The Playbook, Inning 8: Advanced stats to use for fantasy baseball

(The full, nine-inning Playbook was originally published in spring 2020. It has been updated for 2024 where applicable.)

Baseball is such a different game today than it was when rotisserie was first invented.

Back in 1980, most anyone interested in baseball was lured in by such “bubblegum card” numbers as batting average, home runs, wins and ERA. Over the years, the brightest minds in the game brought to light the fact that there were better ways to evaluate baseball players.

Today, we’ve got so many statistics to choose from that even advanced fantasy players might find themselves confused. Even turning on a broadcast might sometimes seem daunting, with recent statistical innovations as Exit Velocity, xwOBA or FIP casually being tossed about. Which of these matter for our purposes? And, perhaps more importantly, what the heck do some of these stats even mean?

Regardless of your experience level in fantasy baseball, a refresher (or primer for the newbies) can be immensely helpful. This edition of the Playbook dives deeper into some of the more modern metrics we use to evaluate players. They are separated into several different statistical categories below.

Statcast

It has been all the rage in baseball analysis, fantasy baseball and even television broadcasts during the past half-decade, but what, exactly, is Statcast?

Statcast is a data-tracking and collection tool that analyzes players’ skills, which began on a partial trial basis in 2014 and came to all 30 big-league stadiums in 2015. Initially, it used a combination of camera and radar systems, but in 2020, a sophisticated camera system called Hawk-Eye was installed in every big-league stadium, with 12 such cameras now in place at each venue. This data, in full, is only available for the past nine seasons (2015-23). MLB.com’s Statcast glossary provides more detailed information on how the system works, for those interested, but to summarize for fantasy purposes, Statcast provides us a way of scouting players by converting players’ raw abilities into statistics.

The easiest place to find Statcast data, in an easily sortable format, is on BaseballSavant.com. There, you’ll find leaderboards, reports on full player statistics, a search engine, individual player pages and a scoreboard that allows you to track player performance in real-time, among other tools.

Here are some of the key, fantasy-relevant Statcast metrics:

Exit Velocity (EV): This measures how fast, in miles per hour, a batted ball was hit by a batter. Ultimately, the harder a batter hits a ball, the less time the defense will have to react and the further it is likely to travel, both of which increase the chances of a positive result for the hitter. Therefore, when this metric is used to evaluate pitchers, lower numbers are more desirable.

A player’s Exit Velocity is most often referred to by the average of this number over all of what Statcast calls “Batted Ball Events,” or batted balls in play, which is his Average Exit Velocity (aEV). The league’s Average Exit Velocity in 2023 was 88.5 mph, and it took a 92.0 mph number for a player to place in the 90th percentile, with 86.6 mph placing him in the 10th percentile.

These were the top 10 in aEV among batting title-eligibles in 2023:

Ronald Acuna Jr., 94.7 mph
Shohei Ohtani, 94.4
Matt Olson, 93.7
Matt Chapman, 93.4
Yandy Diaz, 93.4
Corey Seager, 93.3
Juan Soto, 93.2
MJ Melendez, 93.2
Rafael Devers, 93.1
Julio Rodriguez, 92.7

J.D. Martinez (93.4) and Yordan Alvarez (93.3) were 23 and six plate appearances shy, respectively, from reaching batting title eligibility. Note that Statcast’s leaderboard sets its qualification threshold by the number of “batted ball events,” rather than plate appearances.

These were the bottom 10 in aEV among eligible hitters:

Andres Gimenez, 84.8 mph
Whit Merrifield, 85.1
Thairo Estrada, 85.9
Steven Kwan, 86.0
Jeff McNeil, 86.0
Ha-Seong Kim, 86.2
Myles Straw, 86.3
Dominic Smith, 86.3
Nico Hoerner, 86.6
Andrew Benintendi, 86.6

Esteury Ruiz (82.7) would have easily topped this list, but he finished five plate appearances shy of qualifying for the batting title.

Shifting to the pitchers, among the 127 who worked at least 100 innings last season, these were the top 10 in aEV allowed:

Nick Martinez, 84.7 mph
Kyle Hendricks, 85.2
Shohei Ohtani, 86.4
Corbin Burnes, 86.4
Michael King, 86.8
Zack Wheeler, 86.9
Tyler Anderson, 87.0
Pablo Lopez, 87.1
Blake Snell, 87.2
Paul Blackburn, 87.2

Conversely, these were the 10 worst pitchers in the category:

Shane Bieber, 91.6 mph
Framber Valdez, 91.5
Zac Gallen, 91.5
Andrew Abbott, 91.2
Taj Bradley, 91.2
Brady Singer, 91.0
Griffin Canning, 91.0
Hunter Brown, 90.8
Reese Olson, 90.8
Ryne Nelson, 90.8

Launch Angle (LA): This measures the vertical angle at which a batted ball leaves a hitter’s bat. A Launch Angle of zero degrees means that the ball left the bat parallel to the ground, while a 90 degree result would mean that the ball went straight up off the bat. As with Exit Velocity, Launch Angle is most commonly referred to by its average (aLA).

Launch Angle is one way that we can determine the type of batted ball, when examined individually. For example, a Launch Angle beneath 10 degrees is generally regarded as a ground ball, 10-25 degrees is considered a line drive, 25-50 degrees a fly ball and anything greater than 50 degrees a pop-up. Using averages, players with higher launch angles are generally classified as fly ball hitters (or pitchers), while those with lower launch angles are termed ground-ball hitters (or pitchers).

There were the top 10 batting title-eligible hitters in terms of average Launch Angle last season, along with their ranking in terms of fly ball rate:

Jack Suwinski, 22.4º aLA, 36.3 FB% (3rd)
Isaac Paredes, 22.2º aLA, 28.5 FB% (47th)
Max Muncy, 21.7º aLA, 38.5 FB% (1st)
Mookie Betts, 20.6º aLA, 35.7 FB% (5th)
Daulton Varsho, 20.5º aLA, 31.1 FB% (26th)
Cal Raleigh, 20.3º aLA, 35.1 FB% (8th)
Anthony Santander, 20.2º aLA, 32.5 FB% (18th)
Francisco Lindor, 19.2º aLA, 31.4 FB% (24th)
Marcus Semien, 19.1º aLA, 35.4 FB% (6th)
Kyle Schwarber, 19.0º aLA, 33.8 FB% (14th)

Next, here were the bottom 10 in Launch Angle:

Tim Anderson, 2.0º aLA, 10.7 FB% (lowest)
Christian Yelich, 3.5º aLA, 17.3 FB% (second-lowest)
DJ LeMahieu, 3.8º aLA, 17.4 FB% (third-lowest)
Masataka Yoshida, 3.9º aLA, 19.4 FB% (10th-lowest)
William Contreras, 4.7º aLA, 20.4 FB% (17th-lowest)
Orlando Arcia, 5.4º aLA, 20.8 FB% (20th-lowest)
Jeremy Pena, 5.5º aLA, 18.9 FB% (eighth-lowest)
Yandy Diaz, 5.7º aLA, 20.8 FB% (19th-lowest)
Maikel Garcia, 6.1º aLA, 18.3 FB% (sixth-lowest)
Bo Bichette, 6.2º aLA, 18.8 FB% (seventh-lowest)

Again using 100 innings pitched as our qualification threshold, here were the 10 pitchers with the lowest average Launch Angles in 2023, along with their fly ball rates:

Logan Webb, 0.6º aLA, 14.9 FB% (lowest)
Alex Cobb, 1.3º aLA, 16.3 FB% (third-lowest)
Marcus Stroman, 3.0º aLA, 16.2 FB% (second-lowest)
Framber Valdez, 4.2º aLA, 18.5 FB% (fifth-lowest)
Brayan Bello, 4.9º aLA, 20.4 FB% (ninth-lowest)
David Peterson, 5.3º aLA, 17.2 FB% (fourth-lowest)
Tyler Glasnow, 5.8º aLA, 23.2 FB% (27th-lowest)
Nathan Eovaldi, 6.4º aLA, 21.4 FB% (15th-lowest)
Nick Martinez, 6.4º aLA, 20.6 FB% (11th-lowest)
Tanner Houck, 6.5º aLA, 20.8 FB% (12th-lowest)

Here were the 10 pitchers who had the highest average Launch Angles:

Cristian Javier, 23.8º aLA, 38.8 FB% (highest)
JP Sears, 22.6º aLA, 35.4 FB% (fourth-highest)
Andrew Abbott, 21.6º aLA, 35.7 FB% (second-highest)
Mike Clevinger, 21.4º aLA, 33.8 FB% (10th-highest)
Tyler Wells, 20.7º aLA, 35.2 FB% (fifth-highest)
Kutter Crawford, 20.4º aLA, 31.9 FB% (15th-highest)
Joe Ryan, 20.4º aLA, 34.7 FB% (eighth-highest)
Max Scherzer, 20.2º aLA, 32.7 FB% (13th-highest)
Bailey Ober, 20.0º aLA, 35.2 FB% (sixth-highest)
Jordan Lyles, 19.8º aLA, 33.0 FB% (12th-highest)

Hard Hit Rate: This one takes Exit Velocity one step further, designating a “Hard Hit” batted ball as one that was struck with an exit velocity of at least 95 mph, then taking the player’s average of all batted balls that were hit at least that speed. Again, MLB.com’s Statcast glossary has more details on the methodology, including the rationale for that number, but to summarize, it’s at the 95 mph threshold when a batted ball’s potential result improves dramatically.

While Exit Velocity can help with predictive — meaning, for us, fantasy — analysis, Hard Hit Rate is a better tool, extracting only the rate of the most positive, and productive, results. There’s a stronger correlation between high Hard Hit Rates among hitters or low ones among pitchers and fantasy success.

Among batting title-eligible hitters in 2023, here were the top 10 in Hard Hit Rate:

Matt Chapman, 56.4%
Matt Olson, 55.5%
Juan Soto, 55.3%
Ronald Acuna Jr., 55.2%
Rafael Devers, 55.1%
Shohei Ohtani, 54.2%
Yandy Diaz, 54.0%
Corey Seager, 53.2%
Gunnar Henderson, 52.0%
Julio Rodriguez, 52.0%

These 10 names comprised three of the six hitters who hit at least 40 home runs (Olson, Ohtani and Acuna), and the group averaged 34 homers. Eight of the 10 finished among the top-25 hitters in either fantasy points scored or on the ESPN Player Rater (Olson, Soto, Acuna, Devers, Ohtani, Diaz, Seager and Rodriguez).

Taking the opposite approach, here were the bottom 10 qualifiers in Hard Hit Rate:

Steven Kwan, 18.8%
Myles Straw, 23.3%
Whit Merrifield, 24.3%
Luis Arraez, 25.7%
Ha-Seong Kim, 26.7%
Andres Gimenez, 27.0%
Andrew Benintendi, 27.0%
Jeff McNeil, 27.4%
TJ Friedl, 27.6%
Isaac Paredes, 28.5%

Sticking with the 100-inning pitching qualification threshold, here were the 10 best pitchers in terms of Hard Hit Rate in 2023:

Nick Martinez, 29.9%
Wade Miley, 31.3%
Kyle Hendricks, 31.5%
Tyler Anderson, 32.3%
Corbin Burnes, 32.4%
Paul Blackburn, 33.2%
Michael King, 33.3%
Drew Smyly, 33.6%
Blake Snell, 33.8%
Julio Urias, 34.5%

Conversely, here were the 10 worst pitchers in Hard Hit Rate:

Brady Singer, 48.6%
Shane Bieber, 47.8%
Taj Bradley, 46.4%
Adrian Houser, 46.3%
Zac Gallen, 46.2%
Logan Webb, 46.0%
Framber Valdez, 45.6%
Braxton Garrett, 45.1%
Tyler Glasnow, 44.6%
Logan Gilbert, 44.6%

Barrels: Another “one step further” metric, this time combining Exit Velocity and Launch Angle, Barrels are defined as batted balls hit at the optimal marks in both of those categories. Statcast specifically classifies these as batted balls that, when combining those two factors, have resulted in a minimum .500 batting average and 1.500 slugging percentage — in short, they’re the big hits, and probably home runs. MLB.com’s Statcast glossary delves a little deeper into the category here.

Barrels can be helpful when trying to judge players’ power (or the ability to rein it in, on the pitching side), especially if trying to remove park factors from the mix. Hitters who do well in the category typically fare well in the home run department, as eight of the 13 who managed at least 60 Barrels in 2023 also hit at least 30 home runs (a level that only 29 hitters reached), while all six hitters with at least 66 Barrels hit 37-plus homers (a level that only 11 hitters reached).

Here were the top 10 in Barrels, along with their homer totals and ranks:

Ronald Acuna Jr., 86 Barrels, 41 home runs (fifth)
Matt Olson, 73 Barrels, 54 homers (major league leader)
Shohei Ohtani, 70 Barrels, 44 (fourth)
Aaron Judge, 66 Barrels, 37 homers (tied for 10th)
Marcell Ozuna, 66 Barrels, 40 homers (sixth)
Austin Riley, 66 Barrels, 37 homers (tied for 10th)
Pete Alonso, 62 Barrels, 46 homers (third)
Adolis Garcia, 62 Barrels, 39 homers (tied for seventh)
Spencer Torkelson, 62 Barrels, 31 homers (tied for 22nd)
Kyle Schwarber, 61 Barrels, 47 homers (second)
Bobby Witt Jr., 61 Barrels, 30 homers (tied for 26th)

To repeat, this is a metric that can also be used to evaluate pitchers. Among ERA qualifiers, Corbin Burnes, Justin Steele and Zack Wheeler tied for the fewest Barrels allowed last season (27), while Miles Mikolas surrendered a league-most 66. Steele’s 0.73 HR/9 ratio, unsurprisingly, was second-best among those 44 ERA qualifiers, while Mikolas’ 1.16 HR/9 ratio was 15th-highest among that same group. That said, and to illustrate that this, nor any, category should be considered a “be all, end all” for skills analysis, Burnes (1.02 homers per nine) and Wheeler (0.94) were more middling in the rankings.

Mikolas, incidentally, had the majors’ seventh-lowest HR/FB% among those same qualifiers (8.6%). That he made 21 of his 35 starts (60%) in the game’s 10 least homer-friendly ballparks, not to mention another four in venues that leaned on that side of the league’s average, surely helped keep his home run total from spiraling out of control.

Spin Rate (SR): This measures the rate of spin on the baseball after a pitcher releases it, calculated in revolutions per minute. In addition to velocity, a pitcher’s Spin Rate has a bearing on its movement. For example, a fastball thrown with high spin crosses the plate at a higher plane than one with low spin, which is what causes the mythical “rising fastball.” Higher spin rates, too, create more break on a pitcher’s curveball, improving its effectiveness.

That’s not to say that Spin Rates on either extreme of the spectrum always result in a boost in pitch effectiveness.

Eury Perez, one of 2023’s rookie sensations, had a Spin Rate of 2,635 revolutions per minute on his four-seam fastball, second-highest among pitchers who threw at least 500 total pitches, behind only Ryan Helsley‘s 2,642. Perez’s fastball averaged 97.5 mph, fourth-fastest among 94 pitchers who threw as many as he did, but batters hit .289 and slugged .585 against the pitch, placing him 11th-worst among those 94 in wOBA (.399).

Among the reasons were Perez’s extreme reliance upon the pitch — he threw it 45.4% of the time, despite positively filthy metrics with his slider, curveball and changeup — in part as the Miami Marlins aimed to ease the stress on his arm while keeping his innings in check, but also because he leaned heavily upon the fastball when in hitters’ counts. In those situations, Perez surrendered a .615 wOBA with his fastball, second-worst among pitchers who threw at least as many fastballs as he did in those counts. It’s not an unfair assumption to surmise that hitters adopted a “sit on the fastball” approach with Perez, which could explain why he had such a disparity in performance between his fastball and secondary pitches.

Nevertheless, Perez’s fastball results illustrate that the Spin Rate metric — nor average velocity, on its own — isn’t the solitary indicator of an elite pitch.

Robert Stephenson‘s cutter, a pitch that he introduced into his repertoire in 2023, presents an ideal example of a pitch made more effective thanks to its high spin rate. Among any pitch thrown by an individual at least 250 times last season, his cutter had the most revolutions per minute of that specific pitch type (2,874), a rate that compared with some of the higher spin rates among curveballs or sliders, pitchers that typically generate high spin rates (often in the ballpark of 3,000 revolutions per minute).

Stephenson’s cutter was responsible for 42 of his 77 total strikeouts, generated a 60% miss rate on hitters’ swings, and afforded hitters only a .101 batting average against it. Statcast graded the pitch as worth 12 runs above average, making it one of the league’s most effective pitches overall on a per-pitch basis. It was a huge contributor to what was a breakthrough year for the right-hander, earning him a three-year, $33 million free agent contract this winter from the Los Angeles Angels, who might even give him an opportunity to serve as their closer.

Expected Batting Average (xBA), Expected Slugging Percentage (xSLG) and Expected Weighted On-Base Average (xwOBA): These might be the most helpful for fantasy managers, and definitively wiser metrics for stripping “luck” factors from players’ numbers. Each formulates an expected number based on the Exit Velocity, Launch Angle and, if applicable based on the type of batted ball, the player’s Sprint Speed, providing a better gauge of what the player should’ve been expected to do, either on an individual play or over the season (if the cumulative numbers).

Expected Weighted On-Base Average should be of more interest to those of you in points-based leagues, which reward for doubles and triples. It helps provide a fuller picture of a player’s hitting ability.

Here were the top 10 qualified hitters in terms of xwOBA in 2022, along with their finishes among hitters in fantasy points:

Ronald Acuna Jr., .461 xwOBA, 707 fantasy points (first)
Shohei Ohtani, .427 xwOBA, 490 FPTS (eighth)
Corey Seager, .412 xwOBA, 443 FPTS (15th)
Freddie Freeman, .408 xwOBA, 568 FPTS (fourth)
Juan Soto, .408 xwOBA, 516 FPTS (seventh)
Mookie Betts, .407 xwOBA, 574 FPTS (second)
Bryce Harper, .399 xwOBA, 356 FPTS (57th)
Marcell Ozuna, .396 xwOBA, 405 FPTS (29th)
Matt Olson, .392 xwOBA, 571 FPTS (third)
Kyle Tucker, .386 xwOBA, 524 FPTS (fifth)

If we adjusted the qualification threshold down to 450 plate appearances, then Aaron Judge (.461 xwOBA) and Yordan Alvarez (.438) would sandwich Acuna as the top three on the leaderboard, and Judge (340 FPTS) and Alvarez (388 FPTS) had plenty productive fantasy seasons in their own right.

One hitter who finished high on the xwOBA leaderboard, but whose raw fantasy numbers didn’t mirror it, was Vladimir Guerrero Jr.. His .375 xwOBA greatly exceeded his .340 actual wOBA, resulting in a 35 point differential that was the widest in that direction among qualified hitters. Considering it was the first time in his five big-league seasons that he had an actual wOBA beneath his xwOBA, while logging relatively similar hard-contact metrics, he should be expected to enjoy better fortune on his balls in play in 2024 than he did in 2023.

These categories can also be used to identify regression candidates, players whose batted-ball outcomes were more favorable than they should’ve been. Friedl, mentioned above regarding his lack of hard contact, had the majors’ largest wOBA-xwOBA split among qualified hitters, 64 points in that direction (.353 wOBA, .289 xwOBA).

Here is an excellent place to find all of these expected statistics, as well as some of the other Statcast offerings, including a CSV download option. You can also find the numbers for pitchers here.

Sprint Speed: Introduced in 2017, this measures, in feet, how quickly a player ran during the fastest one-second window of his running the bases. Two types of baserunning opportunities are measured: Runs to first base on weakly hit grounders, or runs of two bases or more on balls kept within the park (excluding runs from second base on an extra-base hit). This helps get a sense of a player’s raw speed, something that can be useful when seeking stolen-base production in fantasy.

Any run measured at greater than 30 feet per second is judged excellent and termed a “Burst,” and the league’s average number in the category is usually only a little better than 27 feet per second. Slower runners sometimes see numbers as poor as 22 feet per second, such as Yasmani Grandal, who averaged a worst-in-baseball (among those with at least 50 measured runs) 22.8 feet per second in 2023.

These were the top 10 performers in Sprint Speed in 2023, among those who had at least 50 “competitive runs” measured, along with their stolen base totals:

Elly De La Cruz, 30.5 feet/second, 35-of-43 stealing bases
Bobby Witt Jr., 30.5 feet/second, 49-of-64 stealing bases
Dairon Blanco, 30.3 feet/second, 24-of-29 stealing bases
Trea Turner, 30.3 feet/second, perfect 30-of-30 stealing bases
Jorge Mateo, 30.1 feet/second, 32-of-37 stealing bases
Corbin Carroll, 30.1 feet/second, 54-of-59 stealing bases
Blake Perkins, 30.0 feet/second, 5-of-7 stealing bases
Jacob Young, 30.0 feet/second, perfect 13-of-13 stealing bases
Jake McCarthy, 29.9 feet/second, 26-of-30 stealing bases
Brenton Doyle, 29.9 feet/second, 22-of-27 stealing bases

As you can see, this group went a combined 290-of-339 stealing bases, for an 85.5% success rate that easily exceeded the league’s average (80.2%).

There are plenty of other Statcast categories you can investigate, but these are the seven that have the most immediate relevance to fantasy managers.

Defense independent pitching metrics

FIP and xFIP: An abbreviation for Fielding Independent Pitching score — and for expected FIP — this attempts to eliminate the influence of a pitcher’s defense upon his statistics, by judging him on only his home runs, walks and hit batsmen allowed and his strikeouts and whittling those down to a number similar to ERA. xFIP takes it a step further, removing the “luck” factor involved with home runs by instead using the pitchers’ fly balls allowed and assuming a league-average home run rate on them.

FIP can be a quick, basic way of stripping any misfortune a pitcher faced during the season in question, identifying pitchers whose fortunes should even out in the future. xFIP, meanwhile, can be helpful when evaluating pitchers assigned to pitch in ballparks with significantly different park factors, or for those changing teams. Whichever you use, both are substantially stronger scouting measures than ERA.

These were the top 10 pitchers in FIP in 2023, among those who worked at least 100 innings pitched, all of whom had excellent seasons:

Sonny Gray, 2.83
Spencer Strider, 2.85
Tyler Glasnow, 2.91
Kevin Gausman, 2.97
Zach Eflin, 3.01
Justin Steele, 3.02
Michael King, 3.13
Zack Wheeler, 3.15
Logan Webb, 3.16
Gerrit Cole, 3.16

Comparing a pitcher’s FIP to his ERA is often a handy, albeit basic, way of unearthing “flukes” who might be in line for better fortune in the year ahead. Again among pitchers who threw at least 100 innings, here were the eight widest ERA-FIP differentials, leaning on the side of their having experienced more misfortune:

Adam Wainwright, 1.41 run difference (7.40 ERA, 5.99 FIP)
Brady Singer, 1.24 (5.52, 4.29)
Joey Wentz, 1.09 (6.90, 5.81)
Spencer Strider, 1.00 (3.86, 2.85)
Dylan Cease, 0.85 (4.58, 3.72)
Luke Weaver, 0.80 (6.40, 5.61)
Taj Bradley, 0.80 (5.59, 4.79)
Hunter Brown, 0.71 (5.09, 4.37)
David Peterson, 0.69 (5.03, 4.34)
Jordan Lyles, 0.66 (6.28, 5.62)

That’s not to say that Weaver is destined for a major rebound in 2024, especially since a 5.61 FIP is anything but a pretty number. Cease’s inclusion on the list, however, indicates that he pitched much better than his raw fantasy numbers indicated.

Flipping things around, here are the 10 pitchers who were most fortunate in terms of their ERA-FIP differential:

Clayton Kershaw, minus-1.57 run difference (2.46 ERA, 4.03 FIP)
Wade Miley, minus-1.55 (3.14 ERA, 4.69 FIP)
Tyler Wells, minus-1.34 (3.64 ERA, 4.98 FIP)
Javier Assad, minus-1.24 (3.05 ERA, 4.29 FIP)
Blake Snell, minus-1.19 (2.25 ERA, 3.44 FIP)
Michael Kopech, minus-1.04 (5.43 ERA, 6.46 FIP)
Josiah Gray, minus-1.03 (3.91 ERA, 4.93 FIP)
Shohei Ohtani, minus-0.86 (3.14 ERA, 4.00 FIP)
J.P. France, minus-0.83 (3.83 ERA, 4.66 FIP)
Jake Irvin, minus-0.69 (4.61 ERA, 5.30 FIP)

Beware of putting too much stock into FIP and xFIP, however, with my recommendation to consider it merely another evaluative tool in your toolbox. Miley, for example, now has an ERA-FIP differential of at least half of a run in each of his last three healthy seasons (2019, 2021 and 2023), exhibiting a tendency to outperform his peripherals thanks to his control and his ability to minimize hard contact.

SIERA: An abbreviation for Skill-Interactive ERA, SIERA is a more recent innovation that, like FIP, attempts to remove defensive influence from the pitching equation and determine just how effective said hurler actually was. The key difference between SIERA and FIP is that while the latter excludes batted balls from its equation, the former does consider them in the calculation. If you’re interested in the mathematical details, FanGraphs wrote a great column explaining SIERA and providing the formula to calculate it here.

While SIERA’s leaderboard doesn’t precisely match that of FIP, it does a good job of identifying pitching skill. Here were the top 10 in SIERA in 2023, using the 100-inning threshold for qualification:

Spencer Strider, 2.86
Tyler Glasnow, 3.08
Logan Webb, 3.16
Michael King, 3.29
Zach Eflin, 3.30
Kevin Gausman, 3.34
Nick Pivetta, 3.36
Pablo Lopez, 3.37
Joe Ryan, 3.44
Freddy Peralta, 3.45

‘Luck’-based statistics

Once the hottest thing in fantasy baseball analysis, luck-based stats have taken more of a backseat in recent seasons, as we gain greater awareness of the ingredients that influence them. Still, it’s worth a quick refresher on these, as each can provide a small insight into a player’s ability, not to mention our understanding of them can reveal the pitfalls involved in trusting each too much.

BABIP, or Batting Average on Balls in Play: First introduced by Voros McCracken around the turn of the century, BABIP measures a pitcher’s ability to prevent hits on balls in play, as well as a hitter’s success rate only on the batted balls he puts into play. This removes walks, strikeouts and home runs — those don’t land within the field of play, after all — from the equation. You can calculate it yourself by dividing hits minus home runs by at-bats minus home runs minus strikeouts plus sacrifice flies, or (H – HR)/(AB – HR – K + SF).

The idea is that the league’s average BABIP is generally around .300, so any player with a number significantly removed from that is likely to regress towards said average in the near future. As defensive shifts took hold over the past decade, however, that number inched downward. In both 2020 and 2021, the league’s average BABIP was .292, and in 2022, it dipped to .290, the league’s lowest rate in 30 years. With new rules in place governing shifts in 2023, however, the league’s BABIP rebounded to .297.

The problem with BABIP as an analytic tool is that it completely ignores both the quality of contact involved with the type of batted ball, as well as the defensive alignment, things that the aforementioned Statcast “expected” statistics aim to correct. That’s why, when examining BABIP, it’s wise to account for the type of pitcher or hitter (ground ball versus fly ball), as well as the player’s own history in the category. For example, has he routinely posted BABIPs that exceed the league’s average?

In 2023, the top two qualified hitters in terms of BABIP were Freddie Freeman (.370) and Yandy Diaz (.367), numbers that were 29 and 54 points higher than their career rates in the category entering the season. That comparison hints that some batting average regression should be anticipated with either hitter in 2024. It’s fair to point out, however, that Freeman has now managed a .350-plus BABIP in six of his past 10 seasons, while Diaz had a .371 BABIP in limited time in 2018 as well as a .323 number in the category as a regular in 2022, meaning neither should be expected to see his BABIP completely regress to the league’s average rate.

Home Run per Fly Ball Percentage (HR/FB%): Alluded to in the xFIP section, Home Run per Fly Ball Percentage determines how fortunate a player might have been in seeing the fly balls he hit clear the outfield fence for a home run. The league’s annual average in the category varies more than does BABIP, but in 2023 was 10.6% — nearly a full percentage point better than 2022’s rate (9.7%). Like BABIP, hitters and pitchers are typically expected to regress towards the mean in the near future, though unlike BABIP, this category can be much more easily influenced by things such as contact quality or park factors.

In 2023, Lance Lynn (13.8%) had the highest qualified rate among pitchers, while Logan Webb, who finished second in NL Cy Young balloting, had a career-high, and third-worst-in-the-league 13.2% rate in the category. Sonny Gray had the majors’ lowest rate (5.5%), while Cristian Javier had a third-best 8.3% rate that suggested his second-half swoon could have been much worse than it was.

One big pitfall to consider with this category is the differing calculations across statistical sources, due to the different classifications in batted ball types as well as the slight differences in formulas. For example, FanGraphs had the league’s average Home Run per Fly Ball Percentage as 12.7%.

Strand Rate, or Left On Base Percentage (LOB%): This measures the percentage of base runners that a pitcher leaves on base in a given outing, or over the course of a season. Rather than taking the actual number of baserunners stranded, it assumes that runners score at a league-average rate. The formula is hits plus walks plus hit batsmen minus runs scored, divided by hits plus walks plus hit batsmen minus home runs times 1.4 (a predetermined, league-average factor), or (H + BB + HB – R)/(H + BB + HB – (HR * 1.4)).

The league’s average Strand Rate is typically around 72.0%, and in 2023 it was 71.9%. Last season among ERA-qualified pitchers, Blake Snell was the leader in the category (86.7%), while Jordan Lyles (56.3%) brought up the rear. Snell’s Strand Rate was eerily similar to the 88.0% number he put forth the last time he won the league’s Cy Young Award (2018), heightening concerns that he’ll regress in 2024.

Site-to-site variance

Not every batted ball is judged the same.

As mentioned in the Home Run per Fly Ball Percentage category, the classification of batted balls in play can have a noticeable influence upon the results. For example, both Statcast and our internal pitch-tracking tool assign pop-ups as their own category, independent of fly balls, whereas FanGraphs’ listed fly ball rates include those pop-ups. Hard Hit Rates also can vary depending upon your source.

For example, Isaac Paredes had the majors’ highest pop-up rate among batting title-eligible hitters, having popped the ball up 14.9% of the time that he put it into play. FanGraphs includes these in his fly ball rate, which is how he had a 47.0% number there, 11th-highest among 133 qualifiers, whereas he had a mere 28.5% fly ball rate per our internal pitch-tracking tool. This is an especially important distinction for Paredes, who plays in one of the most extreme pitching environments in all of baseball, Tampa Bay’s Tropicana Field, as his modest fly ball rate will probably make it challenging for him to repeat or even approach his 31 home runs from last year.

Always consider multiple sources with your data. Wide variance upon the results might require additional research to determine the player’s true skill level. If all else fails, though, I’d trust the Statcast data first and foremost.

Where to research these numbers more deeply on your own

Each of the aforementioned statistical categories is readily available on the internet, including many download options for you to play with the numbers yourself.

BaseballSavant.com, referenced earlier, houses a wide variety of Statcast statistics that can be sorted, searched and downloaded. Some of the links for those are available above, but I’m focusing on its Search page here, since it’s a great place with which to run queries of your choosing while scouting players.

There, you’ll find all sorts of situations with which to examine facets of a player’s game, including performance against different pitch types, in certain counts, against players of either handedness, or using specific date ranges, among many other options. Be sure to first select your Player Type, batter (or specific position player) or pitcher, before entering your query. To provide a specific example, if you’re interested in seeing which hitter had the highest xwOBA during the final month of 2023, choose Player Type batters, set the Game Date >= as 2023-09-01, then choose Sort By xwOBA. You could also set a Min # of Results if you wish, say, 250.

As you can see, Ronald Acuna Jr. (.495) occupies the top spot using this split, while Elvis Andrus (.196) ranks last, perhaps one reason Andrus was still in search of a team as spring training dawned.

Royce Lewis‘ monstrous finish – he batted .313/.410/.612 with six home runs in 18 September games, plus his four additional home runs in the Twins’ six postseason contests — help easily explain how he has been one of the more popular breakthrough picks in early drafts.

FanGraphs is another site that offers custom statistics reports, including those you can download. Here is where you can find the basic 2023 hitters’ leaderboard, but you can select a variety of different reports: Standard statistics, Advanced statistics, Batted Ball statistics, Win Probability and Value statistics, +Stats (which compare the player’s performance to the league’s average), Statcast statistics, Plate Discipline statistics, an entire array of Pitch-Level Data that now resides under its own tab, and many other options.

As with Statcast, FanGraphs offers options to check players’ splits, as well as to request numbers within a Custom Date Range. One example to highlight some of the options is to check the standard stats page for pitchers using the 2023 away-games split. There, you’ll see that Mitch Keller had a 5.35 ERA in his 18 starts away from Pittsburgh’s PNC Park, giving him a near two-and-a-half-run differential between his home (2.90) and road ERAs, the second-widest among qualifiers in that direction.

As a quick note, as FanGraphs isn’t a paywall website, especially in the difficult current environment, consider ordering a membership to provide your support.

Among some of the other websites you should consider in your scouting:

Brooks Baseball: Their strength is their Pitch F/X tool, which can help you do scouting on players similar to some of those available on Statcast. There are options to check player splits by situation and time period, and they have a graphical interface that helps illustrate player skill findings.

Baseball Prospectus: They’ve been around for quite some time, providing analytics for well over two decades as well as publishing an annual that profiles each player individually. Many advanced analytics are available there as well.

Now that you’ve gotten your feet wet with advanced statistics, let’s put them to use! The final inning of the Playbook extracts some of my favorite findings using many of the tools discussed above.