Archive for Player Analysis

Brandon Phillips was a great baserunner this past season. He stole 23 bases and was only caught stealing three times. It wasn’t an all-time great season in terms of stolen bases or baserunning runs overall, and his baserunning is overshadowed by the baserunning greatness of teammate Billy Hamilton, but we can all agree that Phillips put together a very nice season on the basepaths.

Now let’s make things interesting. In contrast to his great 2015, Brandon Phillips was very bad at stealing bases the last few years. In 2013 and 2014 he combined for a grand total of seven stolen bases and six times caught stealing (Phillips in fact had negative net stolen bases in 2014, being caught stealing three times and stealing just two bases), being worth negative runs on the basepaths both years. We now have a rare situation on our hands, where a player was a prolific base-stealer after doing nothing the year before.

Let’s quantify Phillips’ improvement to find some historical comparisons. Here’s the complete list of players that increased their stolen-base total by at least 20 a year after having negative net stolen bases (stolen bases -t imes caught stealing):

I know it can be difficult to read through that entire list, so let me summarize it for you: Before Brandon Phillips in 2015, no player had ever, following a season with negative net stolen bases, increased their stolen-base total by over 20 in the following season!

Pretty cool, right? It gets even better!

Here’s what makes Brandon Phillips’ 2015 season on the basepaths even more unique. Brandon Phillips was also very old this season, turning 34 in the middle of the summer. While it’s not unheard of for old guys to steal lots of bases (Lou Brock stole 118 at 35), it is a lot rarer than players in their primes stealing lots of bases. What is very rare is for old guys to suddenly make a leap in their stolen-base totals.

Let’s go back to the numbers again to find some historical comparisons. Here is the complete list of players who had a 20-stolen-base increase at Brandon Phillips’ age or older since baseball became integrated:

Only five other players since integration have had a 20-stolen-base jump at Brandon Phillips’ age or older. And these aren’t any random players — with Brock, Henderson, Wills, and Campaneris on the list, you have the 1st, 2nd, 14th, and 20th career leaders in stolen bases. The 5th is Jose Canseco, which just confirms what we already knew: Jose Canseco is weird. Canseco’s performance late in his career was also famously PED-boosted to defy normal aging curves, but I decided to just present the stats to you and you could make your own judgment on which performances you consider legitimate.

Even compared to the four all-time great base-thieves and Canseco, Phillips’ 2015 season is still unique. Since integration, Brandon Phillips is the only player his age to ever have an increase of 21 in stolen bases while matching his success rate!

If you had predicted before the season that Brandon Phillips would steal less than 23 bases, no one would have doubted you. After all, 18,845 players have played major-league baseball before and not a single one had accomplished what Brandon Phillips needed to do.

However, as the saying goes, baseball is played on the field and not on a computer. Against all odds there was old Brandon Phillips, chugging along on the basepaths and making his mark in history while doing it.

Notes:

(1) I used a cutoff of 200 at-bats in each consecutive season for players to qualify for the stolen-base-increase list. This was because I wanted the increases in stolen bases to be due to the player’s actions, and not just more playing time. A season where a rookie is called up and steals two bases in five games, and then steals 50 bases in a full season the next year is obviously against the spirit of seeing which players increased their stolen bases the most. I generously made the cutoff to qualify very low to include as many players as possible and so I couldn’t be accused of cherrypicking an at-bat limit to help Brandon Phillips stand out.

(2) A lot of players in the 1890s and 1900s qualified for the 20+ stolen-base increase at 34 years old or later, but since the game was so different back then I decided to just compare Phillips against players from the modern era.

(3) Dave Roberts came close to making the second cutoff, but was just a bit younger than Brandon Phillips.

Oftentimes, preconceived notions inhibit our understanding of the game of baseball. From archaic methods of player evaluation to cultural expectations of players of varying ethnicity, each observer’s individual paradigm dramatically alters his or her view of the game.  Case in point, what common ground could Salvador Perez and Kevin Pillar possibly share beyond their profession? Perez, who recently inked a new contract extension with the Kansas City Royals, stands at a booming 6’3’’ and 240 lbs. Pillar measures in at a more svelte – for professional athletes, at least – 6’0’’ and 205 lbs. Perez signed with the Royals in 2006, at the age of 16, as an international free agent out of tumultuous Venezuela before Pillar had even graduated from Chaminade College Prep, a private Catholic school in San Fernando Valley. Pillar finally signed his first professional contract after being drafted by the Toronto Blue Jays in the 32^nd round of the 2011 amateur draft, less than a month before Perez debuted in the Majors despite being two years Pillar’s younger. Examined from a cultural and personality standpoint, Perez and Pillar seem polar inverses of one another.

Herein lies the beauty of baseball, and sports in general – citizens from all walks of life can come together, abandon their differences, and enjoy a common passion. From first pitch to the final out, no one differentiates between the hulking, affable Venezuelan catcher and the agile, analytical outfielder. Indeed if you only considered their projected on-field contributions, you may discover them indistinguishable.

Projections courtesy of FanGraphs’ Depth Charts, a combination of ZiPS and Steamer, provide us our best estimate of a player’s “true talent” level. No, projections are not infallible, but for this exercise they convey more than enough. Perez and Pillar share striking similarities in their statistical profiles. Solid defense up the middle, meager walk rates complemented by above-average strikeout rates; even the “old school” stats and classic triple-slash lines bear remarkable resemblance. The summary stats further these parallels; both players project around 3 fWAR for the upcoming season, while only one point of wOBA separates them. The only appreciable area of separation resides in base-running, where Pillar’s stolen bases give his BsR a four-run edge over Perez’. Otherwise, Pillar and Perez mirror each other with regard to their contributions on the diamond – the only facet we should judge players by. Perhaps more compelling, their overall approach. The below table lists each players’ plate discipline statistics from the 2015 season, as found on FanGraphs.

Both player profiles match what we should have expected given their walk and strikeout rates above: free swingers, particularly at pitches outside the zone, with an above-average ability to make contact. (Statistically speaking, among qualified batters Perez and Pillar both rank in the top quartile in Swing%, the 96^th percentile in O-Swing%, and top half in Contact%). Nonetheless, the proximity of their plate-discipline statistics encapsulates how comparably Perez and Pillar approach an at-bat.

Having not revealed the identity of the two stat lines* illustrates one of the charms of the game. No matter the background, personality, religion, whatever you may have, we convene to cherish a game we love. With an abundance of animosity arising over “playing the game the right way”, cultural lines oftentimes artificially divide us. We can choose to continue making these superficial discrepancies, or we can focus on what ultimately matters most, the product on the field and the joy it brings to our lives.

*For the curious (and spoilers for those who prefer the anonymity):

Player A – Salvador Perez, Player B – Kevin Pillar

Howie Kendrick is not the model of league-average consistency he seems like at first blush.  Kendrick is basically washed up.  Last year he posted numbers that would appear consistent with his performance since 2011:  BB% in the mid 5’s, K% in the mid-to-high teens, BABIP over .340, ISO hanging in at .114, and 2.1 WAR.  The plate-discipline numbers look stable, but the ISO and BABIP don’t.

The ISO was propped up by a 14.1% HR/FB that he is not going to repeat.  Last year he managed only a .114 ISO despite an elite FB distance of 305 feet, which was 14th-best in the majors.  His FB rate was the main culprit.  It has steadily declined since he arrived in the majors in 2007, bottoming out last year at 17%.  And he’s not going to have elite FB distance in 2016, and is unlikely to be anywhere close to his 2015 number.  He began his career in the low to mid 270s, peaked at 285 at age 28, and had been steadily receding back to the 270s until last year’s unlikely spike at age 32.  In all likelihood the 2015 number was driven by good fortune in a very small sample of fly balls.  Expect that number to be back in the low to mid 270s in his age-33 season.  If he hits the same number of fly balls in 2016 as he did in 2015, but his HR/FB% is cut roughly in half, he will hit 4-5 home runs.  Moreover, his 2015 hard-hit rate (29%) was in the bottom half for the first time in years, and his pull rate (27%) was a career low and good for third-lowest in the majors for all batters with at least 400 PA.  All of this points to an ISO below .100.

His BABIP won’t crater.  He doesn’t pop up and keeps the ball on the ground.  But his BABIP isn’t going to stay over .340 forever, and I would take the under in 2016.  Last year’s homers will be this year’s fly ball outs.  Overall, he’s not hitting the ball as hard.  Nor is he getting any faster.  And, because he can no longer pull the ball — particularly balls hit in the air — he should be getting easier to shift against.  Steamer’s projection of .324 seems about right.

Altogether, he’s looking at a .290-ish wOBA, bottom of the pile for regular second basemen.  Add in his projected league-average baserunning and defense, and he’s worth about 1 WAR.  Steamer has him at 2 WAR (based on a projected .316 wOBA); Zips projects 1.9 WAR (.317 wOBA); and the fans project 2.7 WAR (.322 wOBA).  These figures are double to triple what he is likely to produce.  Note, however, that the Dodgers are paying Kendrick $20 million for 2016 and 2017.  Assuming $8 million per WAR, the Dodgers are valuing him at only 1.25 WAR per season.  To no one’s surprise, it seems Friedman and company have this one right.  Also, Kendrick does have a career wOBA platoon split of .325 vs. righties and .340 vs. lefties.  One way to squeeze additional value from Kendrick (and keep him healthy) at this stage of his career might be a semi-platoon with Utley, who himself sports a career platoon split and projects better against righties than Kendrick.

The Premise: Byung-ho Park will be a very good, and potentially great, first baseman/DH as soon as this season.

The Format: A typical line of discourse between a Park believer — such as myself — and a Park-skeptic.

The First Argument: Park comes from a league with little track record of successful MLB transplants — after all, if Eric Thames can be a star, how good can the league be?

The Rebuttal: It is true that the Korean Baseball Organization (KBO) has sent very few players to the major leagues. However, consider these caveats before rendering judgement. Unlike in Japan, in which baseball has ruled supreme for decades, the sport has only really taken off in Korea in the last 20 years, spurred largely by the success of Chan-ho Park in Korea and then in the majors. Now, however, the country is baseball-crazy: their national team is among the best in the world and the KBO is by far the most popular professional sports league in Korea. This dramatic rise in interest has led to a correspondingly dramatic rise in baseball infrastructure as more talent is discovered and developed from an early age. The early success of Hyun-jin Ryu and Jung-ho Kang in the United States speaks to the ability of the Korean infrastructure to develop its top-tier talents. Korean national teams regularly beat Americans and others on the international stage. The notion that Korea is not on the same level as a baseball-playing nation as Japan, Cuba, the Dominican Republic et al. is a farce.

The Second Argument: Park strikes out too much to be an effective major-league player.

The Rebuttal:  There are two responses to this, one league-oriented and one player-oriented. Implicit in this argument is the notion that the KBO is sufficiently worse than the MLB that all numbers should be significantly adjusted to account for better pitchers in the MLB. While the average KBO pitcher is undeniably worse than the average MLB pitcher, it is worth noting that Cuban League pitching is also decidedly below-average (see this piece by BA’s very talented international correspondent Ben Badler), and Cuban hitters are being snatched up like airline tickets after a decimal point error.

Second, a look at Park’s past seasons reveals an interesting shift in approach. Park’s K% in 2012 and 2013 was 19.8% and 17.2%, respectively, and his slugging percentages were .561 and .602. In 2014, his slugging percentage jumped to .686, but his K% also climbed to 24.8%. Since strikeout rate is a stat which normalizes fairly quickly — 60 PAs, according to FanGraphs — and the overall KBO strikeout rate actually declined from 2013 to 2014 (from 17.3 percent to 16.7 percent), we have to assume that Park changed something in his approach.* My conclusion, given what we know about power hitters striking out more in general, is that Park decided to trade contact for power, much like Mike Trout did before the 2014 season. This is indicative both of Park’s recognition of his strengths as a player, which speaks to his baseball intelligence and ability to learn, and also to his adaptiveness at the plate. If he is striking out too much, I am confident that he can reorient his approach and still be a highly valuable player.

The Caveats: There is, of course, no guarantee that Park will succeed in Minnesota. MLB competition is significantly better than any other league anywhere and there will be a learning curve for Park as he learns to hit MLB pitchers. The steeper hurdle in my mind, however, is culture: American culture is very different from the Korean culture with which he is comfortable. Kang Jung-ho, thanks to no small helping of self-confidence, a good team environment, and a penchant for the dramatic, has thrived in Pittsburgh, but there is no guarantee that Park will adjust as successfully or as quickly.

The Conclusion: These caveats aside, drafting (or signing) Byung-ho Park is a risk worth taking. He will be cheap and the upside is enormous. Acquire Park with confidence; there is a good chance that in the not-so-distant future, both you and the Twins will be the proud owners of one of the best power hitters, and best bargains, in baseball.**

*KBO stats pulled from baseball-reference.com
**Read Dan Farnsworth’s recently published Twins prospect list for further analysis of Park

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.