In last week’s article, Top 20 Starting Pitchers: Early NFBC Drafts , I promised to provide Cliff Notes for my favorite pitcher analytics. Well, here we are. By compiling this, I strive to give you an appreciation for some of the data at our fingertips and make it all a little more useful for pre-season and in-season roster management.

DISCLAIMER: By no means is this an all-inclusive list of pitcher analytics and the targets suggested are common throughout the industry or based on my own experience. Other industry colleagues will likely have different values they recommend. We fantasy baseball nerds are always striving to be better so please feel free to share your thoughts below. You know what they say, “put 10 baseball experts into a room and get 15 opinions.”

What I provide here are what I’ll call the basic analytics (ones that are commonly scored in fantasy or readily available to us) and the advanced analytics (those that require a Ph.D. to fully understand). I’ll do my best not to insult anyone’s intelligence with the basic stuff and not get too “into the weeds” with the advanced stuff. In the end, I’m hoping you find this a useful resource to print or link for quick reference later.

__BASIC ANALYTICS__

**BB/9 or BB%**, *Walk Rate*: Although the term varies within the industry, each refers to the number of walks produced by a pitcher. As the acronym implies, BB/9 (or Control rate) is simply the number of walks per 9 innings pitched and the best pitchers will have BB/9 rates of 2.5 or less. BB% is the number of walks allowed as a percentage of total batters faced. The league-wide BB% last season was slightly over 8% so the general recommendation is to target pitchers with a rate lower than 6%.

**K/9 or K%**, *Strikeout Rate*: Similarly, these terms are used within the industry to indicate the number of strikeouts produced by a pitcher. The higher the better of course. In 2022, the league-wide average K/9 was about 8.7 (or K% > 22%). These numbers are generally good for starting pitchers but I like to target relief pitchers with a K/9 over 10.0 (or K% > 27%).

**K/BB**, *Strikeout-to-Walk Ratio: *This measure of command simply tells us how many strikeouts are recorded for each walk allowed. Again, the higher the ratio the better, but how do we quantify it? Generally, the league average K/BB is about 2.5 so we want to be higher. As a gauge, the elite SPs (Gerrit Cole and Corbin Burnes) spun K/BB in the neighborhood of 5.0 last season, while Aaron Nola was more of an outlier at slightly over 8.0. Outside the top pitchers, most fantasy-targeted starters fall into the range of 3.5-4.5. ** **

**ERA**, *Earned Run Average*: A cornerstone to fantasy baseball, the ERA is defined as the number of earned runs a pitcher allows per nine innings. ERA was intended to be an ideal evaluation of pitchers but there are a few flaws with the stat due to the many factors that affect it, including defensive influences (beyond errors), park factors, etc. In fantasy baseball, we evaluate ERA differently between starters and relievers. Our goal is to find starting pitchers with sub-3.00 ERA’s. In 2022, there were only 19 pitchers with 28 or more starts who achieved this. Conversely, three out of every four relievers with 15 or more saves met this threshold.

While ERA will continue to be the metric used in fantasy baseball scoring, fantasy players are better served to use the ERA value in conjunction with the xERA (see below) to predict future productivity.

**BABIP**, *Batting Average on Balls in Play: *BABIP, also commonly referred to as Hit Rate (H%), measures a pitcher’s ability to prevent hits on balls in play. It removes walks, strikeouts and home runs from the batting average equation. BABIP doesn’t specifically consider the type or quality of contact (ground ball vs. fly ball) nor strength/weakness in team defense. So, the BABIP is generally not the most accurate predictor of pitching results on its own. Over the past few years, the league average BABIP was between .290-.300. So, a pitcher with a BABIP significantly different would be expected to see ERA movement in a direction toward the mean.

**VELO,** *Velocity*: This one doesn’t need much description. While we still generally look at a 100-mph fastball VELO as a gold standard, it’s quickly becoming less of a true measure. With so many pitchers reaching that threshold these days, hitters are becoming more accustomed to the increased speed. MLB hurlers can’t rely on velocity alone to be dominant pitchers, they need to make sure there is movement on the ball as well. Nice segue, Lineup Builder…

**SR**, *Spin Rate:* VELO and SR go hand-in-hand (pun intended), so I align these two analytics together. As the name implies, SR is the rate of spin after a pitcher releases, calculated as revolutions per minute (rpm). In addition to VELO (thanks, gravity), SR factors into the baseball’s movement. For example, a high SR intuitively creates more break on a curveball, but it also generally results in a fastball with a higher plane, giving the illusion of a rising fastball.

There are many nuances here beyond the combination of VELO and SR, so I’ll defer to the bigger brains out there to elaborate on the physics and mathematics of it all. For our purposes though, the average MLB SR is illustrated in the table below. Generally, we can use these values as more of a baseline for evaluating pitch effectiveness.

**WHIP**, *Walks + Hits Per Innings Pitched*: The other cornerstone stat for fantasy baseball, the WHIP is used to gauge how well a pitcher keeps runners off the bases. The stat is straight forward and the correlation between a low WHIP and being considered one of the better pitchers is directly linked. However, hit rate luck also factors in so we need to fine-tune this a bit to better assess a pitcher’s true skill. We do that by incorporating the expected WHIP (xWHIP) into our analyses (see below).

__ADVANCED ANALYTICS__

**Barrels:** A barrel is a batted ball with similar hit types in exit velocity and launch angle that results in a minimum .500 batting average and 1.500 slugging percentage. The batted ball requires an exit velocity of 98 mph to qualify as a barrel. As the exit velocity increases, the launch angle classified as a barrel also increases. The launch angle range grows two to three degrees for every mph increase on the batted ball. Once the batted ball reaches 116 mph exit velocity, a barrel is credited if the launch angle is between eight and fifty degrees.

Barrels are an important analytic because of what they translate to statistically. In 2021, barreled balls had a .772 batting average and 2.591 slugging percentage. Barrel rate has an extremely high correlation with HR rates as well and therefore is the most predictive power metric. In fact, almost 62% of all barrels resulted in a HR.

It’s easy to see why limiting barrels is a tremendously important task for pitchers. The stats available to gauge how a pitcher is limiting barrels is typically Barrels/BBE or Barrels/PA%.

** Barrels/BBE, ***Barrels per Batted Ball Events*: Simple ratio of barrels divided by batted ball events.

** Barrels/PA%, ***Barrels per Plate Appearance***: **Even simpler ratio of barrels per plate appearance.

Note the table below, where some of the top starting pitchers in 2022 are listed. Take special note of the ERA column and compare with both Barrels/BBE % and Barrels/PA%. Remember how I said a Barrel rate has an extremely high correlation with HR rates? I prefer the latter analytic, Barrels/PA%, which tracks very well with a pitcher’s ERA and therefore a very useful analytic for predicting pitcher performance.

AUTHOR NOTE: Before I move off this topic, I want to give a shoutout to the great @RotoClegg and all our friends at Fantrax for the outstanding graphic and detailed background on barrels I borrowed heavily from.

**xERA, ***Expected ERA: *xERA represents what a pitcher’s real ERA might be, calculated with skills-based measures and eliminating the influence on situation-dependent factors (i.e., ballpark, bullpen support, etc.). The difference between ERA and xERA helps predict future pitcher performance (see the table below). In addition to draft prep, this is a particularly good tool for in-season evaluation of pitchers for FAAB bids and trade offers.

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**FIP/xFIP,** *Fielding Independent Pitching/expected Fielding Independent Pitching*: FIP attempts to eliminate the influence of defense on the pitcher’s statistics by judging only on HRs, BBs, HB (hit batsmen) allowed and Ks. xFIP takes it a step further by assuming a league-average HR rate and incorporating a pitcher’s fly balls allowed.

FIP is particularly useful during the season to help identify pitchers whose fortunes should even out moving forward. Using FIP in conjunction with ERA to provide a stronger evaluation basis than ERA alone. Those with a positive “FIP – ERA” value should be in line for better overall ERA while those with a negative “FIP – ERA” may be benefiting from some good luck and see a negative correction on the horizon. Also, xFIP can be helpful when evaluating pitchers at ballparks with significantly different park factors (AL East comes to mind) or those changing teams.

**GB/FB, ***Ground Ball to Fly Ball Ratio: *This isn’t one of my favorite analytics to use but does have some value in the right circumstance. This self-described ratio describes what kind of contact a pitcher allows more frequently. Previous analysis of hitters has shown fly balls tend to make their way over the fence and become HRs 10-15% of the time. Therefore, we want to be cautious of “fly ball” pitchers, especially in certain ballparks. On the flip side, ground balls rarely become HRs, right? Well, we do see an occasional inside-the-park HR, but I’d submit that’s more likely a result of a line drive (not specifically covered here) than a ground ball.

There are many factors that make this a tricky analytic to rely on. Sample size is a huge factor, so in-season evaluation isn’t recommended until you have a month or two of good data. Park factors are also huge – are we talking Colorado or Miami? Also, not all hits are created equally. A “fly ball” can be anything from a pop-out to the catcher to a towering HR. Similarly, a “ground ball” can range from a screaming shot up the middle to a weak chopper to the SS. The one thing we can all agree on though, we want our pitchers to reduce line drives, as those have the lowest probability of becoming outs. As a general rule, sinker, cutter and curveball pitchers tend to produce more ground balls.

So, what is considered a good GB/FB ratio? Again, there isn’t a magic number or formula here. Consider the following 2022 stats:

Julio Urias and Zac Gallen were leaders in limiting BABIP, but you’ll note their GB/FB ratios and ERAs were markedly different. Similarly, Gallen and Max Fried had similar ERAs (important to us fantasy owners) but their GB/FB ratios and BABIPs are noticeably different. Now look at Gallen with Adam Wainwright – similar GB/FB ratios but drastically different BABIP and ERA.

Sure, I’m cherry-picking pitchers here but it doesn’t invalidate the point. I’d advise against using GB/FB ratio as a general analytic, but it does have a place in evaluating specific pitchers. Despite his tremendous ERA last season, I may have given a second thought to starting Julio Urias when the Dodgers visited certain AL East parks.

**STR**, *Strand Rate*: This measures the percentage of runners on base that a pitcher strands (earned runs only). The baseline STR in 2022 was 72%. The fantasy industry generally favors pitchers with a STR over 75%. However, be careful with extreme STR rates over 80%. These pitchers will generally have an artificially low ERA that is prone to normalizing. On the flip side, a STR below 65% will generally have an inflated ERA that is also primed for normalizing. In this latter case, this analytic is another useful metric to evaluate pitchers as trade or waiver targets.

**SwK**, *Swinging Strike Rate: * This measures the percentage of total pitches a batter swings and misses. This metric helps validate and forecast a pitcher’s K/9 rate, those who are both surging and declining. The industry tends to use a SwK of 9.5% as a baseline threshold and 10.5% to identify difference-makers. This metric is unique in that starting pitchers who have a 12% SwK will almost always achieve 25% K rates. On the other end, those at 7% or lower have virtually no chance at even reaching the baseline K rates.

**xWHIP**, *expected WHIP:* Similar to the ERA/xERA discussion above, xWHIP is used to better eliminate the effect of luck that factors into the WHIP. Specifically, hit rate luck (i.e., line drive rates, hits per batted ball type, and double plays per ground ball) contributes to the WHIP, thus not reflecting the true skill of the pitcher. In the calculation of xWHIP, values for the hit rate luck parameters are set at league average rates, allowing a pitcher’s BB%, K%, rate of hit batters and GB/FB ratios to determine their true skill in keeping batters off the base paths. Generally, expect the WHIP to move toward the xWHIP as more data becomes available.

There you have it, my Cliff Notes for pitcher analytics. Like I stated earlier, these are the ones I find most useful in my fantasy baseball prep and in-season management. Of course, there are many other acronyms in this baseball soup that I didn’t cover here…and even more being contemplated every day by the big brains out there. For further research, I recommend you go to resources like Baseball Savant (https://baseballsavant.mlb.com) and other reputable outlets to further your study.

Let me know what you think. Also, if you have other favorites, share them below and briefly describe how and why you use it. I’ll be interested to hear your thoughts. This was a fun January project to put together. I hope my readers find it useful as well.

Like last week, I want to give another plug for all the great stuff available at Razzball before signing off. Create a shortcut to the Razzball rankings page (https://razzball.com/2023-fantasy-baseball-rankings/) and allow Grey (@Razzball) and the rest of us to help you prepare for the fantasy baseball season. See you next week!