This article was originally published at Birds on the Black, a St. Louis Cardinals blog. You can find the blog (@birdsontheblack), author (@zjgifford), and artist (@cardinalsgifs) on Twitter.

Back in November, the FanGraphs staff ranked Marcell Ozuna as the seventh-best available free agent. In that article, Kiley McDaniel and the FanGraphs crowd both expected Ozuna would receive a four-year deal, with the median crowdsource estimate coming in at $64 million ($16 million per year) while McDaniel was a little higher at $70 million ($17.5 million per year). Teams could dream on Ozuna’s potential and a return to his 2017 production with relatively minimal risk — since his 2013 debut, Ozuna has consistently produced as an average or better player. Coming into this offseason, he had produced more than 2 WAR in four straight seasons dating back to 2016. Free agents are never a sure bet, but Ozuna seemed pretty close to one at a reasonable price.

Ozuna ended up betting on himself by taking a one-year deal with Atlanta, which was a bit of a surprise. With that context, I wanted to see what happened during his breakout 2017 campaign and what might be holding him back from tapping back into that potential. We’ll start with some numbers: Read the rest of this entry »

I recently began thinking about how teams can know that they are efficiently spending their money, or where teams actually get the runs that they spend all their money on. With players signing massive contracts in the 2018-19 offseason, I began to wonder if any players were really worth that much money. The process begins with one big question: What is a run worth? I quickly realized that each team theoretically needs to manufacture the same number of runs as all the other teams do if they want a better chance to make the postseason. What is different from team to team is budget. This means that a run is worth a different monetary value to each team, and that each team would be willing to pay a different amount of money for the same number of runs. The problem is that to each player, a run costs the same amount, causing Billy Beane, played by Brad Pitt in the movie Moneyball, to claim that “It’s an unfair game”.

Figuring out what each team values their runs at would enable me to evaluate how efficient the signing of certain contracts was for each team and furthermore would allow me to figure out where the most value comes from in the payroll of a team. First, I had to figure out how to convert the basic statistics of a player into the number of runs that player actually contributed to the team. I eventually came across the Estimated Runs Produced statistic from the 1985 Bill James abstract. Below is the calculation.

ERP = (2 (TB + BB + HBP) + H + SB – (.605 (AB + CS + GDP – H))) .16

This is a stat created by Paul Johnson in order to obtain more accuracy than Runs Created, which he succeeded in doing. I then fired up R and ran some tests on team statistics to see how well it lined up with the actual number of runs that each team scored. I graphed ERP against Runs Scored first for every team dating back to the beginning of the 30-team era in MLB: Read the rest of this entry »

As much as baseball fans would like a simple explanation for the astronomical increase in home runs in 2019, it is becoming clearer that many factors have played into the surge. Among the possible reasons are batters prioritizing hitting homers more than ever before, pitchers having difficulty gripping the seams of the baseball, and of course the famous “juiced balls.” Last month, a committee released initial results of a comprehensive study attempting to determine the driving forces behind the home run rate growth.

I am particularly interested in the idea that fly balls were supposedly carrying more in 2019. On multiple occasions throughout the year, I listened to announcers observe that outfielders seemed to be severely misjudging fly balls. For instance, the center fielder would be drifting back toward the wall, as if he had a bead on it, and the ball would end up 15 rows deep. Although this may seem like evidence for increased carry of the baseball, such observations can easily be driven by confirmation bias. There was a tendency this year to believe that every ball in the air would be a homer, so when a ball would carry a lot, it fit with expectations and the belief continued to grow. It may just have simply been the case that the wind was blowing out that day, or that the batter struck the ball in a particular way, and the carry had nothing to do with the ball itself. To determine if the perception was in fact reality, I focus on the following question: Did similarly struck balls travel farther in 2019 than previous years? Read the rest of this entry »

The 2019 season was a respectable, if not particularly remarkable year for the Texas Rangers. Following a 95-loss campaign that cost manager Jeff Banister his job, the Rangers bounced back to a 78-84 record, nowhere close to a wild card berth but nonetheless good for third place in a loaded AL West. On the whole, it’s not a stretch to say it was a successful season in Arlington — 78 wins is an impressive total for a team that lost its two best hitters in July and had exactly two non-replacement-level starting pitchers.

That doesn’t necessarily mean they were fun or interesting. Outside of Lance Lynn and Mike Minor possibly breaking WAR, the most curious thing about the 2019 Rangers may have been the truly out-of-the-blue breakout of Danny Santana. In a nutshell, after a 4-WAR debut in 2014 bolstered by a .405 BABIP, Santana appeared to be an afterthought unlikely to return to being a big-league contributor, checking in at well below replacement level over the subsequent four seasons split between Minnesota and Atlanta. Santana signed a minor league deal with Texas this past January, rewarding them with a 28 HR/21 SB season, slugging .524, and posting a 111 wRC+ across 511 plate appearances, all while playing every position on the diamond past the pitcher’s mound. Quite the turnaround!

Then again, one can’t be blamed for not paying much attention to what Jay Jaffe called “one of [2019’s] most unlikely breakouts.” In the year of the juiced ball, a light-hitting utility guy more than doubling his career home run production wasn’t as newsworthy as it ordinarily would be. Besides, most observers appear inclined to believe that 2019 was more flash in the pan than an All-Star leap. Jaffe concluded that “ability to hit pitchers of both hands will keep him relevant on a daily basis,” while remaining skeptical that another 28-homer performance or .352 wOBA output is in the cards. “A high BABIP paired with a high strikeout rate and a sudden burst of power screams regression,” Jake Mailhot recently opined. Even Rangers blogs are less than sold on his place on the team going forward. Read the rest of this entry »

Last month, the Tampa Bay Rays signed Japanese slugger Yoshitomo Tsutsugo (筒香 嘉智) to a two-year contract for $12 million. If you add the $2.4 million posting fee paid to the Yokohama Baystars, Tsutsugo’s team in the Japanese League, that would make the Rays’ investment at $14.4 million total for two seasons. The 28-year-old left fielder has been expressing his ambition to play in the majors for years, and he finally found the team to play for. Now the question is who this guy is and how he will fit.

Background

Tsutsugo has been one of the top prospects of Japanese baseball since his younger days. He was one of only two freshmen to hit cleanup in his high school team’s history. In his sophomore year, Tsutsugo led his team to the semifinal in Koshien, the biggest high school baseball tournament in Japan, with a .526 batting average, three home runs, and 14 RBIs in three games. It gained him enough attention to play on his country’s national team. In 2009, Tsutsugo was drafted by the Baystars as the first pick in the Nippon Professional Baseball (NPB) League.

Tsutsugo struggled in Japan until 2014. He struck out too much, and frequent injuries prevented him from playing full-time. Until 2014, he only had one season with more than 100 games played (NPB plays 144 per season). However, Tsutsugo started filling up his minimum at-bats, and his OPS has been over .900 every year since. The peak was 2016, when he hit 44 home runs in 133 games with an OPS of 1.110. He also played for Japan in the 2017 World Baseball Classic as the cleanup hitter and proved his power with three home runs and a .680 SLG in seven games. Throughout his nine-year career in Japan, Tsutsugo hit .285/.382/.528, good for a .910 OPS. The average OPS during that time was around .680 to .720. Yokohama’s superstar was truly one of the league’s elite power hitters. Read the rest of this entry »

Do you like to gamble? The Seattle Mariners do. A lot. In fact, Seattle has chosen to introduce substantial risk into its relationship with Evan White this offseason, with little additional expected return. Given all of the action so far this offseason, you could be forgiven for paying little attention to this particular transaction back in November. But it’s an unusual type of deal that some analysts believe could become more common in future years, and it raises some thorny questions about financial risk management.

When I first read reports that some players were criticizing White — a 23-year-old first-base prospect who has never played above Double-A — for signing a long-term contract with the M’s, I was a little taken aback. My initial reaction to the deal had been the opposite: I couldn’t understand why the Mariners would lock themselves into paying a minimum of $24 million to a player who had never taken an at-bat in Triple-A, much less the majors, and who they would have had team control over through at least his age-29 or age-30 season (depending on when they call him up) in the absence of any long-term contract. If the Mariners simply played it year-by-year with White and he ends up being an above-average major leaguer — or even a star — they could expect to pay him somewhere in the range of $24 million through his six years of pre-arbitration and arbitration years. And if White ends up being a complete or near-complete bust (as is quite possible), the Mariners could have cut him loose while paying him a negligible sum.

And from White’s perspective, if he takes a cold, hard look at the numbers, the probability of him making less than $24 million in his career absent this contract is quite high. Some research indicates that the bust rate for hitters ranked in the bottom half of top 100 prospects and assigned an OFP of 55 on the scouting scale (as Baseball Prospectus did this offseason) is as high as 30-40%. If I’m Evan White, and I assess that there is a greater than, say, 1-in-4 chance that I end up making no more money in my baseball career, you don’t have to ask me twice to sign a contract that guarantees me somewhere between $24 and $55 million. I’m popping the champagne that night and paying for all of my friends to join me on a celebratory trip to Vegas. Read the rest of this entry »

Although he may not have been one of truly few elite free agents on the market, Zack Wheeler was a very big signing for Philadelphia, agreeing to a five-year contract worth $118 million. Over the past two years, Wheeler’s amassed an fWAR of 8.9 in 377.2 innings while posting above-average strikeout and walk rates. Additionally, his underlying metrics have also been strong over the past two seasons, and they don’t signal any drastic mean reversals in performance. Granted, there is obviously still risk here. Wheeler missed the 2015 season and the start of 2016 with a UCL tear, and he was shut down upon his return with a flexor strain. Furthermore, this past July he was shut down with shoulder fatigue, limiting the Mets’ ability to market him to potential suitors at the trade deadline.

One of the most interesting storylines in the game is player development. At the moment, the most analytically inclined teams are thriving at meshing the data with coaching, and the gap is only growing. These teams are creating new players. This is particularly important when signing free agents given the current contract negotiation dynamics. The teams and players have access to most of the same information, with the $/WAR metric playing a central role in future valuations, and if you can “create” a new player who beats the projections, you’re generating additional value for your club.

In terms of Wheeler, I think I have a bit of a theory on how to generate that marginal value through a tweak in his approach. The biggest change in Wheeler’s approach between 2018 and 2019 was in his usage of his fastballs and his changeup. In 2018, the righty threw his four-seam fastball almost three times as frequently as his two-seamer, and his changeup was almost almost non-existent. (Disclaimer: I believe that there may be an error in Statcasts’ classification of Wheeler’s splitter and changeup — they may actually be the same pitch). Read the rest of this entry »

I’d like to highlight a bias within xWOBA that could possibly be accounted for to improve the metric. In my view, however, the more interesting takeaway is the “why” behind what is happening and how this might be used from both a player evaluation and player development perspective.

The bias in xWOBA is found in the amount of backspin on hit balls. For spin, I created an expected distance model based on Exit Velocity (EV), Launch Angle (LA), and Horizontal Hit Direction or Spray Angle. This model has been helpful in assessing whether players should hit with backspin (article here) as well as changes to the ball and the amount of drag (article and model here). Alan Nathan and Tom Tango have pointed out that very-high-spin balls actually have increased drag and less distance. However, what happens at the high end of the spin spectrum does not interfere with the low end; thus, the general conclusions that follow would appear to remain valid. Additionally, while knowing actual spin values might help confirm the findings, it’s not just the spin rpms that are relevant, as the spin type (based on the spin axis) must also be considered. Rolling all this information up into an overall player average for a comprehensive cost/benefit analysis will likely prove challenging as spin axis values don’t average well.

The general takeaway from the research on whether players should hit with backspin is that backspin balls outperform expectations but the players that hit backspin balls more often actually underperform. That may seem a little counterintuitive at first; however, this relationship is clearly visible in the xWOBA player averages based on the data: Read the rest of this entry »

The Louisiana State University Shreveport Pilots are an NAIA team in Shreveport, Louisiana competing in the Red River Athletic Conference. This article was written by Brent Lavallee and David Howell. Brent Lavallee is the Head Coach of the Pilots and David Howell is the Director of Player Development, Director of R&D, and Assistant Pitching Coach.

Introduction

With the rise of affordable bat sensors, we no longer have to rely on only the eye test when it comes to evaluating swings. Gone are the days of attempting to evaluate a hitter’s progress based on the small sample of fall games, or how well they seem to be hitting flips at the end of the season. Even the days of measuring exit velocity during tee work with a radar gun are comparatively basic with what can be accomplished with a sub-$200 Bluetooth sensor.

At LSU Shreveport, we started using Blast Motion sensors this fall, which are placed on the knob of a bat and measure metrics such as bat speed, attack angle, rotational acceleration, and more. The sensors work by taking into account the characteristics of a bat (length, weight, etc.) and derive swing metrics when hitters make contact. Read the rest of this entry »

With the 2019 season firmly in the books and the expanded offering of spin-related pitching data now readily available across the internet, I decided it was time to take a hard look at every team’s pitching staff. The hope in doing so was to identify a trend, if any, within the spin metrics of the best clubs. Do any staffs have a noticeable tendency to use pitchers with a specific spin profile?

To answer this, I pulled together every pitcher and their average spin metrics for each pitch type that they threw a qualified amount of times (30-plus in most cases) in 2019. This meant ignoring splitters because of sample size considerations. I was also tempted to use Bauer Units — a proxy for spin rate divided by velocity, as well as a nod to Trevor Bauer — to control for velocity in this study, but I decided to keep this post more straightforward. The study instead uses raw spin rate, horizontal and vertical movement, and spin efficiency as reported by Baseball Savant. I then aggregated the players’ data by the team they finished the season with to create an average spin profile for every team. This team profile weighs all of their qualified pitchers equally.

Once I was able to establish what the normal team looks like across those categories, I wanted to identify any clear outliers to possibly show where organizations consciously emphasized certain metrics. To do that, I produced league rankings and standard deviations for each category based on the team averages. Read the rest of this entry »