105), and his OPS was 129 points lower. Though Uribe’s offensive output is only slightly above average (as evidenced by his wRC+) it’s still a big improvement over any other option the Indians had at third.

Based upon this analysis alone, the impact Uribe will have on the Tribe will be quite significant. Given, of course, that his current offensive and defensive performance continues to be just as solid as it has been the past few seasons. In closing, Uribe will provide the Indians with something they’ve lacked consistently at the hot corner: a very good glove combined with solid offensive production. It’s likely, considering his age, that he will only be a short-term solution at third. But hopefully his experience will rub off on the younger generation of Indians players behind him, and leave them with a much longer lasting impact.

Game Score was first published in the 1988 Bill James Baseball Abstract as Bill James’ “annual fun stat.” Although the stat was created by one of the most prolific sabermetricians of all time and is now published in most box scores, it hasn’t been widely adopted for use in sabermetric analysis and instead remains mostly a stat that is “fun to play around with,” as James wrote 28 years ago.

Generally the game score metric makes it into headlines on two distinct occasions: first, if a pitcher exceeds a score of 100, due to how rare it is for this to occur; and second, as a means to compare whether a no-hitter or perfect game was more or less dominant than other no-hitters or perfect games throughout MLB history.

There are a few examples over the years of sabermetricians using game score as more than simply a “fun stat,” but these are few and far between. While the weights are indeed mostly arbitrary and it is based on simple counting stats, there is value in the simplicity of Bill James’ version of game score. The simplicity is two-fold: first, game score is easy to calculate; and second, it essentially converts a starting pitcher’s box score into a single number.

GmSc = 50 + 1 * outs recorded + 2 * innings completed after the 4^th + 1 * strikeouts – 2 * hits allowed – 4 * earned runs allowed – 2 * unearned runs allowed – 1 * walks

The results of this formula are as follows: values approaching 100 are outstanding, values around 50 are average, and values approaching 0 are terrible. In rare cases a game score can exceed the 0 and 100 extremes, but it is designed to rate the quality of a start on essentially a 0-100 scale.

In the following analysis I collected game score data for all games started in the six seasons from 2010 to 2015 and calculated the percentage of times each game score value resulted in a recorded win, loss, or no decision for the starting pitcher. These values serve as the inputs for a basic formula that can be used to calculate expected wins, losses, and no decisions.

Designing xW, xL, and xND weights for each GmSc

I pulled all GmSc data as well as the starting pitcher’s recorded W, L, or ND for each game from 2010 through 2015. There were 14,579 games in this six-year time frame and 29,158 Game Scores recorded (one for each starting pitcher, so two per game).

I then calculated the total wins, losses, and no-decisions for each game score value (0 to 100) and divided the total wins, losses, and no decisions for each game score by the total times each game score was recorded to get the win, loss, and no-decision percentage for each game score. To smooth the results, I applied three-median smoothing once and hanning five times.

This resulted in the values listed below, which are the expected win, loss, or no-decision percentage that will be applied to each game score value.

Link to spreadsheet of GmSc xW, xL, xND smoothed weights

The actual results closely match what we would expect: higher game scores result in a high likelihood that the starting pitcher records a win; lower game scores result in a high likelihood that the starting pitcher records a loss; and average game scores have roughly an equal chance to result in a win, loss, or no-decision.

To calculate a starting pitcher’s expected win, loss, and no-decision percent, it simply requires averaging the expected win, loss, and no decision percent for each game score value that pitcher recorded. The chart below shows what this would look like for a pitcher with three starts and game score values of 57, 65, and 28.

Using these expected percentages, it is easy to calculate each starter’s expected wins, losses, and no-decisions by multiplying the average (expected percentage) by the number of games started. For the example above, 3  x .335 = 1.0 xW, 3 x .373 = 1.1 xL, and 3 x .292 = .9 xND.

Below is a table of all starting pitchers with at least 10 games started in 2015. You can sort by the difference to evaluate the luckiest and unluckiest starters in terms of wins and losses. Darker red shading indicates a pitcher was lucky and darker blue indicates a pitcher was unlucky. You will need to download or copy the data to be able to manipulate it on your own.

Link to spreadsheet of 2015 SP GmSc xW, xL, xND

The Lucky

• Collin McHugh outperformed his expected wins more than any other starter on both a percentage and counting basis. His actual record of 19-7 was much better than his expected record of 12-10.
• Nathan Eovaldi was among the luckiest starters in outperforming both his expected wins and expected losses. His actual record of 14-3 was much better than his expected record of 6-7.
• Drew Hutchison, like Eovaldi, was among the luckiest starters in outperforming both his expected wins and expected losses. His actual record of 13-5 was much better than his expected record of 8-13.
• Michael Wacha outperformed his expected wins with the fifth highest win percentage difference. His actual record of 17-7 was much better than his expected record of 12-9.
• Colby Lewis also outperformed his expected wins. His actual record of 17-9 was much better than his expected record of 12-12.

The Unlucky

• Chris Bassitt was the unluckiest starter in all of baseball on a rate basis last year as he led all starters in win percentage difference and loss percentage difference. In 13 starts, Bassitt had a record of 1-8 which was much worse than his expected record of 5-4.
• Shelby Miller was the unluckiest starter on a counting basis as he significantly underperformed both his expected wins and expected losses. His actual record of 6-17 was much worse than his expected record of 13-10.
• Corey Kluber was nearly as unlucky as Miller and had the highest difference between his actual losses and expected losses of all starters. His actual record of 9-16 was much worse than his expected record of 15-8.
• Scott Kazmir was among the unluckiest starters in underperforming wins. His actual record of 7-11 was much worse than his expected record of 12-10.
• Max Scherzer was among the unluckiest starters in underperforming losses. His actual record of 14-12 was much worse than his expected record of 18-7.
• Jesse Chavez was also among the unluckiest starters in underperforming losses. His actual record of 7-15 was much worse than his expected record of 9-10.
• Three potential fantasy sleepers also appear near the top of the unlucky list. In 16 starts, Raisel Iglesias had a 3-7 record compared to his expected record of 7-5. In 17 starts, Kevin Gausman had a record of 3-7 compared to an expected record of 6-6. Lastly, in 20 starts, Justin Verlander had a record of 5-8 compared to an expected record of 9-6.

Other Outliers

• Ivan Nova took a decision in all 17 of his starts to finish with an actual record of 6-11 compared to his expected record of 5-7.
• Chase Anderson was nearly the opposite of Nova as he recorded a decision in only 12 of his 27 starts with an actual line of 6-6 compared to his expected record of 9-10.
• Kyle Hendricks also recorded seven more no decisions than expected. In 32 starts, his actual record of 8-7 compared to an expected record of 12-11.

The Closest Match

• Jordan Zimmermann was the starter whose average win percent, loss percent, and no decision percent had the smallest absolute difference, giving him the dubious distinction of being this system’s most accurately evaluated starter. His actual line of 13-10 matches favorably to his expected line of 13-11. It looks even closer when looking at decimal values: actual wins 13, expected wins 12.6; actual losses 10, expected losses 10.5; actual no decisions 10, expected no decisions 9.9.

If you are interested in how well the xW, xL, and xND percentages correlate year to year, the answer is not well at all. Comparing the expected win and loss percent in year one to the actual win and loss percent in year two shows practically no correlation. The expected percentages and the calculations used above are much more useful when relegated to evaluating past performance.

That said, there is one way that the expected percentages are predictive. In all cases I looked at over the past three years, the outliers (both lucky and unlucky) regressed toward the mean in such a way that no one showed up on the same over or underperforming list two years in a row. Thus, the featured lucky pitchers (in gaining extra wins or avoiding deserved losses) will be hard-pressed to match their luck again this year while the unlucky players (in gaining extra losses or avoiding deserved wins) should fare better this year.

Introduction

The Arsenal score is a metric which can examine how effective a pitch currently is, or how effective it could be. This metric is compiled from z-scores (a statistical measure of how far above, or below the mean a specific value is) of ground ball and swinging-strike rates (Sarris, 2016). Eno Sarris put this metric together to see which players might be on the verge of a breakout, should they figure out control issues, improve their fitness and last longer in games. Eno has used the Arsenal score to rank pitchers from the 2015 season, proposing that pitchers like Chad Bettis, Rich Hill, and Raisel Iglesias are on the verge of a breakout.

My colleague Dan and I built the Stuff metric for a couple of different reasons. The first, and yet to be completed, was to look at how a pitcher’s stuff could influence their risk of injury. The second was for a similar reason as to the development of the Arsenal score – how can we possibly find players who have electric “stuff”, yet are a mere tweak away from major-league success. The Stuff metric is developed in a similar fashion to the Arsenal score – we look at the z-scores of a pitcher’s velocity, change of velocity, velocity of breaking pitches, and amount of break (Sonne & Mulla, 2015). However, unlike the Arsenal score, we have no indication as to how these pitchers are influencing the hitter – if they are causing swings and misses, or if they are inducing ground balls. In a sense, this is a weakness of the Stuff metric compared to the Arsenal scores, but it could possibly be used sooner than the Arsenal score – as minor-league parks install PITCHf/x systems and other tools for measuring pitch movement and velocity. Using the Stuff metric, we’ve proposed possible 2016 breakout pitchers like Chris Bassitt and Mike Foltynewicz.

These two metrics try to get at similar answers, but go about it in a different manner. For this analysis, I wanted to see how these two metrics could be combined to predict pitcher success.

Methods

I used the Stuff metric calculated for 2015 pitchers (found here) and the Arsenal scores for pitchers in 2015 (found here). In both evaluations, a pitch had to be thrown 100 times to be eligible for further analysis. In total, 138 different pitchers were included in this analysis. To see how both new pitching metrics performed (Arsenal scores and Stuff), I calculated the R² between the metric and ERA, xFIP, K/9, and WAR. These result values were obtained from FanGraphs. To see how the combined metrics worked to predict pitcher performance, I used a multiple regression analysis, and developed separate equations for each of the FanGraphs result values, using the sum of Arsenal scores and Stuff value as inputs.

For further analysis of the combined metric model, the difference between predicted values and actual values was calculated for ERA, xFIP, and K/9. This analysis did not include WAR, as to allow for equal comparison between players who played different numbers of games.

Results

Model Performance

In general, the Arsenal score was a better predictor of pitcher performance than Stuff. Arsenal scores had higher R² values when predicting xFIP, WAR and K/9, with Stuff having a slightly higher R² value for ERA (Table 1). The new combined model was a better predictor than either metric alone, with the greatest improvement seen for WAR (an 11% increase in explained variance compared to a single input variable).

The combined Arsenal-Stuff model performed the best when predicting xFIP (accounting for 46% of the variance in xFIP). Predicted vs. actual values can be found in figure 1 for all result variables.

Table 1. R² values between the input variables of Stuff / Arsenal Score, and result values of ERA, K/9, WAR, and xFIP. R² values are also presented for the combined model, which uses both Arsenal Score and Stuff as an input.

Figure 1. Relationships between predicted K/9, ERA, WAR, and xFIP and actual values. All predicted values are determined from a model that uses both Arsenal scores and the Stuff metric.

Player Identification

As a post-hoc analysis, I calculated the difference between predicted values and actual values. For ERA and xFIP, a lower value indicated the player’s predicted ERA or xFIP was lower than their actual results, which, could indicate that the player may perform better in 2016. A higher value may indicate that the pitcher may not have as favourable of results in 2016. The analysis is the opposite for K/9 – with higher values indicating that the pitcher should be expected to strike out more batters in 2016.

Table 2. The top 10 and bottom 10 predicted ERA errors. The top 10 represents pitchers who can be expected to have better results in 2016, with the bottom 10 predicted to perform with less success in 2016.

Table 3. The top 10 and bottom 10 predicted xFIP errors. The top 10 represents pitchers who can be expected to have better results in 2016, with the bottom 10 predicted to perform with less success in 2016.

Table 4. The top 10 and bottom 10 predicted K/9 errors. The top 10 represents pitchers who can be expected to have better results in 2016, with the bottom 10 predicted to perform with less success in 2016.

Discussion

This new model which incorporates both the Stuff metric and the Arsenal score improves predictions of ERA, xFIP, K/9 and WAR. By combining both of these metrics, the new model incorporates both the action of a pitch, plus the ability of a pitcher to induce swings and misses and ground balls.

Examining the player rankings to determine which pitchers are both under-performing and over-performing based on the new model’s predictions, there are some interesting names that show up. Carlos Carrasco appears to be due for improvement based on ERA and xFIP. Matt Moore is slowly returning from injury, but could see improvements in 2016 based off of his Stuff and Arsenal scores.

While pitchers like Zack Greinke, David Price, and Dallas Keuchel appear on the list of pitchers who could see regression in 2016, this is more due to the fact that they had otherworldly, perhaps outlier seasons, than it is a commentary on them pitching above their ability. Zack Greinke has gone on the record saying that his 2015 season was an outlier, and “that he may not actually be that good (Rodgers, 2016)”.  For Blue Jays fans, it is exciting to see how Aaron Sanchez’s stuff predicts he will have a better K/9 next season – though it’s to be seen whether he will pitch as a starter or reliever.

This model, much like the previous evaluations of Stuff and Arsenal scores, does not factor in control, deception or pitch sequencing. While model performance is strong, there is room for improvement of greater than 50% of explained variance. Pitching is complicated, and to achieve better predictions, models will need to grow increasingly complicated.

Conclusion

The combined Stuff/Arsenal score model improves predictions of ERA, xFIP, K/9 and WAR over the individual metrics on their own. This model was used to identify possible candidates for improvement and regression in the 2016 season. Future work should include a variety of more complicated measures to account for control, deception and additional game factors.

References

Rogers, J., 2016.  Zack Greinke on furthering his 2015 domination: ‘I’m probably not that good’. Retrieved from:

http://www.sportingnews.com/mlb-news/4695603-zack-greinke-stats-diamondbacks-projection-cy-young-chances, on February 21, 2016.

Sarris, E., 2016. The Change: Arsenal Scores. Retrieved from: http://www.fangraphs.com/fantasy/the-change-arsenal-scores/, on February 2, 2016.

Sonne, M.W., and Mulla, D., 2015. Revisiting the “Stuff” Metric. Retrieved from http://www.mikesonne.ca/baseball/22/, on December 21, 2016.

Additional Information

Difference between predicted and actual values – all pitchers included in the analysis.

Dynasty leagues are a little bit like the stock market.  What makes a good owner is finding things that may go up in value; this can be players, draft picks, or even money (not all leagues allow trading money, but ours does).  When you find a player that you think will go up in value you try to trade for him, pick him up, or draft him.  Anyone can sign good players in the auction for a lot of money, but what sets the good teams apart is their ability to find the players that are going to go up in value, or as we say “break out.”  I’m a fan of David Peralta.  He has already made quite an impact for teams in 2015.  Hitting .312/.371/.522 will do that.  The good thing for us is that he is not being properly valued right now in fantasy baseball leagues.

Now is the time of the year for rankings.  Every single site out there is coming out with their rankings getting everyone all set for their leagues.  Thankfully, we have sites like FantasyPros to get us consensus rankings and average draft positions.  Right now experts rank David Peralta an average of 40th among outfielders.  He is being drafted on average as the 38th outfielder off the boards.

David Peralta came up as a starting pitcher with the St. Louis Cardinals.  After multiple shoulder injuries he decided to bow out and head back home to Venezuela where he remade himself into an offensive player.  After an impressive year in an independent league he was signed by the Diamondbacks.  He quickly shot up through the system, learning fast for a player already in his mid 20s.  He’s only had a year and a half in the big leagues now, but he’s still been improving.  From what I understand, he is a very hard-working and upbeat player.

Numbers?  How about an improved hard-hit rate, going from 30% in 2014 to 35% in 2015?  A wRC+ jump from 110 to 138?  A HR/FB jump from 9.6% to 17.7%?  Even within 2015 he improved all three of those stats, getting up to a 38% hard-hit rate and a 162 wRC+ in the second half.  That’s destroying the baseball.  He’s spend most of his time batting fourth behind Goldschmidt and Pollock, so the RBI opportunities will continue.

You want to know what the most shocking thing is?  He only started 116 games.  The logjam in the Arizona outfield was to blame.  Well guess what, Ender Inciarte is gone and Yasmany Tomas sucks.  David Peralta is going to have no problem being the permanent cleanup hitter.  If we just took his 2015 stats and ignored any improvement whatsoever and prorated them for a reasonable 150 games we would be looking at 79 runs, 22 home runs, 101 runs batted in, and 12 stolen bases.  That’s even giving him two whole weeks off.  If you bake in some improvement due to his second-half numbers it’s not very hard to see 25-30 home runs with 200 combined runs and RBI.  Those numbers look a lot like what we’d expect from someone like Ryan Braun, Adam Jones, or Matt Kemp, all of whom are going in the 15-25 range.

The only website I’ve seen give Peralta his due was ESPN when Tristen Cockroft put him 25th among outfielders.  So at the very least that means I am not the only one thinking this is a huge value opportunity.  For dynasty leaguers, you need to go out and get him now.  He’s more than likely got a nice cheap contract or he might even be available in an auction because someone didn’t think he’s worth keeping around.  Listen to me, get him now and lock him up.  It’s a done deal.  Guess what, I’ve already done that in my league.  I traded Ken Giles ($1/3) for Peralta ($4/1) and a second-round draft pick back in November, so I put my money where my mouth is.  That was before Giles was in Houston and in our league contracts can be doubled up each additional year so I traded away about seven years of a top-10 closer for three or four years of Peralta and a second-round pick (for the minor-league draft).  But enough about me, don’t worry about my deal.  Go and get him.  Rarely are breakouts this easy to predict.

Justin Upton is a star outfielder, and recently he has joined fellow sluggers Miguel Cabrera, J.D. Martinez, and Victor Martinez on the Detroit Tigers. However, will pure offense be enough to fuel the Tigers’ run at the AL Central? Looking at the Central, there are many strengths and plenty of weaknesses for all five teams. For starters, the Tigers appear to be greater offensively than any other team. Upton, Cabrera, J.D. and Victor Martinez have all combined for 202 HR since 2014, meaning they each averaged approximately 50 HR each over the last two seasons.  The four sluggers also have a combined WAR of 28.4 over the last two seasons as well. Upton, despite being a major factor in that group (7.6 WAR, 55 HR since 2014) has struggled greatly against the American League over the last three seasons. Over the last three seasons Upton has played a total of 58 games against the AL, and in that time he has managed a miserable .205/.262/.338 slash line. His BABIP is a meager .252 over that stretch, and his OPS is just .600. It goes without saying that one of the bigger question marks for the Tigers will be whether or not Justin Upton will be able to adjust to American League pitching.

Ultimately, the Tigers offense should be solid, and the potential is there for it to be among the best in all of baseball. Upton should be a decent addition, however he needs to find a way to jump out of his three-season slump against the AL. That could prove very difficult. For starters, he will have to face the likes of Corey Kluber, Carlos Carrasco, Danny Salazar, Chris Sale, and Carlos Rodon at least a handful of times in the AL Central. Those five are just the tip of the iceberg, since the Central features several other solid pitchers as well. Not to mention the strong bullpens possessed by both the Royals and Indians (2.72 ERA and 3.12 ERA respectively; 1st and 2nd in the AL).

Sticking with pitching, the Tigers still have big question marks in their own pitching staff. The Tigers were the 28th-ranked pitching staff overall in terms of ERA. The biggest question for the Tigers pitching staff is whether or not Justin Verlander can return to his previous self. Verlander improved a bit last season. In 15 starts after the All-Star Break, Verlander posted a 2.80 ERA, with a 4.5 K/BB ratio, and a 1.00 WHIP. Verlander will be extremely helpful to — potentially — another Detroit postseason run if he can continue with his recent trend. This does not solve the rest of the rotation’s problems, and the Tigers will need great performances from young pitching talent Daniel Norris, a bounce-back season from Anibal Sanchez, and at least some solid outings from veteran Mike Pelfrey. Pelfrey has been less than stellar his last three seasons; in 64 starts he has posted a record of 11-27 with an ERA of 4.94. Sanchez was nowhere near himself in 2015, as he posted a miserable 4.99 ERA, averaged 1.7 HR/9, and had an FIP of 4.73. Compared to his outstanding seasons in 2013 and 2014 (where he had a combined ERA of 2.92, and averaged just 0.4 HR/9) Sanchez was a completely different pitcher. The one bright spot, perhaps, is newcomer Jordan Zimmermann. Zimmermann was not quite as sharp in 2015 as he has been previously in his career, but he’s still an extremely solid addition nonetheless. Hopefully for Detroit he can return to his previous form, as he was dominant in 2013 and 2014 going 33-14, with a 2.96 ERA and a 5.0 K/BB ratio.

Quite possibly the most important upgrade the Tigers made was to their bullpen. Last season the Detroit bullpen was less than stellar, posting a 4.38 ERA, 1.44 WHIP, and a meager 1.95 K/BB ratio. There was no argument needed to show that their bullpen was clearly the worst in the AL Central. However, the 2016 outlook looks a lot brighter. The Tigers brought in Mark Lowe, Francisco Rodriguez, and Justin Wilson to bolster their ranks. Combined, the trio posted a 2.44 ERA and 1.02 WHIP in 2015 (K-Rod also had 38 saves in 40 opportunities). It goes without saying that these three will be a big boost to an otherwise abysmal bullpen.

2016 will be a defining year for the Tigers. Will their offense really be all that it seems? Will the bullpen be good enough to keep them in games? Can Verlander and Zimmermann carry the starting rotation? Will Anibal Sanchez be able to bounce back? Will Justin Upton be able to adjust to the American League? All these questions will be answered soon enough. Detroit has quite a bit of talent and for their sake everything needs to work like a clock if they want to have any chance at contending for the Central title and make another pennant run.

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.

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.

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.