The Playbook, Inning 8: Advanced stats to use for fantasy baseball
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 an advanced fantasy player might find him or herself confused. Even turning on a broadcast might sometimes seem daunting, with such new 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?
Whether you’re an experienced player or one new to 21st-century statistical innovations such as Statcast, a refresher, or primer for the latter group, is often helpful. This edition of the Playbook dives deeper into some of the new metrics with which we can evaluate players. They’re broken down into several different categories below.
Inning 1: Fantasy baseball 101
Inning 2: League formats
Inning 3: Salary-cap drafts
Inning 4: Preparation
Inning 5: Roster optimization
Inning 6: Nine must-follow tips
Inning 7: Adjusting to league trends
Inning 8: Utilizing advanced stats
Inning 9: Mastering the player pool
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 an automated tool that analyzes players’ skills, using radar and camera systems which began to get installed in major-league stadiums over a decade ago and were fully installed in all ballparks beginning with the 2015 season. That means this data, in full, is only available for the past seven seasons (2015-21). 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 their 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 2021 was 88.1 mph, and it took a 92.1 mph number for a player to place in the 90th percentile, with 86.6 mph placing him in the 10th percentile. Aaron Judge (95.8 mph) was the league leader in the category among those eligible for the batting title, as well as those who put the ball into play at least 150 times.
The majors’ worst batting title-eligible player in the category was David Fletcher (82.3 mph), while Alcides Escobar (81.7 mph) was worst among those with at least 150 balls in play.
Turning to the pitchers, Zack Wheeler (84.6 mph) had the lowest average Exit Velocity among ERA qualifiers, Blake Treinen (83.3 mph) was best among relievers with at least 150 batted balls allowed, and Victor Gonzalez had an absurd 82.4 mph average Exit Velocity — best among pitchers with at least 50 batted balls allowed.
Conversely, Robbie Ray (90.4 mph) allowed the highest average Exit Velocity among ERA qualifiers, but bear in mind that a record-low 39 pitchers met said qualification. Adjusting the innings threshold downward, Justus Sheffield (92.9 mph) was the worst pitcher with at least 250 batted balls allowed, while David Hess (94.7 mph) was the worst overall pitcher who allowed at least 150 batted balls.
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 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).
To that end, Adam Duvall’s 23.6 degree average Launch Angle was the highest among batting title-eligible hitters, and his 40.1% fly-ball rate was, predictably, also the highest (by a full 2%) among those 132 hitters. Meanwhile, Isiah Kiner-Falefa’s minus-4.4 degree average Launch Angle was lowest among batting title eligibles, and his 12.3% fly-ball rate was also easily the league’s lowest (by 2.5%).
Pitching-wise, Luis Castillo‘s 4.1 degree average Launch Angle was the lowest among ERA qualifiers, and his 17.0% fly-ball rate was the second-lowest among that group. Again, since so few pitchers met that qualification threshold, Framber Valdez’s minus-5.5 degree average Launch Angle was the lowest among pitchers with at least 300 batted balls allowed. Freddy Peralta (21.4 degree aLA) was on the opposite end of the scale. They had the lowest (11.1%) and highest (32.4%) fly-ball rates, respectively, among pitchers who worked at least 130 innings.
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.
The league’s top batting title-eligible hitter in terms of Hard Hit Rate in 2021 was Aaron Judge (57.9%), who finished 38th overall on the ESPN Player Rater and tied for 45th in fantasy points scored. If you’re looking for high-placing names that might surprise you in this department, consider Miguel Sano and his 55.9% Hard Hit Rate, the third consecutive season in which he has enjoyed a rate of at least 55%. That played a big part in his clobbering 77 home runs from 2019-21. There’s also Joey Votto and his 53.2% Hard Hit Rate, which underscores the dramatic changes to his swing that he made in late 2020 which led to his hitting a 10th-best-in-baseball 44 homers since Aug. 29, 2020.
This metric, as with the previous two, can also be used to evaluate pitchers, specifically their ability to limit hard contact. Zack Wheeler led all ERA-qualified pitchers in Hard Hit Rate (28.5%), while Jonathan Loaisiga (24.1%) paced all pitchers who allowed at least 180 batted balls. Wheeler finished 12th on the Player Rater with the fourth-most fantasy points, while Loaisiga finished 147th overall on the Player Rater, doing so without the advantages of the closer role. Both of their successes can be attributed in large part to their ability to suppress hard contact.
One of the more troubling Hard Hit Rates on the pitching side came from late-season sensation Drew Rasmussen, who had a 1.46 ERA over an eight-start stint to conclude the regular season. He had the majors’ highest Hard Hit Rate allowed among pitchers who allowed at least 200 batted balls, with 50.2%.
The 2022 fantasy baseball game is live! Get the gang back together, or start a brand new tradition.
Join or start a league for free >>
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 all eight who managed at least 65 Barrels in 2021 also hit at least 35 home runs (a level only 19 hitters reached). Shohei Ohtani led with 78 Barrels, and he finished third in the majors with 46 home runs. In fact, the top three in Barrels in 2021 – Ohtani, Vladimir Guerrero Jr. and Salvador Perez – also comprised the top three for the season in home runs.
Again, this is a metric that can also be used to evaluate pitchers. Corbin Burnes allowed only 12 Barrels all season, the fewest among any pitcher who qualified for the ERA title, while J.A. Happ and Tarik Skubal tied for the most allowed by any pitcher (58). Burnes’ 0.38 HR/9 was, naturally, easily the majors’ lowest among pitchers who worked at least 120 frames, while Skubal’s 2.11 HR/9 was second-worst among that group (Happ’s 1.77 ranked 12th-worst).
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. Daniel Bard was last year’s example. His Spin Rate of 2,727 revolutions per minute on his four-seam fastball was second-highest among pitchers who threw at least 500 total pitches, behind only Trevor Bauer‘s 2,783. Bard also threw the pitch a blazing 97.5 mph on average, yet saw opponents bat .337 while slugging .518 against it.
In his case, neither velocity nor spin rate was the problem with the pitch, and Coors Field wasn’t completely the cause, either. Bard’s fastball, unfortunately, had some of the least vertical movement, with Statcast measuring it as minus-4.5 inches of drop on average, seventh-least among pitchers who threw at least 250 such fastballs. There have been theories as to why Bard’s fastball has proven so ineffective, but the bottom line is that the Spin Rate metric alone — nor average velocity, really — isn’t a solitary indicator of an elite pitch.
Lance McCullers Jr.’s curveball is a great example of a pitch made much more effective by a high spin rate. Among pitchers who threw at least 2,000 total pitches in 2021, his curveball generated 2,923 revolutions per minute, third-most behind only Garrett Richards’ (3,142) and Charlie Morton‘s (3,053). McCullers recorded 55 of his 185 strikeouts last season, and 462-of-750 over his big-league career, on curveballs. His success with that pitch alone is largely the reason fantasy managers are so forgiving of his injury absences over the course of his seven big-league seasons.
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.
Bryce Harper led the majors in xwOBA last season with a .430 mark, only percentage points better than 2020 leader Juan Soto‘s .430, and they finished ninth and second among hitters in terms of fantasy points. Among hitters who finished high on the leaderboard but whose raw fantasy numbers didn’t mirror it was Jorge Soler, who had a .354 xwOBA, compared to a .319 wOBA, with that 35-point difference being the fifth-widest of any batting title-eligible hitter. Max Kepler, who was previously discussed in Chapter 6, had a .347 xwOBA but only a .309 wOBA, another signal that he should have finished with much more appealing fantasy stats than he did. Accordingly, both players could be relative bargains in 2022 drafts.
These categories can also be used to identify regression candidates, players whose batted-ball outcomes were more favorable than they should’ve been. Frank Schwindel had the majors’ largest wOBA-xwOBA split among hitters with at least 250 plate appearances, 74 points (.329, compared to .403). Randy Arozarena was the highest-placing batting title-eligible hitter, with a 48-point spread (.350 wOBA, .302 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 Albert Pujols, who for the second consecutive season brought up the rear, averaging 22.4 in 2021.
Last season, Trea Turner (30.7 feet per second), Eli White (30.5), Jorge Mateo (30.4), Byron Buxton (30.0) and Jo Adell (29.9) were the top five performers in this category among players who had at least 50 “competitive runs” measured. Sure enough, this quintet managed to go 56-for-67 combined in stolen base attempts last season, with their combined total that low mainly because Turner was the only one who met the game’s batting title-eligible qualification (or came even remotely close to doing so).
There are plenty of other Statcast categories you can investigate, but these are the ones which have the most immediate relevance to fantasy managers.
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.
Predictably, the top three pitchers in FIP in 2021, among those who worked at least 120 innings, were Burnes (1.63), Trevor Rogers (2.55) and Wheeler (2.59), whose ERAs ranked second (2.43), sixth (2.64) and eighth (2.78) among that same qualification group. Good pitching generally breeds elite, across-the-board results.
Deeper down the list, however, you’ll find some pitchers who might have struggled through a good share of unfortunate bounces. Matt Harvey led 120-inning pitchers with a 1.67 differential in his FIP (4.60) and ERA (6.27), neither of which was particularly good, but right behind him was the entirely fantasy-relevant Eduardo Rodriguez, with a 1.42 gap (3.32 FIP, 4.74 ERA), who should be in line for a rebound with the Detroit Tigers. Aaron Nola, mentioned as well in Inning 6, had a 1.26 gap and should similarly be expected to catch more breaks in 2022.
On the other side of the scale, Marco Gonzales had the widest gap in FIP/ERA among pitchers with at least 120 innings, with minus-1.32 (5.28 FIP, 3.96 ERA). As he’s more of a pitch-to-contact than overpowering pitcher, not to mention an extreme fly-baller, he is sure to see regression in his ERA in 2022. Cal Quantrill, who had the second-largest gap with minus-1.18 (4.07 FIP, 2.89 ERA), is also in the ERA danger zone.
Others who stood out on the wrong side of the scale: Adrian Houser (4.33 FIP, 3.22 ERA), Casey Mize (4.71/3.71) and John Means (4.62/3.62).
Beware of putting too much stock into FIP and xFIP, however, with my recommendation to consider it as merely another evaluative tool in your toolbox. Quantrill, for example, has a decent ground-ball leaning (43.2%), was one of the game’s better pitchers at suppressing hard contact, and has a two-year pattern of substantially out-pitching his FIP. His return-to-earth number in 2022 might not be as catastrophic as other pitchers with as wide an ERA/FIP divide.
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 run precisely in the same order as that of FIP, it does grade the game’s best similarly: Burnes (2.61) was the ERA-qualified leader, followed by Max Scherzer (2.90) and Gerrit Cole (2.93), and all three also finished 1-2-3 among pitchers with at least 120 innings pitched. Carlos Rodon (2.96) and Clayton Kershaw (3.10) were fourth and fifth among the 120-inning group. SIERA was also much more favorable of American League Cy Young Award winner Robbie Ray‘s 2021 (3.21) than FIP was (3.69), alleviating somewhat the concerns fantasy managers might have about his wide ERA-FIP gap.
Once the hottest thing in fantasy baseball analysis, luck-based stats have taken more of a back seat 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. In both 2020 and 2021, the league’s average BABIP was .292, and it does vary by a few points from year to year depending upon the league environment. Today’s three-true-outcome-oriented game has caused that league-wide BABIP to drop by a few points, in large part because of the fewer overall batted balls put into play, but also the greater fly-ball rates that come with it.
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?
Last season’s Nos. 1 and 2 qualified hitters in BABIP were Tim Anderson (.372) and Starling Marte (.372), numbers that were 19 and 28 points higher than their career rates in the category. That’s enough of a change compared to their past norms that some batting average regression should be expected, but it’s also fair to point out that both are speedy hitters capable of legging out more infield hits than an average hitter. In Anderson’s example, too, he actually had higher BABIPs in both 2019 (.399) and 2020 (.383), with his year-over-year history of delivering elite numbers in the category encouraging enough to support a repeat.
A hitter who stood way out on this scale was Austin Riley, whose .368 BABIP was third-highest among batting title-eligible hitters. Riley did have numbers in that vicinity during the earlier stages of his minor-league career, but he was sub-.300 in the category in his first two big-league seasons and lacks the elite contact quality he would need to repeat the effort. He’ll almost inevitably regress in batting average in 2022.
Home Run per Fly Ball Percentage (HR/FB%): Mentioned 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 average in the category varies more than BABIP, but in 2021 was 10.8%. 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.
Last season, Patrick Corbin (17.1%) had the highest qualified rate, as well as the highest among pitchers with at least 120 innings, in the category among pitchers. Trevor Rogers had the lowest rate (5.2%) among pitchers with 120-plus frames, and Corbin Burnes (5.6%) had the lowest among ERA title-eligible pitchers. Corbin has had a 12%-plus rate in this department in 4-of-7 seasons since Tommy John surgery, but the last time he had a rate this high (17.6%, in 2016), he rebounded nicely with a 11.8% mark that helped him knock a full run-plus off his ERA. Hey, anything helps.
One other pitfall to consider with this category is the differing calculations across statistical sources. For example, FanGraphs had the league’s average Home Run per Fly Ball Percentage as 13.6%.
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 2021 it was 72.1%. Last season among ERA-qualified pitchers, Ray was the leader in the category (90.1%), while Nola (66.8%) brought up the rear. Ray’s Strand Rate was more than 13% greater than his career number (76.9%), while Nola’s was more than 7% lower than his career rate (74.0%), so it is highly likely that both will see some correction to their ERAs in 2022.
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.
A casual glance at the numbers might overlook the fact that Hunter Renfroe had one of the higher pop-up rates in baseball last season (11.6%, third-highest among batting title-eligibles), which is why his FanGraphs fly-ball rate was 43.4% but Statcast’s was merely 27.0%. Without considering that, one might see his move to Milwaukee’s homer-friendly Miller Park as a dream landing spot for his swing, thinking he hits a slew of fly balls rather than a decent chunk of harmless pop-ups. A deeper dive into Renfroe’s batted-ball metrics hint that he adapted his swing to compensate for Fenway Park’s closer left-field fence, too, so it’s a dangerous thing to assume that his 2021 profile will immediately translate to a boost in his 31 home runs (though he certainly could adapt his swing again and hit more).
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.
Inning 1: Fantasy baseball 101
Inning 2: League formats
Inning 3: Salary-cap drafts
Inning 4: Preparation
Inning 5: Roster optimization
Inning 6: Nine must-follow tips
Inning 7: Adjusting to league trends
Inning 8: Utilizing advanced stats
Inning 9: Mastering the player pool
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 2021, choose Player Type batters, set the Game Date >= as 2021-09-01, then choose Sort By xwOBA. You could also set a Min # of Results if you wish, say, 250.
As you can see, Lourdes Gurriel Jr. (.478) occupies the top spot using this split, while Tucker Barnhart (.194) ranks last among non-pitchers. Gurriel’s monster finish — he batted .301/.359/.634 with seven homers in a huge September that helped him rescue what was an otherwise lost season — gives him good cause for a rebound in 2022, especially considering the Toronto Blue Jays’ outfield should be less cluttered, helping pad his playing time in a way the team couldn’t in 2021.
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. My favorite report providing an example of some of the options is to check the standard stats page for pitchers using the 2021 home-games split. The Milwaukee Brewers, who play at Miller Park — a venue that hasn’t seen a sub-1.000 (neutral) home run Park Factor in a single season since 2008 — placed three pitchers among the 12 in ERA at home, Brandon Woodruff (2.31, 2nd), Corbin Burnes (2.85, 11th) and Freddy Peralta (2.88, 12th). All three of them, in fact, finished 2021 with a sub-1.00 HR/9, a testament to how immensely skilled each of them is.
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!