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

You need a lot more than just batting average and home run totals to evaluate players like Ian Happ. Kamil Krzaczynski-USA TODAY Sports

(The full, nine-inning Playbook was originally published during the spring of 2020. It has been updated for 2023 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.


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 eight seasons (2015-22). 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 and a search engine if you're interested in fielding a specific query. MLB.com also has Statcast leaderboards available for a handful of categories.

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 2022 was 88.1 mph, and it took a 91.6 mph number for a player to place in the 90th percentile, with 86.7 mph placing him in the 10th percentile.

These were the top 10 in aEV among batting title-eligibles in 2022:

Aaron Judge, 95.9 mph
Yordan Alvarez, 95.2
Kyle Schwarber, 93.3
Rafael Devers, 93.1
Shohei Ohtani, 92.9
Matt Olson, 92.9
Vladimir Guerrero Jr., 92.8
Teoscar Hernandez, 92.6
Austin Riley, 92.5
Matt Chapman, 92.2

Judge, incidentally, has now led the majors in the category in back-to-back seasons.

These were the bottom 10 in aEV among eligible hitters:

Tony Kemp, 84.4 mph
Cesar Hernandez, 84.8
Steven Kwan, 85.1
Adam Frazier, 85.1
J.P. Crawford, 85.1
Kyle Farmer, 85.5
Jose Altuve, 85.9
Miguel Rojas, 86.1
Isiah Kiner-Falefa, 86.2
Thairo Estrada, 86.2

As only 45 pitchers qualified for the ERA title in 2022, expanding the qualification list to those who worked at least 100 innings provides a more comparable sample size to the hitters above, as 140 pitchers reached that threshold. Among those 140, here are the 10 best pitchers in terms of aEV in 2022:

Tyler Anderson, 85.0 mph
Chris Bassitt, 85.7
Zack Wheeler, 85.9
Max Fried, 86.2
Joe Musgrove, 86.4
Jose Quintana, 86.5
Nick Martinez, 86.5
Julio Urias, 86.7
Marco Gonzales, 86.7
Drew Smyly, 86.7

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

Yusei Kikuchi, 91.6 mph
Daniel Lynch, 91.2
Logan Gilbert, 91.0
Bryse Wilson, 90.9
Patrick Corbin, 90.8
Nick Pivetta, 90.7
Chad Kuhl, 90.5
German Marquez, 90.5
Madison Bumgarner, 90.3
Drew Hutchison, 90.3

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 five batting title-eligible hitters in terms of average Launch Angle last season, along with their ranking in terms of fly-ball rate:

Nolan Arenado, 21.7º aLA, 29.0 FB% (41st)
Anthony Santander, 21.4º aLA, 28.7 FB% (46th)
Patrick Wisdom, 21.0º aLA, 31.8 FB% (23rd)
Max Muncy, 20.9º aLA, 39.5 FB% (1st)
Jose Ramirez, 20.7º aLA, 28.6 FB% (47th)

Next, here are the bottom five in Launch Angle:

DJ LeMahieu, 3.0º aLA, 17.0 FB% (third-lowest)
Christian Yelich, 3.6º aLA, 18.0 FB% (sixth-lowest)
Isiah Kiner-Falefa, 4.3º aLA, 14.8 FB% (lowest)
Vladimir Guerrero Jr., 4.3º aLA, 17.1 FB% (fourth-lowest)
Brendan Rodgers, 4.6º aLA, 19.7 FB% (15th-lowest)

Again using 100 innings pitched as our qualification threshold, here are the five pitchers with the lowest average Launch Angles in 2022, along with their fly-ball rates:

Framber Valdez, minus-3.9º aLA, 10.6 FB% (lowest)
Andre Pallante, minus-3.6º aLA, 12.5 FB% (second-lowest)
Alex Cobb, 1.1º aLA, 14.5 FB% (third-lowest)
Aaron Ashby, 1.8º aLA, 19.6 FB% (13th-lowest)
Logan Webb, 2.6º aLA, 15.2 FB% (fourth-lowest)

Here are the five pitchers who had the highest average Launch Angles:

Cristian Javier, 24.7º aLA, 38.7 FB% (highest)
Joe Ryan, 23.4º aLA, 32.8 FB% (seventh-highest)
Max Scherzer, 20.6º aLA, 30.8 FB% (17th-highest)
Hunter Greene, 20.3º aLA, 36.2 FB% (second-highest)
Jake Odorizzi, 19.5º aLA, 30.4 FB% (21st-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 2022, here were the top 10 in Hard Hit Rate:

Aaron Judge, 61.8%
Yordan Alvarez, 59.8%
Kyle Schwarber, 54.4%
Teoscar Hernandez, 53.3%
Jose Abreu, 51.8%
Matt Chapman, 51.2%
Matt Olson, 50.9%
Rafael Devers, 50.9%
Austin Riley, 50.8%
Julio Rodriguez, 50.7%

These 10 names comprised four of the top six spots on the home run leaderboard (Judge, Alvarez, Riley and Alvarez), and eight of the 10 nine finished among the top 30 hitters in either fantasy points or on the Player Rater (Judge, Alvarez, Schwarber, Abreu, Olson, Devers, Riley and Rodriguez). Taking the opposite approach, here were the bottom 10 qualified hitters in Hard Hit Rate:

Tony Kemp, 15.0%
Steven Kwan, 20.8%
Adam Frazier, 24.5%
Myles Straw, 26.5%
Miguel Rojas, 26.5%
J.P. Crawford, 29.7%
Cesar Hernandez, 29.7%
Jose Altuve, 29.7%
Isiah Kiner-Falefa, 30.1%
Jeff McNeil, 30.2%

Sticking with the 100-inning pitching qualification threshold, here were the 10 best pitchers in terms of Hard Hit Rate in 2022:

Tyler Anderson, 28.5% 23
Julio Urias, 30.4%. 5 13
Dylan Cease, 31.2%. 8 6
Alek Manoah, 31.5%. 4 5
Aaron Nola, 31.6%. 14 10
Max Fried, 32.1%. 13 15
Joe Musgrove, 32.5%. 38
Shane McClanahan, 32.6% 11 16
Chris Bassitt, 32.8%. 40
Jake Odorizzi, 33.1% 266

Urias, Cease, Manoah, Nola, Fried and McClanahan all finished among the top-16 pitchers in terms of both fantasy points scored and on the Player Rater while Anderson finished 20th in fantasy points and 23rd among pitchers on the Player Rater. Conversely, here were the 10 worst pitchers in Hard Hit Rate last season:

Yusei Kikuchi, 47.9%
Daniel Lynch, 47.7%
German Marquez, 47.2%
Logan Gilbert, 45.6%
Nick Pivetta, 45.6%
Jonathan Heasley, 45.3%
Nathan Eovaldi, 45.2%
Patrick Corbin, 45.0%
Kris Bubic, 44.9%
Chad Kuhl, 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, 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 as eight of the 10 who managed at least 59 Barrels in 2022 also hit at least 34 home runs (a level that only 12 hitters reached), with the only outliers being Ryan Mountcastle (61 Barrels and 22 homers, though the ballpark dimension changes during the 2021-22 season made Camden Yards one of the five toughest venues for right-handed power) and Vladimir Guerrero Jr. (59 Barrels and a respectable 32 homers).

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

Aaron Judge, 106 Barrels, 62 home runs (first)
Yordan Alvarez, 78 Barrels, 37 homers (sixth)
Kyle Schwarber, 76 Barrels, 46 homers (second)
Shohei Ohtani, 72 Barrels, 34 homers (tied-11th)
Austin Riley, 71 Barrels, 38 homers (fifth)
Ryan Mountcastle, 61 Barrels, 22 homers (tied-55th)
Matt Olson, 61 Barrels, 34 homers (tied-11th)
Pete Alonso, 59 Barrels, 40 homers (tied-third)
Mike Trout, 59 Barrels, 40 homers (tied-third)
Vladimir Guerrero Jr., 59 Barrels, 32 homers (tied-15th)

To repeat, this is a metric that can also be used to evaluate pitchers. Among ERA qualifiers, Max Fried allowed the fewest Barrels last season (21), while Jordan Lyles surrendered a league-most 59. Fried's 0.58 HR/9 ratio was fifth-best among those 45 ERA qualifiers, while Lyles' 1.31 HR/9 was 10th-highest among that same group. Lyles, however, was one of the more fortunate pitchers in terms of fly balls clearing the fence, his 8.8% HR/FB rate more than a half-percentage-point beneath the league's average, making his Barrel rate a more representative measure of his skill set than his HR/9 rate.

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.

Last season, Jose Leclerc's Spin Rate of 2,639 revolutions per minute on his four-seam fastball ranked fourth-highest among pitchers who threw at least 500 total pitches, behind only Alexis Diaz's 2,656, Jason Adam's 2,654 and Ryan Helsley s 2,643. Leclerc's fastball averaged 96.5 mph, well above the league's average rate, but batters managed a .298 batting average and .579 slugging percentage against it. Among the reasons for this were Leclerc's minimal extension -- this is the distance from home plate that the pitcher releases the baseball, with higher numbers better due to it granting hitters less time to react -- his inability to locate the pitch in the zone, his 44.8% rate of doing so third-worst among pitchers who threw at least as many as the 286 four-seam fastballs that he did, and his tendency to leave the ball high in the zone, with his 58.7% rate of pitches in the upper third of the strike zone (or outside it) more than 10% greater than the league's average. Leclerc's results illustrate that the Spin Rate metric -- nor average velocity, on its own -- isn't the solitary indicator of an elite pitch.

Dylan Cease's slider presents an outstanding example of a pitch made more effective thanks to its high spin rate. Among any of the 25 specific pitches that any individual threw at least 1,200 times last season, Cease's slider had the second-most revolutions per minute (2,833) among any pitch type, trailing only Charlie Morton's curveball (3,068), which in large part explains how Cease's slider was responsible for 131 of his 227 total strikeouts. Those 131 K's were the third-most recorded by any specific pitch last season, behind only the 135 that each of Gerrit Cole and Carlos Rodon recorded with their four-seam fastballs, pitches that they threw considerably more often than Cease threw his slider. That's as good a reason as any to believe in Cease's 2022 breakthrough as his permanent arrival as a fantasy ace.

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:

Aaron Judge, .463 xwOBA, 607 fantasy points (first)
Yordan Alvarez, .462 xwOBA, 453 FPTS (eighth)
Freddie Freeman, .403 xwOBA, 525 FPTS (third)
Juan Soto, .401 xwOBA, 437 FPTS (14th)
Shohei Ohtani, .385 xwOBA, 411 FPTS (18th)
Austin Riley, .378 xwOBA, 399 FPTS (21st)
Kyle Schwarber, .375 xwOBA, 381 FPTS (26th)
Jose Abreu, .373 xwOBA, 380 FPTS (27th)
Corey Seager, .372 xwOBA, 402 FPTS (19th)
Paul Goldschmidt, .367 xwOBA, 490 FPTS (fourth)

One hitter who finished high on the xwOBA leaderboard, but whose raw fantasy numbers didn't mirror it, was the aforementioned Ryan Mountcastle. His .362 xwOBA greatly exceeded his .316 actual wOBA, resulting in a 46 point differential that was the widest in that direction among qualified hitters. Sure, the ballpark probably had a lot to do with that, but he could also be expected to have at least slightly better fortune on his balls in play in 2023 than he did in 2022. Seager, who was a subject of Playbook's seventh inning, regarding the restrictions on defensive shifts, was another player who had a wide wOBA-xwOBA split, 41 points in that same direction. That's an additional reason he could be in line for a 2023 rebound.

These categories can also be used to identify regression candidates, players whose batted-ball outcomes were more favorable than they should've been. Goldschmidt, remarkably, had the majors' largest wOBA-xwOBA split among qualified hitters, 52 points in that direction (.419 wOBA, .367 xwOBA). Andres Gimenez was another player who had just about everything go right, his split an eighth-widest 38 points (.364 wOBA, .326 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 Yadier Molina, who averaged a worst-in-baseball 21.8 feet per second in 2022.

These were the top 10 performers in Sprint Speed in 2022, among those who had at least 50 "competitive runs" measured, along with their stolen base totals:

Corbin Carroll, 30.7 feet/second, 2-of-3 stealing bases
Bubba Thompson, 30.4 feet/second, 18-of-21 stealing bases
Jose Siri, 30.4 feet/second, 14-of-16 stealing bases
Bobby Witt Jr., 30.4 feet/second, 30-of-37 stealing bases
Trea Turner, 30.3 feet/second, 27-of-30 stealing bases
Jorge Mateo, 30.1 feet/second, 35-of-44 stealing bases
Jake McCarthy, 30.1 feet/second, 23-of-26 stealing bases
Oneil Cruz, 29.9 feet/second, 10-of-14 stealing bases
Jo Adell, 29.8 feet/second, 4-of-6 stealing bases
Tyler O'Neill, 29.8 feet/second, 14-of-18 stealing bases

As you can see, this group went a combined 177-of-215 stealing bases, for an 82.3% success rate that greatly exceeded the league's average (75.4%).

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 eight pitchers in FIP in 2022, among those who worked at least 100 innings pitched, all of them excellent hurlers:

Spencer Strider, 1.83
Carlos Rodon, 2.25
Kevin Gausman, 2.38
Shohei Ohtani, 2.40
Justin Verlander, 2.49
Clayton Kershaw, 2.57
Aaron Nola, 2.58
Max Scherzer, 2.62

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:

Patrick Corbin, 1.47 run difference (6.31 ERA, 4.84 FIP)
Alex Wood, 1.33 (3.76, 5.10)
Taylor Hearn, 1.15 (3.98, 5.13)
Trevor Rogers, 1.11 (4.36, 5.47)
Austin Gomber, 1.02 (4.54, 5.56)
Kevin Gausman, 0.97 (2.38, 3.35)
Alex Cobb, 0.93 (2.80, 3.73)
Lucas Giolito, 0.84 (4.06, 4.90)

That's not to say that Corbin is destined for a major rebound in 2023, especially since a 4.84 FIP is hardly a pretty number. Gausman's inclusion on the list, however, indicates that he pitched much better than his raw fantasy numbers indicated.

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

Julio Urias, minus-1.55 run difference (2.16 ERA, 3.71 FIP)
Tony Gonsolin, minus-1.14 (2.14, 3.28)
Alek Manoah, minus-1.10 (2.24, 3.35)
Nick Martinez, minus-0.96 (3.47, 4.43)
Michael Kopech, minus-0.95 (3.54, 4.50)
Marco Gonzales, minus-0.92 (4.13, 5.05)
Dylan Cease, minus-0.90 (2.20, 3.10)
Josiah Gray, minus-0.83 (5.02, 5.86)

Beware of putting too much stock into FIP and xFIP, however, with my recommendation to consider it merely another evaluative tool in your toolbox. Gonzales, for example, now has an ERA-FIP differential of at least seven-tenths of a run in each of his past three seasons, 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 eight in SIERA in 2022, using the 100-inning threshold for qualification:

Spencer Strider, 2.41
Shohei Ohtani, 2.73
Gerrit Cole, 2.77
Aaron Nola, 2.80
Shane McClanahan, 2.82
Carlos Rodon, 2.83
Max Scherzer, 2.88
Corbin Burnes, 2.91

'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 each.

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). (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 has 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. We'll see how much that category rebounds under the new rules implemented in 2023.

The problem with BABIP as an analytic tool is that it completely ignores the quality of contact involved with the type of batted ball, something that the aforementioned Statcast "expected" statistics aims 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 2022, the top two qualified hitters in terms of BABIP were Paul Goldschmidt (.368) and Nathaniel Lowe (.363), numbers that were 21 and 25 points higher than their career rates in the category entering the season. That comparison hints that some batting average should be anticipated with either hitter in 2023, though it's fair to point out that Goldschmidt has on three prior occasions managed that high a BABIP, while Lowe made some critical changes to his overall approach that gives him a fighting chance of at least approaching the effort.

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 2022 was 9.7% -- more than a full percentage point beneath 2021's rate (10.8%), and reflecting the impact of the change of the baseball on the overall power environment. 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 2022, German Marquez (14.2%) had the highest qualified rate, while fantasy ace Gerrit Cole had a career-high 13.3% rate to finish second in the category. Jose Quintana had the majors' lowest rate (5.4%), and Tyler Anderson had a fourth-best 6.1% rate, more than five percent better than his career number entering the season. Cole has struggled more in this category since joining the New York Yankees, who call a homer-friendly environment their home, but he should be expected to be at least slightly more fortunate in that department in 2023.

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 slighty differences in formulas. For example, FanGraphs had the league's average Home Run per Fly Ball Percentage as 11.4%.

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 2022 it was 72.6%. Last season among ERA-qualified pitchers, Julio Urias was the leader in the category (86.6%), while Kyle Gibson (67.7%) brought up the rear. Urias' Strand Rate was more than 10% greater than his career number entering the season (75.7%), while Gibson's was more than 3% lower than his career rate entering 2022 (71.0%), so it's pretty likely that both will experience some ERA correction in 2023.

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 instance, Anthony Santander had the majors' highest pop-up rate among batting title-eligible hitters, having popped the ball up 15.8% of the time that he put it into play. FanGraphs includes these in his fly-ball rate, which is how he has a 49.8% number, fourth-highest among qualifiers. Both our internal pitch-tracking tool as well as Statcast, meanwhile, have Santander's fly-ball rate as 28.7%, which is 46th-highest among the 130 qualified hitters and much closer to a league-average number. This is an especially important difference considering Camden Yards' dimension changes last season, as Santander's more modest fly-ball rate hinders his chances of exceeding the 33 homers he hit last season there, barring him reining in the pop-ups.

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 2022, choose Player Type batters, set the Game Date >= as 2022-09-01, then choose Sort By xwOBA. You could also set a Min # of Results if you wish, say, 250.

As you can see, Yordan Alvarez (.504) occupies the top spot using this split, while Lewin Diaz (.177) ranks last among non-pitchers, perhaps one reason Diaz has changed organizations four times already this winter. Alvarez's monstrous finish -- he batted .355/.440/.677 with six homers and 17 RBIs in 26 September and October regular-season games, plus had numerous big hits during the postseason - only enhances his odds of an MVP push entering 2023.

FanGraphs is another site that offers custom statistics reports, including those you can download. Here is where you can find the basic 2021 hitters' leaderboard, but you can select a variety of different reports: Standard statistics, Advanced statistics, Batted Ball statistics, Pitch Type and Value statistics, Plate Discipline statistics and many other options.

As with Statcast, FanGraphs offers options to check player 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 2022 home-games split. There, you'll see that Sandy Alcantara had a 1.64 ERA in his 16 starts at Marlins Park, the lowest ERA by a pitcher who worked at least 120 innings in his home games since Dallas Keuchel posted a 1.46 mark in 129 1/3 frames at home in 2015.

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! There's one more Inning left in the Playbook, and it extracts some of my favorite findings using many of the tools discussed above. Stay tuned!