This week marks the halfway point of the regular season. I don't know about you, but for me time has flown by! It must be another consequence of the pitch clock.
This is also the time of the year many take stock of their fantasy baseball squads to decide how to approach the second half. Where is my team weak, and where do I have a surplus? Are any trades in order? Perhaps most importantly, it's time to assess both those who are underperforming on your roster as well as identifying players who are "out over their skis." Preparing for a fall can help soften the blow.
It has become mainstream to analyze pitching by comparing "actual statistics" to an expected version. The most common metric put under the microscope is ERA, as there are several easily accessible expected ERA formulas such as FIP, xFIP and SIERA, along with Statcast's xERA. Though they all have differing inputs (with some overlap), they all translate skills to what "should have happened." A significant difference between actual and expected stats often indicates that regression is forthcoming. A pitcher with an ERA below what was expected is probably going to post a higher ERA going forward, while someone with an ERA above what was expected will probably enjoy better days ahead, assuming little-to-no change to their skills.
Hitters can be evaluated in a similar manner. The frequency of contact (as well as the type of contact), yields expected outcomes. If a hitter's numbers differ greatly than expected, then they are candidates for some regression -- just like with pitchers and ERA.
One key point to understand is that just because regression is expected, that does not mean it has to happen. If we flip a coin six times, the "expected outcome" based on probability is that it will land on heads three times and tails the other three. However, probability also dictates that 3% of the time, we will flip six consecutive heads while another 3% will get six tails in six flips.
Now, if we keep flipping, the overall total will eventually regress toward a 50% heads/tails split, but the initial six flips are in the bank. What we should not expect is for the next six flips to even out the count. Believing this to be the case is what we call the "gambler's fallacy."
In baseball terms, if a hitter is currently batting .250 but has an expected batting average of .275, we should not predict that he will post a .300 mark over the second half to "even things out." The hitter should be expected to hit .275 over the final three months, finishing somewhere around .263. Again, some will end higher, some lower. All we can do is use probability to help us gauge the most likely outcome and manage our rosters accordingly.
Note: For a deeper dive into some of the terms we will be using in our analysis, such as hard-hit rate, barrels and run production, please click here to jump down to the glossary at the end of this article.
Destiny's dozen
What follows are 12 hitters whose production should be better over the second half. If you have any on these guys on your current fantasy roster, team improvement could be organic. This doesn't mean you can just sit back and "wait for it to happen," but you also don't need to be as aggressive, or take as many chances. On the flip side, if the current team manager of any of these batters is frustrated with their performance to date, perhaps some trade talks are in order.
Vladimir Guerrero Jr.: While his .280 average is 31 points below expected, his average exit velocity is the second highest of his career. His 56.2% hard-hit rate should portend to a BABIP over .370, but it's sitting at .304. Anticipating a 66-point bump the rest of the way is optimistic, but it's clear Guerrero has been shortchanged in the hit department.
Guerrero's barrels project only one more homer, so most of his uptick in production should be from batting average and the associated runs and RBIs. Keep in mind that the Blue Jays are due to score more runs anyway, further embellishing Guerrero's expected second half. To date, he has scored only 39 runs and knocked in 49. Over the past two seasons, he has averaged 107 runs and 104 RBIs.
Bryce Harper: His expected batting average is only 11 points higher than the actual number, but his "misfortune" has come in the power department. Even given that Harper missed around 30 games, he should still have more than just three homers. With his 12.9% barrel rate being 83rd percentile, he should be approaching double-digit home runs in his 204 plate appearances.
For his career, Harper has clubbed 308 doubles and 288 homers, nearly a 1:1 ratio. Harper has 10 two-baggers, more than three times the number of homers. His average fly ball distance is down, but not nearly enough to account for the dip in dingers. Harper's HR rate should be expected to ramp up.
Rafael Devers: The good news is that Devers is tied for 11th in the majors with 18 homers and tied for third with 60 RBIs. The bad news is that he's hitting a mere .241, almost 40 points shy of his career mark. The wicked good news is that the Boston slugger is lagging his expected mark in both areas.
Devers' expected average is .272, still a tad lower than normal, but far more palatable. His 51.8% Hard-hit rate is virtually the same as it's been the last two seasons, when he sported BABIPs of .329 (last year) and .307 (2021). Currently, it sits at .252, so it's fair to expect an impending positive correction. Devers' barrel rate is 13.4%, the second highest of his career. Devers has left the yard 18 times, but the barrels point to a total in the low 20s.
While his batting average and homers should both improve, Devers' batting average with runners in scoring position is .287, or 46 points above his overall average. With apologies to the "clutch" crowd, Devers has been fortuitous to gather hits with ducks on the pond. That said, if his average improves, more RBI should dovetail, likely countering any decline to any diminishing good luck.
Pete Alonso: Here's a hitter who has also been victimized in both average and power. His average is 48 ticks light while his slugging should be .056 higher. As a reminder, these deficits may not be precise, but they're disparate enough to identify Alonso as being unlucky.
Alonso's 42.3% hard-hit rate is the second lowest of his career, but it still should still be manifesting a BABIP much higher than his current .189. By all rights, it should be around .310, but after factoring Alonso's lack of foot speed and his home venue, expecting something closer to the career .274 BABIP he sported coming into the season is fair.
Not many would complain about his having gone yard 24 times, but Alonso's career best 15.9% barrel rate could probably have generated another homer or two.
Bobby Witt Jr.: Here's yet another player who checks in with a double dose of bad luck. Something we've not yet discussed is that a bump in batting average may also generate more stolen bases. This didn't apply to our first four hitters, but if Witt's average improves as expected, he could eclipse 50 steals as he's currently on pace for mid-to-high 40s.
To that end, Witt's 43.7% hard-hit rate translates to around a .315 average and perhaps even better, considering his speed. His current BABIP is .272, driving an actual .242 average to be 40 points below expected. Witt's expected slugging mark is a whopping 90 points shy of expected. Yes, his 11.3% Barrel rate is a marked improvement from the 8.7% he generated in his rookie season, but he should still have 3-4 more homers than the 12 he's actually hit so far.
Willson Contreras: Considering that Contreras has posted a .216 average, it's probably not a surprise he makes this list. Sure, some of that drop is the result of a move from Wrigley Field to Busch Stadium, but the bulk of the blame can be attributed to a .257 BABIP. Contreras' 45.5% hard-hit rate is his lowest since 2018, but it's still 68th percentile and should produce a BABIP north of .320, less some points since Contreras has below-average speed. Still, Contreras should be closer to the career .307 BABIP he had coming into the season.
One of the advantages of dealing for a catcher is that the upgrade over your current backstop could be more significant than anything you might achieve by improving other positions. Contreras might well be one of the best players to target in all of fantasy baseball.
Tim Anderson: Injuries have certainly dulled Anderson's first half, but so has a .282 BABIP, which checks in at around 60 points below his pre-2023 mark. On the other hand, Anderson's 41.8% hard-hit rate is a tad higher than last season when his BABIP was .347. Anderson' sprint speed is down, which no doubt is contributing to a suppressed BABIP, but there is also a good deal of bad luck involved.
Anderson has also yet to leave the yard. Sure, his power has been steadily declining, and a 3.4% barrel rate doesn't equate to much more than low double-digit homers for a full season, but Anderson has certainly deserved to trot around the bases at least once or twice.
Anderson is intriguing since not only does he have regression in his corner but, assuming he can shake his current shoulder woes, Anderson should be healthier over the second half. Even so, while he can lend a hand in the batting average department, he isn't much use in power, and returning to his old stolen base pace isn't a sure thing.
Spencer Torkelson: This sophomore sensation-in-waiting has taken strides from last season, but just how much is being masked by some misfortune. He's hitting only .218 (15 points higher than his rookie mark), but he should be closer to .250. His 48.4% hard-hit rate is almost seven ticks higher than in 2022, but it's not being reflected in his .266 BABIP. According to the research, Torkelson's BABIP should be over .340. That may be wishful thinking, but its current level is definitely too low. Torkelson's barrel rate has also grown from last year, but his nine homers are shortchanged one or two from what's to be expected.
One of the advantages of acquiring a young and still unproven player is that he could pair improvement due from regression with skills growth. It's truly not much, but Torkelson is striking out a tick less while walking a smidge more than last season, so perhaps he continues those trends and ends up even better indicated by his expected stats.
Eugenio Suarez: With Suarez, it's strictly about power as his expected slugging percentage is .113 higher than the real thing. His 12.9% barrel rate is Suarez's worst since 2018, but it's still 83rd percentile and should have generated more than the eight homers currently on his ledger. Suarez will be hard-pressed to match the 31 long balls he's smashed in each of the last two seasons, but if he maintains his current barrel rate, mid-20s is fair.
One of the positives when dealing for Suarez is that you don't have to pay for a high batting average. That is, if your primary need is power, and you have a buffer in batting average, the cost of acquisition for Suarez is more reasonable than a slugger who also helps you in the batting average category.
Keibert Ruiz: His appeal is that while most fantasy managers might realize he's hitting for a lower-than-expected average, they may not realize that his power surge is fully supported. That is, some could infer he's sacrificing average for more power, but the power has come on its own, while his average is artificially low.
Ruiz's calling card is outstanding contact and the key to "getting to the next level" is hitting the ball with more authority. Not only has Ruiz further reduced his already-low strikeout level, but he's also sporting a career high 36.7% hard-hit rate. OK, that's not eye-popping, but combined with a frugal 7.9% strikeout rate, Ruiz's .228 average should be at least 50 points higher. His .221 BABIP is around 60 points shy of where it should be according to his hard-hit rate. As for power, an 8.8% barrel rate is no great shakes, but it supports a 20-homer season, which is quite the leap from the seven he hit last year.
Ryan Mountcastle: Most importantly, let's hope Mountcastle continues to be clear from vertigo as he's embarking on a rehab assignment. Before he had to miss time, Mountcastle was incurring bad luck on BABIP. His 44.8% hard-hit rate portends to a BABIP in the .320 range, but it currently resides at just .256.
Hopefully, Mountcastle will be welcomed back by a batting average boost, but his primary bane was a low power output. He's due even more of an improvement in power as his 15.3% barrel rate is up a speck from last season. He swatted 11 long balls before being placed on the IL, but the expected stats indicate he could have cranked 14.
Jean Segura: The third baseman's inclusion is warranted via the numbers, but he makes this list mostly to have each position represented since he still carries 2B eligibility from last season. His usefulness is relegated to deeper leagues, especially those with a roster spot for a middle or corner infielder.
Segura's line is only .195/.262/.252, and that was buoyed by a Fenway Park homer in the opener of this week's interleague set between the Marlins and Red Sox. However, according to Statcast, Segura should be hitting closer to .241 and slugging in .332 territory. Again, it's nothing mind-blowing, but Segura could upgrade some deep-league infielders, and he's freely available in over 90% of ESPN leagues.
Ronald Acuna Jr.: Surprise! Let's do a baker's dozen. Given, there is likely no way to pry Acuna away from his current fantasy manager, but those rostering the individual who sits at the top of the ESPN Player Rater might be stunned to find out that he has actually been unlucky in 2023. His .328 batting average trails the expected .354 mark while his .568 slugging percentage falls short of the anticipated .659.
Keep in mind, part and parcel to all this analysis is a continuation of the current skill level -- and not even Acuna can be expected to sustain this pace. Still, it's fascinating to note that he could have posted an even better first half. Scary!
Glossary of terms
The chief elements of a hitter's performance subject to actual versus expected variance are batting average, power and run production. A player's hard-hit rate serves as a great indicator for batting average while barrel rate is a nice proxy for homers.
Hard-hit rate
Statcast defines Hard-hit rate as "contact with an exit velocity of at least 95 mph." Studies show a strong correlation between hard-hit rate and BABIP (batting average on balls in play). BABIP is one of the driving forces of batting average, along with strikeout rate and homers. It's not a perfect correlation, but a Hard-hit rate should return a specific BABIP. If a player's actual BABIP is significantly different that their expected level derived from hard-hit rate, a correction can be anticipated, assuming the hard-hit rate stays the same.
Barrel rate
Statcast defines a barrel as "contact within a specified range of exit velocity and launch angle such that the expected outcome is at minimum a .500 batting average and 1.500 slugging percentage." Barrel rate is the percentage of barrels per contact, with contact excluding bunts and popups. Barrel rate correlates quite well with home runs. Like with hard-hit rate and BABIP.
Run production
Runs and RBI should piggyback a higher average and more homers. However, there is another trick to help gauge whether a player's runs and/or RBI are artificially low or high. While some obstinately still believe in the notion of clutch hitting, it is just that -- a notion, and not a provable concept. Overall, the league's batting average with runners in scoring position is a few ticks higher than the overall batting average. This is because more pitchers working from the stretch, altered defensive positioning (playing the infield in and holding on runners) as well as a more limited pitch selection to avoid passed balls with runners on third base.
If a hitter's batting average with runners in scoring position is much higher than his average, his RBI total is inflated, and vice versa. Over the rest of the season, the rate of RBI should lessen or increase, depending on the direction of the discrepancy.
The same is true for runs, but here it is more of a "team stat" and not the individual's batting average with runner in scoring position. For example, if a team's batting average with runners in scoring position is much lower than their average, the team is collectively unlucky, so their scoring should pick up as the level normalizes. Ergo, the run scoring pace of players on the team should increase.
Through Wednesday's action, the overall league average is .248. Meanwhile, with runners in scoring position it's .254.