(The full, nine-inning Playbook was originally published during the spring of 2020. It has been updated for 2021 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 bubble-gum-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 tossed about. Which of these matter for our purposes? But, 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.
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 an automated tool that analyzes players' skills, using radar and camera systems that began being 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 six seasons (2015-20). 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, full player statistics reports 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 2020 was 88.0 mph, and it took a 92.1 mph number for a player to place in the 90th percentile, with 86.0 mph placing him in the 10th percentile. Fernando Tatis Jr. (95.9 mph) was the major league's leader in the category, among those eligible for the batting title. The majors' worst batting title-eligible in the category was Victor Robles (82.2 mph), the second consecutive season he has brought up the rear in the category, while Ender Inciarte (78.2 mph) was worst among those with 50 balls in play.
Turning to the pitchers, Kenta Maeda (85.3 mph) had the lowest average Exit Velocity among ERA qualifiers, Phil Maton (82.7 mph) was best among relievers with at least 20 innings and Ryan Yarbrough (82.6 mph) was the best overall pitcher with at least 100 batted balls allowed. Conversely, Framber Valdez (91.4 mph) allowed the highest average Exit Velocity among ERA qualifiers, Josh Staumont (94.4 mph) was the worst reliever with at least 20 innings and Jorge Lopez (92.8 mph) was the worst overall pitcher who allowed at least 100 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 fly-ball hitters or pitchers, while those with lower launch angles are termed ground-ball hitters or pitchers. To that end, Joey Gallo's 26.8 degree average Launch Angle was the highest among batting title-eligible hitters, and his 29.8% fly-ball rate was, predictably, seventh-highest among those 142 hitters. Meanwhile, Isiah Kiner-Falefa's 0.8 degree average Launch Angle was lowest among batting title eligibles, and his 15.6% fly-ball rate was also the league's 15th-lowest.
Pitching-wise, Framber Valdez's minus-0.8 degree average Launch Angle was the lowest among ERA qualifiers, and his 9.4% fly-ball rate was also the lowest among that group. Matthew Boyd (20.9 degree aLA) was on the opposite end of the scale, and his 26.6% fly-ball rate was second-highest among the group.
Hard Hit Rate: This one takes Exit Velocity one step further, designating a "Hard Hit" batted ball 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 among pitchers and fantasy success.
The league's top batting title-eligible hitter in terms of Hard Hit Rate in 2020 was Fernando Tatis Jr. (62.2%), who finished third overall on the Player Rater. If you're looking for high-placing names that might surprise you in this department, consider Miguel Sano and his 57.3% Hard Hit Rate, the second consecutive season he enjoyed a rate of at least 57%, which played a big part in his hitting 47 home runs from 2019-20; or Evan White and his 52.5% Hard Hit Rate, which probably explains how he was able to hit eight home runs despite a .176 batting average in 2020.
This metric, as with each of the previous two, can also be used to evaluate pitchers, specifically their ability to limit hard contact. Kenta Maeda led all ERA-qualified pitchers in Hard Hit Rate (24.7%), while Max Fried (24.5%) paced all pitchers who allowed at least 100 batted balls. They finished 16th and 51st overall on the Player Rater, thanks in large part to their ability to suppress hard contact.
One of the more troubling Hard Hit Rates on the pitching side came from another pitcher previously mentioned: Framber Valdez, the No. 72 overall player and No. 23 starting pitcher on the 2020 Player Rater, had the majors' highest number (48.7%) among ERA qualifiers.
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 three of the seven who managed at least 25 Barrels in 2020 also hit 17-plus home runs (a level only six hitters reached all year). Fernando Tatis Jr. led with 32 Barrels, and he finished tied for fourth in the majors (and one off the National League lead) with 17 home runs.
Again, this is a metric that can also be used to evaluate pitchers. Hyun Jin Ryu allowed only six Barrels all season, the fewest among any pitcher who qualified for the ERA title, while Jon Lester allowed the most of any pitcher (25).
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 results in a boost in pitch effectiveness. Mike Minor, who was also last year's example, in 2020 threw a four-seam fastball that had an average Spin Rate of 2,587 revolutions per minute, seventh-highest among pitchers who threw at least 200. He also threw it only 90.6 mph on average, diminishing the pitch's chances of moving as significantly as, say, Trevor Bauer's (2,776 Spin Rate, 93.5 mph) or Garrett Richards' (2,626 and 95.2), which ranked first and fifth in the cateegory. It did help Minor, but this metric isn't an instant indicator of an elite pitch.
Framber Valdez's curveball is a good example of a pitch with the kind of high spin that boosts its effectiveness. His generated 2,982 revolutions per minute last season, fourth-most (minimum 100 thrown) behind only Lucas Sims' (3,334), Seth Lugo (3,213) and Dustin May's (3,090). Valdez generated 60 of his 76 strikeouts on curveballs and limited hitters to a .124 batting average with it, which is how he was able to overcome some of the other troubling metrics cited earlier.
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 the player's hitting ability.
Juan Soto paced the majors in xwOBA last season with a .470 mark, 21 points higher than any other hitter's, which underscores how exceptional his year was in spite of its late start. There were some players relatively high on the leaderboard who seemed to underperform in terms of raw fantasy numbers, hinting improvement in 2020: Bryce Harper had a .435 xwOBA, compared to a .393 wOBA, with that 42-point difference the fourth-widest of any batting title-eligible hitter in baseball. Nick Castellanos, meanwhile, had a wOBA (.324) 32 points beneath his xwOBA (.356). Either player could be expected to improve somewhat this season.
These categories can also be used to identify regression candidates, players whose batted-ball outcomes were more favorable than they should've been. DJ LeMahieu had the majors' largest wOBA-xwOBA split, 67 points (.355, compared to .422). Ryan Mountcastle (.371 wOBA, .319 xwOBA, 52 point difference) also placed high on the list.
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 brought up the rear with 22.0.
Last season, Tim Locastro (30.7 feet per second), Roman Quinn (30.5), Adam Engel (30.3), Byron Buxton (30.0) and Trea Turner (30.0) were the top five performers in this category among players who had at least 10 "competitive runs" measured. Sure enough, this quintet managed to go 31-for-36 combined stealing bases last season, with their combined total that low mainly because three of them played sporadically (Locastro, Quinn and Engel).
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.
Predictably, the top three qualifiers in FIP in 2020 were Shane Bieber (2.07), Yu Darvish (2.23) and Jacob deGrom (2.26), whose ERAs ranked first (1.63), fourth (2.01) and seventh (2.38) in baseball. Good pitching generally breeds elite, across-the-board results. Deeper down the list, however, you'll find some pitchers who might've struggled through a good share of unfortunate bounces: Zack Greinke led the league with a 1.23 differential in his FIP (2.80) and ERA (4.03). Luis Castillo had the seventh-largest gap in that direction (0.57, as his FIP was 2.65 and ERA 3.21), strengthening his case for a top-15 starter season in 2021. Even Andrew Heaney, he of the 3.79 FIP and 4.46 ERA, seemed not to catch as many breaks as he should.
On the other side of the scale, Chris Bassitt had the widest gap in FIP/ERA in either direction last season, with minus-1.30 (3.59 FIP, 2.29 ERA). As he's neither a high-strikeout pitcher, extreme ground-baller or a hurler elite at minimizing hard contact, he is sure to see regression in his ERA in 2021.
Others who stood out on the wrong side of the scale: Zach Davies (3.88 FIP, 2.73 ERA), Anthony Senzatela (4.57/3.44) and Dallas Keuchel (3.08/1.99).
Beware putting too much stock into FIP and xFIP, however, with my recommendation to consider it merely another evaluative tool in your toolbox. Davies, for example, has a pitching style that helps boost his chances of posting ERAs lower than his FIPs, having posted a FIP more than a full run higher than his ERA in each of the past two seasons.
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: Bieber (2.52) was the ERA-qualified leader, followed by deGrom (2.70), Kenta Maeda (2.92), Trevor Bauer (2.94) and Darvish (3.14). SIERA was also much more favorable of Gerrit Cole's 2020 (3.21) than FIP was (3.89), alleviating concerns fantasy managers might have about his wide ERA-FIP gap.
'Luck'-based statistics
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 2020, the league's average BABIP was .292, and it does vary by a few points from year to year depending upon the league environment.
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 Michael Conforto (.412) and Donovan Solano (.396), numbers that were 107 and 65 points higher than their career rates in the category. Neither saw a significant shift in his batted-ball distribution or contact quality, and in fact, it could be argued that both performed slightly worse on batted balls compared to 2019, so both should be expected to regress somewhat in 2021.
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 2020 was 12.2%. 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, Carlos Carrasco (16.3%) had the highest qualified rate in the category among pitchers, while Dallas Keuchel (4.9%) had the lowest. Carrasco has struggled in this category the past two years (17.8% in 2019), but had a 11.9% mark from 2015-18, seasons during which he remained mostly healthy. Keuchel's was a full four percentage points beneath his next-lowest single year number, and 7.5% lower than his career rate (12.3%). Both pitchers should be expected to perform at closer to their career averages in 2021 and beyond.
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 14.8%, while our internal pitch-tracking tool had it as 12.2%.
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 2020 it was 71.8%. Last season among ERA-qualified pitchers, Bieber was the leader in the category (91.1%), while Johnny Cueto (63.3%) brought up the rear. Next-closest on the trailing side was German Marquez, whose 65.5% rate was more than four full percentage points lower than his career number (71.7%), supporting his rebound prospects.
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.
A casual glance at the numbers might overlook the fact that Max Kepler had one of the higher pop-up rates in baseball last season (15.4%), which is why his FanGraphs fly-ball rate was 45.6% but Statcast was merely 22.1%. Without considering that, one might assume that he's more capable of a rebound to his 36-homer level of 2019 than he truly is. Strangely, FanGraphs and Statcast also disagreed on Kepler's hard-contact rate, as the former had him producing it on 32.4% of his batted balls, the latter 38.2%. The variance makes the case for a significant rebound weaker.
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 2020, choose Player Type batters, set the Game Date >= as 2020-09-01, then choose Sort By xwOBA. You could also set a Min # of Results if you wish, say, 100.
As you can see, Atlanta Braves teammates Freddie Freeman (.493) and Marcell Ozuna (.480) occupy the top two spots using this split, while John Ryan Murphy (.155) ranks last among non-pitchers. Ozuna's high placement on the list is compelling evidence that his 2020 power and run production were somewhat sustainable, though his .338 batting average is almost certain to correct.
FanGraphs is another site that offers custom statistics reports, including those you can download. Here is where you can find the basic 2020 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 players' splits, as well as to request numbers within a Custom Date Range. My favorite report providing an example of some of the options -- and understand that this references a past season due to the shortened 2020 -- is to check the Dashboard for pitchers during the second half of 2019. Jack Flaherty's absurd second half numbers (0.91 ERA, .206 BABIP, 94.2% Strand Rate) highlighted his skills improvements that you can also see in the Pitch Value and Plate Discipline reports, and while he did show expected regression in 2020, they serve a reminder of the ace-caliber raw ability he possesses.
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" of the Playbook, and it extracts some of my favorite findings using many of the tools discussed above to help you master the 2021 player pool