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Advanced stats to use for fantasy baseball

Jorge Soler clubbed an AL-best 48 home runs last season and also led the majors in Barrels with 70. Cody Glenn/Icon Sportswire

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 system. That means this data, in full, is only available for the past five seasons (2015-19). 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 2019 was 89.0 mph, and it took a 91.3 mph number for a player to place in the 90th percentile, with 83.5 mph placing him in the 10th percentile. Nelson Cruz (93.7 mph) was the major league's leader in the category, among those eligible for the batting title, and Aaron Judge (95.9 mph) led among players who put at least 100 batted balls into play. The majors' worst batting title-eligible in the category was Victor Robles (81.0 mph), while Billy Hamilton (78.3 mph) was worst among those with 100 balls in play.

Turning to the pitchers, Kyle Hendricks (85.2 mph) had the lowest average Exit Velocity among ERA qualifiers, Michael Lorenzen (84.5 mph) was best among relievers with at least 50 innings and Julio Urias (83.2 mph) was the best overall pitcher with at least 100 batted balls allowed. Conversely, Mike Leake (90.4 mph) allowed the highest average Exit Velocity among ERA qualifiers, and Nick Wittgren (92.0 mph) was both the worst reliever with at least 50 innings as well as 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, Rhys Hoskins' 24.0 degree average Launch Angle was the highest among batting title-eligible hitters, and his 33.7% fly-ball rate was, predictably, second-highest. Meanwhile, Wilson Ramos' 0.0 degree average Launch Angle was lowest among batting title eligibles, and his 13.8% fly-ball rate was also the league's lowest.

Pitching-wise, Dakota Hudson's 3.0 degree average Launch Angle was the lowest among ERA qualifiers, and his 13.9% fly-ball rate was also the lowest among that group. Matthew Boyd (18.8 degree aLA) was on the opposite end of the scale, and his 27.3% fly-ball rate was sixth-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 2019, again, was Nelson Cruz (51.5%), who finished 53rd overall on the Player Rater (and only that low because he missed 44 games). Miguel Sano (57.2%) had the highest rate among players who put at least 100 batted balls into play. If you're looking for a high-placing name that might surprise you in this department, consider Kyle Schwarber and his 51.2% Hard Hit Rate. Sure, he set a career best with his 38 home runs, but he also finished only 139th overall (and 37th among outfielders) on the Player Rater.

This metric, as with each of the previous two, can also be used to evaluate pitchers, specifically their ability to limit hard contact. Anibal Sanchez led all ERA-qualified pitchers in Hard Hit Rate (28.3%), while Sergio Romo (23.5%) paced all pitchers who allowed at least 100 batted balls. While neither is considered a fantasy standout, this skill helps explain how they were able to nevertheless finish 177th and 176th overall on the Player Rater, or 54th among starters and 29th among relievers.

Here's one of the more puzzling, and somewhat concerning, Hard Hit Rates on the pitching side: Shane Bieber, the 14th-best rotisserie player last season, had the second-highest number (43.1%) 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 five of the seven who managed at least 60 Barrels in 2019 also hit 40-plus home runs (the amount required for a top-10 finish in that category). Jorge Soler led with 70 Barrels, and he finished third in the majors (and first in the American League) with 48 home runs.

Again, this is a metric that can also be used to evaluate pitchers. Max Fried allowed only 21 Barrels all season, the fewest among any pitcher who qualified for the ERA title, while Mike Leake allowed the most of any pitcher (59).

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, for example, threw a four-seam fastball that led the majors in 2019 with an average Spin Rate of 2,650 revolutions per minute, but the fact that he threw it 92.5 mph on average diminished the pitch's chances of moving as significantly as, say, Justin Verlander's (2,577 Spin Rate, 94.6 mph) or Gerrit Cole's (2,530 and 97.1), which ranked second and fourth. It did help Minor, but this metric isn't an instant indicator of an elite pitch.

Charlie Morton's curveball is a good example of a pitch with the kind of high spin that boosts its effectiveness. His generated 2,886 revolutions per minute last season, fourth-most behind only Sonny Gray's (2,988), Walker Buehler's (2,915) and Gerrit Cole's (2,886). Sure enough, Morton generated 136 of his 240 strikeouts on curveballs, and he limited hitters to a .151 batting average with it.

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.

Unsurprisingly, Mike Trout paced the majors in xwOBA last season with a .455 mark, 26 points higher than any other hitter's. There were some players relatively high on the leaderboard who seemed to underperform in terms of raw fantasy numbers, hinting improvement in 2020: Marcell Ozuna had a .382 xwOBA, compared to a .336 wOBA, with the 46-point difference the widest of any batting title-eligible hitter in baseball. Mookie Betts, meanwhile, had a wOBA (.380) 28 points beneath his xwOBA (.408). 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. Fernando Tatis Jr. had the majors' largest wOBA-xwOBA split, 53 points (.398, compared to .345). Tim Anderson (.363 wOBA, .328 xwOBA, 35 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 Brian McCann, who brought up the rear with 22.2.

Last season, Tim Locastro (30.8 feet per second), Trea Turner (30.4), Byron Buxton (30.3), Garrett Hampson (30.1) and Roman Quinn (30.1) 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 89-for-100 combined stealing bases last season, with their combined total that low only because four of them played sporadically (Locastro, Buxton, Hampson and Quinn).

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 2019 were Max Scherzer (2.45), Gerrit Cole (2.64) and Jacob deGrom (2.67), whose ERAs ranked eighth (2.92), third (2.50) and second (2.43) 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: Jose Quintana led the league with a 0.89 differential in his FIP (3.80) and ERA (4.68). Lance Lynn had the sixth-largest gap in that direction (0.54, as his FIP was 3.13 and ERA 3.67), strengthening his case for a 2020 repeat. Even German Marquez, he of the 4.06 FIP and 4.76 ERA, seemed not to catch as many breaks as he should.

On the other side of the scale, Dakota Hudson had the widest gap in FIP/ERA in either direction last season, with minus-1.58. In fact, that space between his 4.93 FIP and 3.35 ERA was the third-largest among qualifiers this entire century, and while the earlier note indicated that he had an extreme ground-ball leaning that helps minimize damaging big hits, it's almost certain he'll see regression in his ERA in 2020.

Others who stood out on the wrong side of the scale: Mike Fiers (4.97 FIP, 3.90 ERA), Jeff Samardzija (4.59/3.52) and Mike Soroka (3.45/2.68).

Beware putting too much stock into FIP and xFIP, however, with my recommendation to consider it merely another evaluative tool in your toolbox. Returning to Hudson's example, the style of pitcher that he is helps boost his chances of posting ERAs routinely lower than his FIPs. And returning to Fiers, bear in mind that his career FIP (4.45) is 0.43 higher than his career ERA (4.02), and he has had 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: Cole (2.62) was the ERA-qualified leader, followed by Scherzer (2.93), Justin Verlander (2.95), deGrom (3.29) and Shane Bieber (3.36). Bieber's placement there -- and bear in mind his FIP was also a solid 3.32 -- mitigates some of the concern raised by the earlier note about his Hard Hit Rate.

Two players stand out as potential values in the SIERA column: Marquez (3.85, 17th-best among qualifiers) is a name to keep in mind thanks to his much-stronger-than-his-results skill set, if you can manage to navigate through his treacherous Coors Field assignments, while Matthew Boyd (3.61, 11th) might not be quite the source of worry that his 5.51 second-half ERA or high fly-ball rate suggests.

"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 2019, the league's average BABIP was .298, 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 Yoan Moncada (.406) and Tim Anderson (.399), numbers that were 37 and 54 points higher than their career rates in the category. Neither saw a significant shift in his batted-ball distribution or contact quality, so both should be expected to regress somewhat in 2020.

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 2019 was 12.0%. 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, Yu Darvish (17.3%) had the highest qualified rate in the category among pitchers, while Marco Gonzales (8.5%) had the lowest. Darvish's number was nearly five full percentage points higher than his next-highest in a previous season, and 5.8% higher than his career number (11.5%), while Gonzales's was more than a half a percent lower than his next-lowest single year number, and 1.1% lower than his career rate (9.6%). Both pitchers should be expected to perform at closer to their career averages in 2020 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 15.3%, while our internal pitch-tracking tool had it as 12.0%.

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 2019 it was 72.3%. Last season among ERA-qualified pitchers, Verlander was by far the leader in the category (88.4%), while Joe Musgrove (63.2%) brought up the rear. This is one of the tools that supports Musgrove's 2020 improvement 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 Rougned Odor had one of the higher pop-up rates in baseball last season (11.0%), which is why his FanGraphs fly-ball rate was 47.9% but Statcast was merely 21.7%. Without considering that, one might assume that he's more capable of better power numbers than he is. Strangely, Odor also had the largest variance between ground-ball rates of any batting title-eligible hitter: FanGraphs had him with 34.7%, while Statcast and our internal pitch-tracking tool had him with 38.7%. The variance was particularly interesting in that he had one of the higher Hard Hit Rates, 45.7% on FanGraphs and 45.6%, which does support his quest for power, but the sum of the parts suggests that he might have one of the wider range of outcomes of any player for 2020.

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 second half of 2019, choose Player Type batters, set the Game Date >= as 2019-07-11, then choose Sort By xwOBA. You could also set a Min # of Results if you wish, say, 100.

As you can see, Jorge Soler (.437) is the leader using this split, while Anthony Bemboom (.143) ranks last among non-pitchers. That's further evidence that Soler's 2019 breakthrough was legitimately skills-driven, and to take it an additional step further, he had a .443 xwOBA from Sept. 1 forward as well.

FanGraphs is another site that offers custom statistics reports, including those you can download. Here is where you can find the basic 2019 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, to provide an example of some of the options, is to check the Dashboard for pitchers during the second half of 2019. Look at Jack Flaherty's absurd second half: a 0.91 ERA, .206 BABIP and 94.2% Strand Rate! While he showed skills improvements that you can see in the Pitch Value and Plate Discipline reports, there's little doubt that he's going to see some regression in those rates in 2020.

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, coming next week, and it extracts some of my favorite findings using many of the tools discussed above.