It is always difficult to for fantasy baseball managers to know when to give up on a player, or when to hold onto them. It is also difficult to identify players who are over/underperforming and when to buy or sell them. Sometimes fantasy managers are ready to give up on a player because of lackluster performance, when there are signs that improvement is coming, or hang onto a player for too long because of a hot start, when there are signs of imminent regression. It is extremely important for fantasy managers to know when to buy low and sell high. Here is one player every fantasy manager should be looking to trade away and one every manager should be looking to acquire.

Sell High

Fantasy managers should try to trade away starting pitcher Carlos Rodon RIGHT NOW. The 12.96 K/9 rate is a career best for Rodon, and the 0.72 ERA is incredibly impressive. While he has made adjustments in some areas to contribute to these improvements, all signs point to a downward turn at some point in the near future. This is the perfect time to capitalize on his value.

One major adjustment for Rodon was adding a curveball to his pitch mix. He’s only thrown it 14 times so far this year, but it has a 100% swing and miss rate.

He’s added velocity to his three other pitches – a four seam fastball, slider, and changeup. Despite these changes and improvements, each pitch has a higher expected batting average against than the actual batting average against, as well as higher xSLG and xWOBA than their standard versions. These are signs that regression is imminent.

Another sign of regression for Rodon is the .130 BABIP, which for pitchers is typically a sign of good luck. The low pitching BABIP could also mean that the opponents he’s pitched against have had lackluster offenses, or that his defense has been performing extremely well. The White Sox have one of the worst team fielding percentages in baseball, so he isn’t being helped by an exceptional defense. Instead, Rodon has had some fortunate matchups including two games against Cleveland, one against the Tigers, and one against the Mariners. Rodon will not have easy matchups all season. The Tigers have the worst run differential in the MLB, the Mariners have a -7 run differential, and the Cleveland baseball team has the lowest of all the positive run differentials at +7.

Finally a 5% HR/FB rate is a sign of good fortune as well. The league average HR/FB rate for pitchers usually sits around 11-12%, meaning around 11 or 12% of fly balls turn into home runs. Rodon’s fly ball rate is 43%, and only 5% of those fly balls are turning into home runs. That number should even out, which will end up regressing his ERA. That is why his xERA, FIP, xFIP, and SIERA all think he is closer to a mid-2 to mid-3 ERA pitcher than a sub-1 ERA pitcher.

Even though the projections think his ERA will still be very solid, he will likely be on an innings limit after not pitching significant innings since 2018. That season he threw 120 innings. His career high is 165 innings in 2016. This is a perfect time to sell high on Rodon.

Buy Low

Kyle Tucker has been a frustrating player, not only this season, but also over the course of his major league career. In the minor leagues, Tucker flashed 20/20 and even 30/30 potential. He hit for average across every level of the minors. He just has not had the same success at the major league level so far, but he has shown that the potential is still there. In 489 major league plate appearances across four seasons, Tucker has 18 home runs and 16 steals. His plate appearances this season should eclipse his career total, since this is the first time he has been given significant time as a starter. Although he has not done much with his time so far in 2021, it is not entirely his fault, and he is a perfect player to buy low.

Many managers who roster Kyle Tucker are at a point where the frustration is boiling over. He may be getting dropped, or managers may be trying to sell him off for whatever they can get. If that is the case in your league, then you should buy. The first sign that he should and could be doing better is a very low .177 BABIP, which is nearly 100 points lower than his career BABIP. This is clearly suppressing his batting average. Some might argue that the batting average may be low because of discipline issues, but that is not the case. He is striking out only 19% of the time, which is the second lowest rate of his short MLB career.

His other plate discipline metrics show that he is a disciplined hitter. He has nearly an 80% contact rate, and goes down swinging only 10% of the time. Other players with comparable swinging strike rates include Freddie Freeman, Trevor Story, and Aaron Judge, all of whom have lower overall contact rates than Tucker.

Kyle Tucker’s contact metrics also show hints of a good hitter underneath the lackluster batting average. He has the second best exit velocity, max exit velocity, barrel rate, and hard hit rate of his career. According to Statcast, his expected batting average, expected slugging, and expected wOBA are all significantly higher than his actual numbers. All of this data suggests some improvement is on the horizon.

One issue for Tucker is a 15% soft contact rate, which would be the highest of his career. Some of that soft contact should end up turning into medium or hard contact over the rest of the season and result in more productive at-bats. Many of his underlying stats from this year are similar to what he did in 2019, when he batted .269, so fantasy managers should expect a return to form this season.

This might not be the year where he puts it all together, but it is reasonable to expect him to improve this season. If he continues to make good contact and stay relatively disciplined, the hits will come, the BABIP will improve, and so will the batting average, but do not tell your fellow managers that! Buy low on Kyle Tucker, and thank me later.

Today we are going to get into a type of data that hitters and pitchers are using to develop and improve their games. It’s called Statcast data, and today we are going to focus specifically on the hitting Statcast statistic Exit Velocity. Let’s get into it.

The Stat

Exit velocity is exactly what it sounds like. In simple terms, it’s how hard a ball is hit off the bat. Now before we dig into it, I want to give you some disclaimers. First, exit velocity it by no means a be-all-end-all statistic. And second, we’ll be looking at other statcast data that you can use along with exit velocity in your analysis of players. Our goal for today is just to understand what it is, how to find it, and how to apply our understanding of it to evaluate hitters in fantasy baseball.

Exit velocity absolutely has a correlation to hard contact rate, which I went over in episode 3. So if you need a refresh of hard contact rate, I suggest going back to listen. Generally, the harder a ball is hit, the farther it goes. One piece that we’ll talk about in the future is how launch angle factors into that. You can hit a ball super hard straight up or straight down, and it won’t matter how hard you hit it because it will be an out. This is why exit velocity is not the only thing we are going to factor into our analysis.

Another thing you have to consider with exit velocity, before we do some analysis, is that a player who strikes out a lot can have a really excellent exit velocity, but if they strike out a lot, it won’t matter because the production won’t be there. So you can’t just take exit velocity for what it is on the surface. You need to look at the whole picture and apply it along with other data points in your evaluation.

You won’t be surprised to know that some of the best batters in the MLB are on the leaderboard for average exit velocity. Aaron Judge, Nelson Cruz, Christian Yelich, Josh Donaldson, and Matt Chapman all fall in the top 10. Franmil Reyes with Cleveland is number four on the list, and I really like him for this season. Power stats are going to be valuable in this shortened year and I think he has potential to lead the league in home runs. Sure he could end up with a .240 batting average, but his power is elite. Yoan Moncada, Kyle Schwarber, and Shohei Ohtani round out the top 10 in average exit velocity for last season.

Why Is This Stat Important?

Understanding exit velocity is important, although the best way to use it is to help understand fluctuations in a player’s BABIP, and evaluating the legitimacy of the number of home runs a batter is hitting.

Remember, BABIP is the “luck” statistic for hitters – it factors in the outcome of the batted balls in play which can change depending on the defense. Yoan Moncada, for example had a top 10 average exit velocity last season, and had an insane .406 BABIP. So his exit velocity helps us evaluate him to say that while a .406 BABIP may be unsustainable, he may have a high BABIP year to year because he hits the ball hard, hits the ball do different fields, and he hits different types of balls as well with a solid spread of line drives, ground balls, and fly balls. One thing to remember is that the speed of a ball off the bat can affect how a ball is played by the defense. Think of a ball pulled hard down the third base line by a right handed hitter. For Anthony Rendon or Nolan Arenado, it might be an easy play. For Vlad Jr., it could be more difficult. So a high exit velocity could also lead to a higher BABIP – you have to pay attention to the types of balls a batter is hitting – line drives, ground balls, or fly balls. What you want to see from a player with a high exit velocity is fly balls, because it’s likely those balls turn into home runs. Or you want to see ground balls with a high BABIP, because that means the batter is hitting into gaps or hitting balls that are difficult for the defense to get to. So for a player like Moncada, you may look at his high BABIP and say, “Oh he is getting super lucky.” But if you understand exit velocity and pay attention to where and how he is hitting the ball, you would see him as a very good hitter who is making his own luck.

And when a batter has a low exit velocity and a high number of home runs, you can reasonably assume that he is getting fortunate, and that the home runs are unsustainable over a long period of time. Yuli Gurriel is an example of this from last year. His 31 home runs in 2019 were the same amount he hit in 2017 and 2018 combined, in only 40-50 more plate appearances than either of those seasons. You would think that his exit velocity and hard contact must be elite, but he was 106th in qualified hitters in average exit velocity. What that tells me going into this year is that I can’t trust his home run count from last year, and that it may have more to do with park factors, launch angle, luck, or you know, being an Astros hitter.

Where Do I Find This Stat?

Surprisingly, you can’t find statcast data on Fangraphs. Instead you’ll want to go to baseballsavant.mlb.com and check the leaderboard for hitters. You’ll find a ton of great data that we’ll get into in future episodes.

So, just to review really quickly – exit velocity is how hard a ball is hit off the bat. You can use it to qualify a batter’s BABIP or the legitimacy of their home runs.

So on baseball savant, you can sort in a lot of different ways. When researching exit velocity, I’m sorting that column from highest to lowest. And so what we can do is take a look at a couple players here who show up at the top of the list to evaluate and decide how we should approach them going into this season.

First, let’s look at Miguel Sano. You’re not going to be able to find the player’s other stats on baseball savant, only their Statcast data, so I’d recommend having their Fangraphs page open as well so you can correlate the data. Sano had the second highest average exit velocity last season, so should we value him as an elite hitter going into this season? To me the answer is no, but he’s close. Last season, he struck out nearly 40% of the time he was up to bat. Pardon the pun, but that is strike one. That immediately will limit his batting average. Strike two against valuing him as an elite hitter this year is his 36% HR/FB rate from last year. It was 10% higher than his career average, meaning he got a little more lucky in his home run count, and should probably have been closer to 28 or 29 home runs than the 34 he ended up with. That’s also strike 3 because you would think that an elite exit velocity would translate into elite home runs, but it doesn’t because he doesn’t play in the majors enough to get the at bats and when he does, he strikes out 4 out of every 10 times at the plate.

What about Kyle Schwarber? He had the 9th highest average exit velocity last season. How should we value him? He was very productive last season – 38 home runs, 82 runs, and 92 RBI. He struck out 25% of the time, which isn’t great, but isn’t terrible either. He had a solid hard contact rate, though not elite, and his HR/FB ratio was in line with his career mark, which is a good sign that he could repeat last year’s power totals in a full season. One interesting thing is that for a player with a high exit velocity, his .276 BABIP from last year is noticeably low. He pulls the ball 40% of the time, and as a lefty, that is a killer because he is hitting into the shift. So it doesn’t matter how hard you hit the ball. If you have a great exit velocity, but you’re hitting directly at defensive players 40% of the time, a lot of those balls put into play are going to result into outs. What I want to see before adding him on my team is that he can distribute the ball to all fields better, and I’d like to see him lower his K%. I would definitely consider drafting him, but probably not as early as he is going to go in most drafts. I think you can find better value.

So who do I target?

This might sound crazy, but hear me out. Draft Howie Kendrick. He is number 21 on last season’s leaderboard for exit velocity, and the rest of his numbers line up. He had a career year last year as a 36 year old, cranking 17 home runs in 370 plate appearances. And he did so striking out only 13% of the time. His elite exit velocity backs up his .359 BABIP last year, as does a nearly even spray chart – meaning he hits the ball to all fields. He did have a high 48% ground ball rate last season, but if he is spreading the ball across the field while also hitting the ball very hard, he is likely hitting gaps where there are no defenders, or hitting balls that are too hard for defenders to handle. He had an excellent hard contact rate last season as well, corroborating his elite exit velocity. This is a valuable player who will have multiple position eligibility going into this season, and with Ryan Zimmerman out, Howie will see time at first base, second base, and probably DH too, so he will absolutely get at bats. And all signs point to Howie having productive at bats. If you haven’t drafted yet, snag him, and if you have drafted, trade for him.

That’s all for today, folks. Next time, we’ll look into another piece of statcast data – launch angle.

We last covered the pitching version of BABIP to explore how you can quantify the pitcher’s luck as a way to evaluate his talent and the legitimacy of his production. Today, we’ll spend some time breaking down a sabermetric stat called SIERA, one of my favorite go-to pitching sabermetrics to look into, so let’s get into it.

The Stat

SIERA stands for Skill Interactive ERA, and is in the ERA, FIP, and xFIP family. When you see the stat, it’s in the same format as ERA, so it should be easy to look at and understand. Similar to FIP and xFIP, the goal of SIERA is to figure out what the true skill of the pitcher is, and how they should be doing based on what they are doing.

Involved in the SIERA calculations are strikeout rate, walk rate, and ground ball rate – all relatively within a pitcher’s control, and usually a point of consistency for pitchers from year to year. These are things you want in an ERA predictor because you want to measure what a pitcher has control of in order to measure his skill. What SIERA does that stats like FIP and xFIP don’t do is that it factors in the type of ball that gets put into play and adjusts for that result. To help make sense of that, if a pitcher has a high xFIP, but has also induced a high proportion of grounders and pop-ups instead of line drives, his SIERA will be lower than his xFIP because he is being rewarded by SIERA for the grounders and fly balls he is inducing.

All that being said, if SIERA still seems confusing, here’s an easier way to think about it… High strikeouts, low walks, and ground balls all result in a low SIERA, and point to a pitcher who is doing what is in his power to reveal his true talent.

Why Is This Stat Important?

SIERA reveals just how important it is for pitchers to get strikeouts and gives value to pitchers who may not get a ton of strikeouts but can still induce ground balls, which can result in outs and double plays. SIERA factors in the ability of pitchers to induce ground balls and fly balls to get outs more than FIP or xFIP.

The top 12 in SIERA from 2019 all had 10.5 K/9 or more, and not a name should be surprising – Cole, Scherzer, Verlander, deGrom, Bieber, Strasburg, Buehler, Morton, Giolito, Darvish, Boyd, and Flaherty all appear.

The top 9 players aren’t that surprising to see, nor is Flaherty – high strikeout pitchers that you likely drafted pretty early. The names I like that offer some value are Yu Darvish and Matthew Boyd. Boyd’s ERA was nearly a point higher than his SIERA, suggesting that his true talent is that of a high strikeout, mid-3 ERA pitcher like Giolito, Lance Lynn, or even Aaron Nola. Understanding SIERA can help you find value in players that you may think are much worse than they actually are, and that is only because ERA is not good enough.

Something that I’ve established in the last few podcast episodes, and something that is understood now by many baseball analysts is that ERA doesn’t consider luck, park factors, and fielding and isn’t the best barometer at how a pitcher is truly doing because there are so many things out of a pitcher’s control that don’t get factored in to ERA. With SIERA, all of those things are considered, and so it’s a much better measurement of how the pitcher himself is doing.

FIP does not take balls in play into account, therefore it is limited as an evaluation tool. It can paint you some of the picture, but not the whole thing.

According to the website Fansided, “SIERA is the most in-depth ERA-estimator you are going to find, and it is also the most accurate. It performs better than FIP and xFIP in predicting next year’s ERA and in year-to-year consistency.”

My final recommendation with SIERA is to continue monitoring pitchers’ SIERAs throughout the season, and like always, to look at it in conjunction with other stats. But if you are seeing discrepancies between FIP and SIERA, I’d lean towards the latter in my analysis.

How Do I Find This Stat?

This stat is on Fangraphs, so first, go there and up at the top right, you’ll select leaders and from the drop-down choose 2019. Once this season gets started, you’ll be looking at 2020 data.

Then, you’ll want to select Advanced from the toolbar you find when you scroll down a little bit. And once you’ve found that and selected advanced, you’ll want to click on SIERA, which is in the column all the way to the right and sort from low to high SIERA.

As I said before, a lot of those top guys are not surprising. So let’s look at a few players that have a discrepancy between their ERA and SIERA in either direction. One very large gap is in Hyun-Jin Ryu’s ERA to SIERA. He had an outstanding year last year, with an incredible 2.32 ERA. SIERA suggested he should have been closer to 3.77. This is why I never wanted Ryu last year and why I refused to draft him this year – I didn’t trust what his ERA was telling me. On the other hand, German Marquez had a bloated 4.76 ERA, but his SIERA (which takes into account the park factor of pitching in Colorado) suggests he should have pitched nearly a run better at 3.85.

Who Do I Target?

Finally – who should you target this year, based on SIERA? My suggestion is Robbie Ray, although with a caveat. Remember, SIERA rewards a low walk rate, a high strikeout rate, and a good ground ball rate. Robbie Ray had a horrible walk rate last year, averaging nearly 4.5 walks per 9 innings, and still managed a 4.02 SIERA vs a 4.34 ERA. This means he’s a better pitcher than his 4.34 ERA suggests. In the 3 years previous, he has had a sub 4 SIERA at 3.89, 3.53, and 3.59, with only the 3.53 SIERA being higher than his ERA that year. So the caveat with suggesting Robbie Ray, is watching the walk rate. If you see his walk rate spiking and his SIERA starts to spike, I would cut ties, even if his ERA looks good. In fact, that’s more reason to try trading him.

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I also wanted to spend some time today going over the impact of players opting out of the season, but rather than focusing on them, I want to focus on who will be impacted most by those decisions. So first, here are the major players that have opted out in 2020, and although the names might not be huge, the impact will be felt in fantasy baseball.

Ryan Zimmerman and Joe Ross from the Nationals, Ian Desmond from the Rockies, David Price from the Dodgers, and Nick Markakis from the Braves.

First, with Ryan Zimmerman opting out, you have a platoon spot opening up at first base. Zim would also have been a prime DH candidate. So who is going to benefit most from his absence? I think you’re likely to see more at-bats from Howie Kendrick at first and at DH, as well as Eric Thames. I also think there’s a chance for more at-bats for Asdrubal Cabrera as well, who had a decent year last year with the Nats in the second half.

Another National that opted out was Joe Ross, who likely would have been the fifth starter. Instead, you’re likely to see Eric Fedde and Austin Voth compete for time there. Watch them because one of them is going to emerge and be pretty good, the other will probably become a stretch arm in the bullpen, which could also turn out to be very valuable this year especially early on if pitchers aren’t able to go deep into games.

Former National and current Rockies outfielder Ian Desmond also will not be playing this year, and a crowded outfield just became a little bit clearer. Charlie Blackmon’s time in the outfield was never really in question, though they should DH him, since his defense is in decline. And if they do that, it would open the outfield even more. But, with just Desi sitting out, that means more time for David Dahl, Garrett Hampson, Sam Hilliard, and Raimel Tapia to see at bats.

David Price sitting out is going to create a domino effect in the Dodgers’ clubhouse. Buehler and Kershaw are definitely numbers 1 and 2 in the rotation. I think we are likely to see Alex Wood in the rotation. But the rest is very unclear. The Dodgers have Julio Urias, Dustin May, Ross Stripling, Brusdar Graterol, and Tony Gonsolin all who could see time starting. You could see a six man rotation in LA. You could see a rotation where some pitchers start for 3 innings, and another arm comes in for another 3 innings, before going to a set up man or a closer. My suggestion is to keep an eye on Urias, May, Stripling, Graterol, and Gonsolin in LA because you may find value with all of them.

Nick Markakis is also sitting out of this year. Likely the starting right fielder, Markakis’ absence opens a hole in the outfield and in the lineup. Acuna may end up moving to right for Ender Inciarte to play center full time, so that may give Inciarte some more at bats. What may end up happening is that there’s more playing time in left field for Marcell Ozuna and Austin Riley. You could see a platoon there or in center in some combination with Inciarte. I’d also keep an eye on prospect Christian Pache.

Well, that’s all for today. Next time, we’ll do an intro into statcast data and what that entails. Thanks for reading.

Just like HR/FB Rate, BABIP works for both hitters and pitchers and in this case, it is very similarly used. Remember BABIP from when we covered hitting stats stands for Batting Average on Balls In Play. Basically what it measures, for both hitters and pitchers, is the result of non home run balls put in play. But it goes deeper than that, so lets dig into it.

The Stat

When you see the BABIP number, it’s going to look similar to how batting average looks, no matter if you are looking at the BABIP for a hitter or a pitcher. And it looks that way because it measures something similar to batting average. Batting average is calculated by measuring how much a batter hits the ball – counting every thing from singles to home runs – divided by their at bats. And generally, you can say a .300 batting average is very good, and go from there. However, it doesn’t work that way for BABIP. BABIP instead, takes home runs out of the equation. BABIP is also reflective of the defense behind the pitcher. For example, a batter who hits the ball into an outfield gap against a poor defensive outfield may record a hit would improve his BABIP, but a better outfielder may reach the ball which would reduce his BABIP. So as we explored in one of the earlier posts, BABIP is often called the “luck” statistic because there are some elements of luck involved. And generally for a hitter, an extremely high BABIP means a batter is getting lucky and an extremely low BABIP means a batter is getting unlucky. But, this is the OPPOSITE for pitchers. So we have to look at how this works for pitchers, so I’ll use an example. If you missed the FIP or xFIP posts, you’ll want to go back and read them, because I’m going to be referencing those stats here.

Pitching BABIP is important to understand because pitchers have less control of their BABIPs. Once the bat hits the ball it is out of the pitcher’s control, so it is important to consider the defense behind the pitcher when evaluating pitchers based on BABIP. So if a pitcher has an astronomically high BABIP, there is a good chance it means he’s been getting unlucky – either because of bloop hits falling or because his defense has made some mistakes – and an astronomically low BABIP could be influenced by a defense doing extremely well or because the pitcher is inducing ground balls and double plays. Some quick examples are Justin Verlander, whose BABIP was .218 last year. Just like with hitting, we want to measure BABIP against his career BABIP, and not look at it in isolation. His career BABIP is .281, so we have to wonder if this was something that Verlander was actively doing to lower his BABIP, or was it more the result of luck or defense. His FIP and xFIP were higher than his ERA, meaning that his defense is better than the league average because it kept his ERA lower than it should have been with league average defense behind him. That’s my first hint that his low BABIP was because of defense. Verlander did induce a 35% ground ball rate (which is not high in relation to some other pitchers), but again, once the ball is in play, pitchers are reliant on their defenses to complete the play, which the Astros defense did do. There isn’t really anything in his peripheral stats to suggest he did anything special to limit his BABIP aside from limiting hits altogether, and so that tells me he was getting pretty lucky last year, and as we see some regression to the mean, I wouldn’t expect his ERA to stay as low as it was in 2019.

Why Is This Stat Important

This stat is important because you can use it to measure a pitcher’s talent in season and use it to make decisions on drop/adds, start/sits, and trades. As a reminder, do not just look at a pitcher’s BABIP in isolation. You should be measuring it against their career BABIP and look for anomalies there. If you see a BABIP that is much higher or lower than their career number, you have to assume that there is something affecting the stat, and then you have to figure out if it is something the pitcher is doing or if it is defense or luck related. Normally it takes probably half of the season to establish a solid reading on the BABIP for pitchers because you need a decent sample size to measure it and that creates a problem for this season. I still recommend using BABIP as a point of analysis for this season, though don’t make it your only one. And look at BABIP in conjunction with other stats too. Someone with a really high BABIP last season was Jon Lester. Last year his BABIP was .347 compared to a career .301 number. Remember, the higher the BABIP for a pitcher, generally the unluckier he’s getting. So lets see if we can figure out if he really was getting unlucky or if this was something he was doing to spike the number. He actually improved his strikeout, walk, and ground ball rate from the previous year, all of which should be good signs. One issue is that his hits, runs allowed, and earned run numbers all went up from the year before. So if you present me with those two data sets, what I see is a pitcher who is improving on things in his control, but not getting the results that should go along with it, and that tells me maybe the defense or some bad luck could be to blame. To go a step further, if you look at his FIP and xFIP, you’ll see that they are lower than his ERA. That’s a sign that if he had league average defense, his ERA could have been BETTER! That means his defense last year was below league average and likely turned potential good outcomes into bad results. He’s a guy who you can reasonably predict in a normal seasons to do better than he did in 2019, although I wouldn’t bet on him to have a top season this year.

How Do I Find This Stat

Go to Fangraphs of course! Once you’re there, go to leaders at the top right and select pitching leaders from last year.

Once you’re at the leaderboard, sort by ERA this time – and sort it so that it starts at the lower ERA. What I want to do with you is to find a pitcher with a low BABIP shows how good luck and defense influenced his season.

First, we’ll look at Jack Flaherty. His BABIP last season was .247, which was low even for pitchers with similarly low ERAs. Was this due to good luck, defense, or good pitching? My first inkling that his defense helped him a lot is that his FIP and xFIP are nearly a run higher than his ERA. That means a good defense behind him saved him nearly a run on average. If he had a league average defense behind him, he would have done worse, meaning his defense was above league average. He also had similar peripheral numbers across the board to the previous season where he had a 3.34 ERA, so that also makes me thing his defense helped him out.

To try to put a bow on pitching BABIP, here’s a way to think about it in easier terms with a couple of possible ERA/BABIP combos.

High ERA + High BABIP usually = a pitcher getting unlucky or bad defense.

High ERA + Low BABIP usually = the defense and luck are in the pitcher’s favor, and he is doing something poorly that is spiking his ERA.

Low ERA + Low BABIP usually = a pitcher may be getting lucky, but there is a good chance he is doing something well to limit hits and runs

Low ERA + High BABIP usually = a pitcher has a poor defense behind him and may be getting unlucky, but he is doing well at limiting runs and hits

Who Do I Target

What we want is a pitcher whose 2019 BABIP is in line with their career number, and whose BABIP reflects that they had control of their outcome. And hopefully we can use that to reasonably predict good things for this season.

One player that I like going into this season is Matthew Boyd from Detroit. He had a .307 BABIP last year, up from his .297 career number. But like Jon Lester, he improved in a lot of peripheral statistics from the previous year, some were significant improvements, all in only 15 more innings. So it’s not hard to figure out that a big uptick in hits, runs, and earned runs are not necessarily a result of the pitcher, but are more so likely a sign that his defense did not help him out very much, and that he got pretty unlucky. His FIP and xFIP being lower than his ERA also suggest he would have done better had his defense improved. That being said, Detroit did make some improvements this year adding CJ Cron, a deceptively good defensive first baseman, and Cameron Maybin , a defensive outfield upgrade. I don’t think Boyd will ever have the best ERA in baseball, but he is going to strike out a lot of batters, which is valuable, and some improvements to the defense could help his ERA dramatically. We already know with his BABIP that he is doing well with things that are in his control.

Well, that’s all for today, next week, we’ll go way off the beaten path and look at a sabermetric stat called SIERA. Thanks for reading.

In my last post, I took a look at HR/FB rate, and how although it is similar to the hitting version of the stat, when you analyze it, you need to take a different strategy and use it for a different purpose. One important stat that helps understand HR/FB rate and vice versa is a sabermetric called xFIP, so that’s what we’ll go over today. Let’s get into it.

The Stat

A few weeks ago, I went over a statistic called FIP, or Fielding Independent Pitching. Essentially what that stat factors in is only balls put in play by the pitcher, and what the results of those plays are with league average defense. FIP is one way to measure what a pitcher should be doing by only considering what is in their control with balls put in play. The stat xFIP takes it one step further.

In the equation for xFIP (which I won’t get into because I can barely understand the math), the pitchers’ home runs that would be considered in the FIP equation, are replaced by the pitchers’ expected home runs by plugging the league average HR/FB percent into the equation instead. Basically, the league average HR/FB percent in the equation narrows the projection a bit more than the FIP statistic for what a pitcher should or could be doing.

Why Is This Stat Important

The Fangraphs glossary puts it simply by saying that xFIP “tries to remove some of the randomness in the pitcher’s actual performance.” FIP is important because it’s a better performance indicator than ERA because it limits the math to include only what the pitcher is responsible for. xFIP is even more important to understand and use because it strips down the factors even more by taking out the randomness of home runs and fly balls, something that is relatively out of most pitchers’ control.

Like FIP, you have to compare xFIP to a pitcher’s ERA. It’s not enough to just look at xFIP and say, “Oh, he has a 3.20 xFIP, he’s a good pitcher.” It doesn’t quite work that way. Although the two stats are related, you should be looking at ERA, FIP, and xFIP in combination with each other in order to compare them and assess the pitcher. xFIP is predictive in a way, because it shows either the positive or negative regression that a pitcher may experience, but that regression either way is not guaranteed. So the real way you should be using xFIP is to understand the role HR/FB plays in it and rather than look at xFIP as a be-all-end-all, look at it as a way to see how the pitcher is pitching completely independent of their defense.

As you look at xFIP throughout a season for various pitchers, you’ll be able to use it in a similar way to FIP. Use it to decide on a pitcher’s future value and whether you should buy, sell, drop, add, or just sit tight because you found a good pitcher. Someone I had last year that I liked a lot was Charlie Morton. His 2019 ERA, FIP, and xFIP all line up pretty well (3.05, 2.81, and 3.28), and all reflect that his talent was exceptional, and he did basically exactly what he should have last year. If you had a pitcher like Morton last year, you can see from his xFIP that his talent and projection was right around what his ERA was, and likely what you did if you had him was you hung onto him for the year. On the flip-side, Zack Greinke‘s xFIP was nearly a full run worse than his ERA, making him a prime candidate to be traded away if you had him. His 2.93 ERA looked good on the surface, but his 3.74 xFIP reflected a less talented pitcher – one who was getting lucky, not striking many batters out, and not leaving many runners on base.

How Do I Find This Stat

Go to Fangraphs of course! Once you’re there, go to leaders at the top right and select pitching leaders from last year.

Once you’re at the leaderboard, sort by K/9, even though that’s not what we’re looking at. Generally with pitching, that’s what I do to order the top pitchers. I want high strikeout pitchers, so I begin there and look for whatever else I need.

We’ve talked about a few pitchers already, along with how to use xFIP to evaluate pitchers. So I’m not going to go into great detail here with analysis. But once you get to the leaderboard, and get it sorted by K/9, you’ll have a pretty easy visual to assess pitchers on xFIP.

Who Do I Target

Looking at that leaderboard, my eye goes to Luis Castillo from the Reds. He’s a high strikeout pitcher, which I like a lot. He gets a lot of ground balls, which is good. And he strands a decent amount of base-runners. But what we want to look at is the ERA and the xFIP, which are nearly identical. Castillo had a 3.40 ERA last year, and a 3.48 xFIP, which shows that he pitched close to his true talent. He was an excellent pitcher last year and I expect him to be just as good, if not better this year (if there is a season…).

Anyways, that’s all for today! Next week we’ll take a look at pitching BABIP. Thanks for reading.

So far, I’ve explored 2 of the pitching stats that I use to evaluate pitchers in fantasy baseball. The first was K/9 or strikeouts per 9 innings, a simple but useful stat to begin your analysis. Next, was FIP, a stat to help evaluate a pitcher’s true performance strictly on things that are in his control. This week, we explore the pitcher version of HR/FB (Home Run to Fly Ball) rate, which actually impacts FIP and a new stat that we’ll go over next week called xFIP. So let’s break down HR/FB rate for pitchers.

The Stat

HR/FB rate is both a stat for hitters and pitchers. Weeks ago, I went over the hitting version, which is a percentage of how many home runs a batter hits out of all their fly balls. Using it, you can measure if a hitter is outperforming what he should be doing – if he’s hitting a crazy amount of home runs, you can generally tell because his HR/FB rate will be astronomically high, and you can expect it to come back to earth, along with that player’s output. The pitching version is similar, but the analysis of it is different. For pitching, HR/FB rate is the percentage of how many home runs the pitcher allows out of all the fly balls they allow. The thing about HR/FB rate for a pitcher, is that there is a league average, usually around 11-12%, which means that if a pitcher’s HR/FB rate is super high, like 20%, it will spike his ERA. Interestingly, if that happens, either the FIP or xFIP will usually be lower than the ERA, because those sabermetrics factor in that the pitcher is getting unlucky because a higher amount of home runs are being produced than average.

That may sound complicated so I’ll give you an example. Let’s take Robbie Ray from last year. His ERA was 4.34, which is not great. However his HR/FB rate was 20%, which is 8-9% above the league average. That means he had an above average number of the fly balls he allowed turn into home runs. His ERA spiked last season because of this. Instead, if you look at his FIP, you’ll see it’s lower at 4.29, and his xFIP even lower at 3.76. So while on the surface he looked like a very average pitcher, his other stats reflect that he was unlucky last season and should have done much better. That’s important for those of you that haven’t drafted.

On the flipside, Marco Gonzalez from the Mariners seemed to have a decent 2019, with a 3.99 ERA and 16 Wins. The issue is his below league average HR/FB rate of 9.3%, which means he got lucky a lot, and his ERA should have been a bit higher. His FIP was 4.15 and his xFIP was 5.11, because those reflect league average defenses and league average HR/FB rates. If you want a true measure of a pitcher’s output, a good place to look is at their HR/FB rate.

Why Is This Stat Important

This stat is important mainly because it allows you to better understand other metrics like FIP, and xFIP, and how they are affected. The way you should look at HR/FB rate is to use it as proof of how a pitcher is really doing. And you can use it as a guide to help you make decisions.

With the hitting version of HR/FB rate, you can tell if a batter is producing more or fewer home runs than he usually does, and in doing so, determine if that player is worth keeping on your roster or not. The pitching version of the stat is similarly useful. Gerrit Cole for example had a 2.50 ERA last year, and a 16.9% HR/FB rate. He was actually unlucky in terms of allowing more home runs than the league average, but because his other metrics were so good, he was able to still be an excellent pitcher. He struck out more than 13 batters per 9 innings, he didn’t walk many batters, he left a lot of people on base, and he got 40% of batters to ground out. But imagine how much better his ERA would be if he had even 5% fewer of those fly balls stay in the park. You can use the HR/FB metric to verify a pitcher’s stat line.

Another example is how a lot of people loved Lance Lynn from last year. However, if I’d had him, I would have likely sold high – meaning I’d have traded him away to try to get higher value. The reason I would have done that is because his 9.9% HR/FB rate from last season showed that he was getting lucky. His 3.85 xFIP shows a truer version of what his production could have and should have been last year, and because of that, I would have traded him away.

You should be monitoring this stat often during the season, both for pitchers on your team and pitchers that you’re targeting.

How Do I Find This Stat

Go to Fangraphs of course! Once you’re there, go to leaders at the top right and select pitching leaders from last year.

Once you’re at the leaderboard, sort by K/9, even though that’s not what we’re looking at. Generally with pitching, that’s what I do to order the top pitchers. I want high strikeout pitchers, so I begin there and look for whatever else I need.

We’ve talked about a few pitchers already, along with how to use HR/FB rate to evaluate pitchers. So I’m not going to go into great detail here. What you’ll notice is that those pitchers who have around 11-12% HR/FB rates usually have other metrics that match their ERA, meaning they’re doing exactly what they should be doing, and producing exactly how they should be producing.

Walker Buehler is an example of that.

The thing with HR/FB rate is that it doesn’t paint 100% of the picture. So there may be inconsistencies with projecting FIP and xFIP from it. You also have to consider a pitcher’s walk rate, left on base rate, and BABIP. All of which we’ll look into in the future.

Who Do I Target

Of course, you want to know who to target. Again, I’ll stay away from the obvious answers like Max Scherzer and Jacob deGrom. And we’re going to base the decision on mainly on HR/FB rate. That being so, I would recommend drafting, picking up, or trading for German Marquez from the Rockies. His HR/FB rate was astronomical last year – likely from playing in Colorado. So what that tells me is that his ERA spiked to 4.76 because nearly twice as many fly balls turned into home runs than the league average. He doesn’t walk a ton of people, and his ground ball rate is great too, nearly 50%. His 4.06 FIP emphasizes that he should have been a better pitcher last year. I’d expect his HR/FB rate to do a little positive regression in 2020 and get more back to normal, and I’d expect his ERA to reflect that.

That’s all for this week. Next week, we’re going to get into another pitching sabermetric called xFIP, to add another piece to the puzzle. Thanks for reading!

Last week, I began my series on sabermetric and traditional pitching statistics and how fantasy baseball managers can use those statistics to influence their decision making in various aspects of the game. The first stat, introduced last week, was K/9, which is the first thing I look for when I begin my analysis of pitchers. Remember, K/9 is the amount of strikeouts a pitcher averages over 9 innings.

Today we talk about FIP.

The Stat

FIP stands for Fielding Independent Pitching, and in the most basic terms, what it means is that it’s what the pitcher’s ERA would be if you don’t factor in the defense. In a more detailed sense, it takes defense, strikeouts, walks, hit by pitches, and home runs allowed out of the equation. FIP only factors in the outcomes of balls that get put in play while including league average defense in the equation. There is a fancy mathematical equation involved, but I’m not going to get into that because it’s way over my head.

To help explain it a little more, think about someone like Patrick Corbin pitching to Bryce Harper with a runner on second base. Let’s say Harper hits it to center field, and on that day, Michael A. Taylor is in center. It’s a deep hit, tough for most defenders to get to, but Michael A. Taylor is a great defender and gets to it, saving a potential run from being scored, and keeping Corbin’s ERA low. Because Corbin had a great defender behind him, his ERA is made better. If the same thing happened with Albert Almora, ranked as the second worst defensive center fielder last year, playing there instead of Michael A., that ball might get misplayed, then the run scores from second, and the pitcher’s ERA will rise because of below average defense behind him. FIP takes that out of the equation and only considers what would happen with a league average defender there.

A lot of people, especially those in the fantasy baseball community, will say that FIP is a lot more revealing to a pitcher’s true value than ERA. And you can see why.

Why Is This Stat Important?

This might be the second stat that I look at when I’m doing my pitcher analysis, but it’s potentially one of the most important, if not the most important stat in helping me make decisions. After I’ve looked at K/9 rates and sorted them to find pitchers with K/9’s above 9, I’ll look at their FIP compared to their ERA. It’s really important that you’re not just looking at the FIP, but that you’re comparing to ERA. The reason for that is simple – doing so tells you if they should be doing better or worse than they are. Remember, ERA depends on a LOT of different outcomes. FIP limits the outcomes to what happens when the pitcher has the ball put in play against him.

You might be wondering what this analysis looks like, so I’ll give you an example. A player I was targeting last year was Noah Syndergaard. He’s out this year because of Tommy John surgery, so don’t go after him this year. But, the reason I was targeting him was because he was a high strikeout pitcher with a 9+ K/9 last year. His ERA was 4.28, which isn’t exciting, but his FIP was 3.60, meaning that the defense behind him, along with a few other factors like more than average fly balls turning into home runs, was costing him more than half a run. From this data, you could reasonably assume that he should and could improve to get closer to that 3.60 if he had a couple good defensive games. He actually improved greatly throughout the year – his April ERA was 6.35, but he improved each month after that through July where he had a 2.70 ERA. And if you paid attention to his FIP, you could potentially have foreseen that and tried to buy low.

Similarly, you can compare a pitcher’s ERA and FIP to find sell high candidates on your own team. These are pitchers who have low ERAs, but high FIPs, meaning that they likely should be doing worse than they are because they are benefiting from above average defense, fewer fly balls turning into home runs, etc. One of the most stark examples of this I can find from last year is Dakota Hudson from St. Louis. His 2019 ERA was 3.35, but his FIP was 4.93. So on the surface he looks like a good pitcher, but it seems he just got kind of lucky last year. He had a bunch of batters ground into double plays, and in his 3 best months from last year, his pitching BABIP (which we’ll get into in the future) was extraordinarily low, meaning the batters were getting super unlucky. He would have been a prime trade high candidate, even with the low strikeouts, especially for a team that needed an ERA boost. However, the risk with him is that he could get unlucky and a string of bad starts could balloon his ERA. Then he completely loses value. But if you used FIP to see the downward spiral coming, you could sell high on a player like him.

How Do I Find This Stat?

Let’s get into looking for this stat. And like usual, we’ll go to my favorite website Fangraphs. We are going to do this two ways, so first I want you to go to the top right of the page and select Leaders. When it drops down, choose pitching leaders from 2019. And for our purposes, even though we are looking at FIP, I still want you to sort by K/9, so click on that column.

What I want you to do for a little practice, is find a few pitchers who you would buy low on – pitchers whose FIP suggests they should have done better, and who could have some sneaky value going into this year. So press pause, take a look, and when you’re ready, come back and you can compare with my list. My buy low pitchers from last year, who I think have an opportunity to be really good this year are Robbie Ray, Matthew Boyd, Lance Lynn, Max Fried, and German Marquez.

Let’s do the same thing, but with pitchers you might sell high on, if they were on your roster, or pitchers who maybe are being valued too high going into this season. Who is on your list? I am staying away from Aaron Nola and Clayton Kershaw. I think they are being valued too high and may have a little regression coming this year.

Now, the other way to do this sort of analysis is if you’ve drafted already and the season has begun and you can see the stats in real time. Once the seasons starts, you’ll want to keep track of pitchers’ FIPs on a weekly basis at least. If you’re crazy like me, you’ll check every day. Using FIP as a guiding statistic is a great way to gauge the true value of a pitcher in your league.

Who Do I Target?

So finally, who do you go after this season, whether you’ve drafted or not, based on their FIP from last year? I could give you some obvious names like Max Scherzer, Charlie Morton, or Shane Bieber. But that’s not what you’re here for. So let’s find some value later in a draft, or someone that your fellow league managers are undervaluing. I suggest targeting Max Fried from the braves. First, he is a high strikeout pitcher – in 165 innings last season, he struck out 173 batters, more than a batter per inning. He also leaves about 75% of runners on base, meaning that he is limiting scoring opportunities against him (it was 82% in 2018). He’s been getting better at limiting walks and home runs, but he is still plagued by a well-above average HR/FB ratio against him, which probably has something to do from playing in a division where 4 of the 5 teams play in the top 15 of hitter friendly parks according to 2019 Park Factors data. I’ll get into the pitching end of the HR/FB rate in a future podcast. But, generally, it can spike a pitcher’s ERA. And since ERA factors in home runs, and FIP doesn’t – and his FIP from last year was 3.72, compared to a 4.02 ERA – Max Fried is a player I recommend looking for in your leagues this year.

That’s all for today. Next week, I’m going to have a special guest on my podcast, and I’ll end up posting the transcript of the interview here. In the 2 weeks, I’ll be going over HR/FB rate for pitchers and how that can impact a pitcher’s value in fantasy baseball. Thanks for reading.

I recently wrapped up my series of batting sabermetric and traditional statistics that I use to evaluate hitters in fantasy baseball. Those included Hard Contact Rate, BABIP, LD/GB/FB%, GB/FB rate, HR/FB rate, plate discipline (O-Swing, O-Contact, Z-Swing, Z-Contact, Contact%, SwStr%), and Walk and Strikeout rates. All of those statistics should be looked at in combination with each other in order to paint a clear picture on the hitter you are researching. By looking at those stats, what you’re doing is giving yourself a leg up on your fellow fantasy baseball league managers. If you take the time and do the research, you’re able to make data driven decisions for drafting players, starting and sitting players, dropping and adding players, and trading players. You’ll be able to reasonably predict peaks and valleys, and you’ll have a good idea of when to buy low and sell high.

This week, the idea is to do the same thing, but begin focusing on sabermetrics that can be used to help you analyze pitchers and their performance for the same purposes – drafting, dropping and adding, starting and sitting, and trading them. Today, we’ll spend time on one of the simpler stats to understand – K/9 or Strikeouts per 9 innings.

The Stat

K/9, or Strikeouts per 9 innings, is a simple stat to understand. It’s how many strikeouts a pitcher averages over 9 innings. Rarely nowadays do pitchers go a full 9 innings. This stat is not that. The way it is calculated is that the number of strikeouts are divided by the number of innings, and then that number is multiplied by 9. So a pitcher who pitches 150 innings and has 140 strikeouts has a K/9 of 8.4. Conversely, a pitcher who pitches 140 innings and has 150 strikeouts would have a much better K/9 rate of 9.6.

Why Is This Stat Important?

The Stikeouts per 9 innings stat is important when evaluating pitchers because it helps you gauge how proficient a pitcher is at getting outs, and especially since most, if not every type of fantasy baseball league, values strikeouts, this stat becomes super important to focus on and understand. Fangraphs offers an evaluation tool to help you evaluate players on the K/9 stat. According to them, a 10+ K/9 is excellent, 9-9.9 K/9 is Great, 8.2-8.9 is Above Average, etc. For me, I simply don’t bother looking for pitchers with a K/9 under 9, with very few exceptions. Some of those exceptions deal with other pitching stats, which I’ll review in the future. The reason I target exclusively pitchers with over 9 Strikeouts per 9 innings is that I know from game to game, I’m going to have that category filled up, and I know I’m getting around a strikeout per inning from that pitcher.

I also target these high K/9 pitchers because these are typically pitchers who don’t pitch to contact. There are some pitchers, typically low K/9 pitchers, that when they pitch, they are hoping to get contact, albeit poor contact, off the bat in order to get outs. Pitchers like that who come to mind are Dakota Hudson, Marcus Stroman, and Mike Soroka. These are pitchers that induced a lot of ground balls last year, and relied less on strikeouts. Interestingly these 3 pitchers in particular had lower ERAs than you might expect, but other pitching metrics show that these pitchers actually overperformed last year.

How Do I Find This Stat

This stat is easy to find on Fangraphs. We are going to be looking at some of the top players here, so when you go to Fangraphs, click the leaders tab up at the top right, and when it drops down, click on pitching leaders from 2019.

Once you’ve done that, just scroll down a little bit and you’ll see a dashboard with a number of pitching stats. Simply click K/9, and you’re all set!

You’ll see that the top 5 players in K/9 last year were Gerrit Cole, Max Scherzer, Robbie Ray, Justin Verlander, and Lucas Giolito. And some of the players rounding out the top 10 are not surprises – Bieber, Morton, DeGrom are all there. One thing you’ll notice is that a lot of the leaders in K/9 are the top pitchers in baseball. This is why you want to target players with a high K/9. On that screen, you’ll also see other stats like ERA, FIP, and xFIP, all of which we’ll get into in the coming weeks. K/9 is a great place to start when looking for pitchers to draft, pick up, or trade for, but there are a lot of other factors that will play into helping you make those decisions.

In another couple of weeks, we’ll do some more intensive player analysis in regards to combining some of these stats to evaluate players. Right now, let’s just look at someone you can target that may be flying under the radar in your league.

Who Do I Target

Just based on K/9 data, I’ll recommend a few players for you to look into – Matthew Boyd, Yu Darvish, and Max Fried. Each of them average more than 9 strikeouts per 9 innings, meaning that their starts are productive and will fill up your stat sheet. They are middle-of-the-pack in walks, so there is little risk in that regard. Boyd and Darvish possess a little risk in giving up home runs, but that’s okay – Justin Verlander does too and look at his numbers from last year. Boyd, Darvish, and Fried all strand about 3/4 of base-runners. And they all were plagued last year by an inordinate amount of fly balls that turned into home runs, raising their ERA. This year, I wouldn’t be surprised if the number of home runs they give up is lower, leading to better ERAs for all three of them. Players in your league might be ignoring them because of lackluster numbers last year, but we need to project to this year, and the likeliest outcome is that we have some positive regression, and they do better this season. If you haven’t drafted yet, try to snag them. And if you have drafted already, put those three guys on your watch list, and if they start to look good, go after them.

Well, that’s all for this week. I’ll be back next week to continue my pitching statistic series with a stat called FIP. Thanks for reading.

I’m taking a break this week from statistical analysis to review my league’s draft which we did on Saturday, hoping for the MLB to resume at some point. In the past, I’ve written about draft strategy, so I stuck with my guns, used my customized Excel draft board, and got to work. I am mostly happy with my team. There were some mid-round picks I’m not happy with, but for the most part, I’m glad I was able to follow my strategy and field a (hopefully) winning team.

First, let me give you some details about my league. We are an 8 team league, which many people will scoff at and say things like, “Every team must be a super team!” or “Is it even competitive?” And I always say yes – in fact because every team is full of good players, it makes it more difficult because anyone can win any week. It’s also much harder to let players go for waiver adds because everyone on your team is solid. There are definitely bonuses to playing in smaller leagues. Each team also gets 4 keepers to hold over into the following year. There are no limits to how long you can own your players. And in the draft, the keepers are automatically populated on our rosters for the first 4 rounds. The draft order is the reverse order of the final standings from the year before, so the worst record from the previous year drafts first. I was the third pick in this year’s draft.

My keepers were Bellinger (who ESPN Fantasy let me keep at 1B surprisingly…they often slot the player into their first listed position if it’s a player eligible at multiple positions), Yelich, Trea Turner, and Rendon (who I traded for a week before the draft, giving up Springer, Bieber, and Bogaerts to acquire him). I was anticipating Bellinger being forced into an outfield slot by ESPN, so I had been going about my mock drafts under that assumption, selecting 1B with my first pick out of the keeper rounds. I had been targeting Anthony Rizzo, who ended up going in round 6 (2 rounds after the keeper rounds). My first pick was Jose Altuve, who I’m counting on to have a solid average and hit for some power. His counting stats have been declining, but he’s still VERY good. In order, the picks in the first non-keeper round went Jose Ramirez, Verlander, Altuve, Strasburg, Machado, Albies, Yordan Alvarez, Clevinger.

The next round followed with Goldschmidt, Meadows, Corbin, Rizzo, Luis Castillo, Morton (my pick), Judge, and Ketel Marte. My thought in selecting Morton was that I needed a stud pitcher, someone to lock down my rotation and give me good ERA and strikeouts, especially after locking down my infield. I love Luis Castillo, so I was disappointed to see him go there. I’d have picked him ahead of Morton. I also had Corbin high on my draft list, but I’m happy with Morton. In terms of my fellow managers, I had Rizzo higher ranked than Goldy, so that pick surprised me a little. And I think the team that selected Marte was going with outfield there no matter what, and was hoping Meadows fell to them, so Marte may have been a little bit of a reach. He did have the wrap-around picks (snake draft), and got Kershaw with the first pick in round 7, so that was a solid job.

Rounds 7-8 followed with Kershaw, Villar, Gallo (my pick), Vlad Jr., Snell, Josh Bell, Olson, Ozuna, Paddack, Eloy Jimenez, Stanton, Rosario, Abreu, Pham (my pick), Cruz, and Hiura. I have never drafted Joey Gallo before, and it could be risky if his batting average plummets again, but he looked like he changed something in his approach last year before getting hurt. He was having a ridiculously good season, and that’s the Gallo I’m hoping for in 2020. I also wanted to start filling in my outfield positions, and my strategy this year is to win the 3 power stats for batting each week, even if it means sacrificing some average. Gallo helps with that (although my team batting average could be buoyed by guys like Rendon, Altuve, and Yelich, among others). I considered going pitcher instead of outfield, but didn’t like anyone available at the time – Snell’s range of outcomes is too wide, and Paddack won’t get me the strikeouts I want. Then I went Pham with my pick in round 8. He has 20/20 potential, which I like. He’s in a good lineup in San Diego. I like him more than Castellanos, who was also available, and although Eugenio Suarez and Matt Chapman were available, I went for a player at a position I needed to fill, rather than adding another corner infielder.

Rounds 9-10: Castellanos, Nola, Greinke (my pick), Moncada, Matt Chapman, Giolito, Merrifield, Eugenio Suarez, Hader, Mondesi, LeMahieu, Kirby Yates, Tim Anderson, Darvish (my pick), Realmuto, Semien. I was going back to back pitching here no matter what. My plan this year was to draft a lot of stud relievers and attempt to win Saves, ERA, and WHIP each week, so I wanted to get to more solid starters here before drafting my relievers and the rest of my bats. My hope was that Giolito would last until round 10, where I’d been selecting him in mock drafts, but that wasn’t the case here. In hindsight, I wish I had drafted Giolito in round 9 instead of Greinke. I’m happy with Darvish in 10, though. He started to turn it on in the second half last year, and despite one extremely rocky start last season, he was pretty good. Between him and Greinke, my hope was to balance out strikeouts and ERA. In round 10, we also had our firsts – the first reliever and catcher were selected (Hader and Realmuto). There were a couple of picks here I think are risky. LeMahieu has been consistently one of the best hitters for batting average year after year, but last season his power numbers spiked. It will be interesting to see if it was a one year aberration like what has happened with Elvis Andrus, among others, or if it was the real deal. Still, I would haven’t have drafted him there. Muncy, Moustakas, and Lux were all available, and I think they could all end up being ranked higher than LeMahieu at the end of the season. I also thought Tim Anderson was a risky selection. He had a good statistical season last year, but I’ve written about how the sabermetric data doesn’t back it up at all. He could be absolutely terrible this season if the ball doesn’t bounce his way. Correa, Semien, Seager, and DeJong were all available, and I think all will be better than him this year.

11-14: Bauer, Soler, Aroldis Chapman (my pick), Gary Sanchez, McNeil, Muncy, Woodruff, Jansen, Moustakas, Bichette, Conforto, Glasnow, Ohtani, Osuna (my pick), Correa, Benintendi, Carrasco, Brantley, Liam Hendricks (my pick), Victor Robles, Schwarber, Hoskins, Luzardo, Taylor Rogers, Sano, Berrios, Kluber, Franmil Reyes, Lynn, Laureano (my pick), Eduardo Escobar, Carlos Santana. I think my strategy was obvious here. I wanted to snag some of the top relievers and I did so with Chapman, Osuna, and Hendricks. I got another potential 20/20 bat for my outfield in Ramon Laureano. And through 14 rounds, I feel really good about my team. I still needed to fill another outfield slot, my utility slot, catcher, middle infield, and corner infield, but I was in a good position. A lot of players I like were taken in these rounds. Loved the Soler pick…Muncy, Woodruff, Glasnow, Correa, Brantley, Robles, Hoskins, Luzardo, Reyes, Lynn, and Escobar I thought were all drafted appropriately. Luzardo was a great snag – he could be lights out for Oakland. Lynn has a lot of upside after what he did last season. Franmil Reyes was another great pick here in the mid-rounds. He has huge power potential. Glasnow and Woodruff have great pitching upside this year. Escobar I thought could have been drafted 3 or 4 rounds earlier and he would have been a great pick then. This was an excellent pick in round 14. Some picks I thought were not so great in these rounds were Bauer (who has really only had 1 good year), Bichette (who I think needs another year to grow before he is decent – he’s a batting average risk), Ohtani (I don’t understand the hype – gets injured a lot, and doesn’t do enough on either side of the ball to be good), and Schwarber (who I just have never gotten behind as a good fantasy player).

15-20: Edwin Encarnacion, Gray, Kepler (my pick), Lux, Hand, Montas, Nick Anderson, Grandal, Eaton, Yuli Gurriel, Luis Robert, Ray, Soroka, Corey Seager (my pick), Biggio, Giles, Raisel Iglesias, Cron, Edwin Diaz (my pick), Khris Davis, DeJong, Brian Anderson, Lourdes Gurriel, Gallegos, McCullers, Bryan Reynolds, Paxton, Pederson, Daniel Hudson, Justin Turner (my pick), Workman, Zack Wheeler, Will Smith (Catcher), Will Smith (reliever), Garver (my pick), Wong, Eduardo Rodriguez, Contreras, Neris, Hansel Robles, Civale, Dahl, Kingery, Salvy Perez, Yastrzemski, Lamet (my pick), JD Davis, Lorenzo Cain. I’m happy with Kepler completing my outfield (Yelich, Gallo, Pham, Laureano, Kepler – solid). I’m satisfied witih Seager filling my MI slot, and I got another high upside reliever in Edwin Diaz. Justin Turner was a panic pick for me. At the time, I still needed to fill my CI and UTIL slots, as well as catcher. Justin Turner has elite batted ball numbers, but can’t stay on the field, and is in a crowded and deep lineup in LA. Looking back, I’m wishing I had picked Garver instead of Turner, Eduardo Rodriguez in Garver’s slot, and then filled a bench role with someone else in round 20, saving the corner infield slot for Christian Walker, whom I took later. I’m okay with Lamet – I think he’s an ERA risk – but he has 200 strikeout potential in a normal season. Picks I love here: Lux, Hand, Gray (I’m going to attempt to trade for him – he’s one of my top sleeper candidates this year), Nick Anderson, Eaton, Robert (great timing for him – one of the best prospects in baseball taken in the last half of the draft = no risk), Robbie Ray (another player I’ll look at trading to get), DeJong, McCullers (I think he could be one of the best pitchers in baseball this year), Reynolds, Catcher Will Smith, Rodriguez, Civale, Cron, and JD Davis. I’m staying away from both Gurriels – Yuli because I believe he benefited greatly from the cheating ring in Houston, and Lourdes because I just haven’t seen enough from him in Toronto to trust him in a starting slot. Brian Anderson I think has potential, but the production just isn’t there and that Miami lineup isn’t going to help him much in that respect. Reliever Will Smith is also a risky pick because Melancon is also in Atlanta and who gets the saves there is a toss-up. That could turn out to basically be a wasted pick. I also was surprised to see Daniel Hudson get drafted – I think it’s more likely Doolittle gets significantly more saves in Washington. I also thought Yastrzemski was an odd pick there, but only because McCutcheon, Renfroe, and Mercado were all available too. I see them all finishing better than Yastrzemski.

21-25: Archie Bradley, Danny Santana, Leclerc (my pick), Britton, Kimbrel, Bumgarner, Julio Urias, Renfroe, Gregorius, Brandon Lowe, Fried, Matt Boyd, Doolittle, Christian Walker (my pick), Colome, McCutcheon, Ryu, Wilson Ramos, Renato Nunez (my pick), Howie Kendrick, Caleb Smith, Carlos Martinez, Maeda, Mallex Smith, Kela, McMahon, Mercado, Starlin Castro, Justin Upton, Hunter Dozier (my pick), Hosmer, David Price, McKay, Gallen, Odor (my pick), Givens, Daniel Murphy, Hampson, Christian Vazquez, Dickerson. A lot of the players selected in these rounds are dart throws, filling bench spots, that you hope pan out for you during the season. These late rounds are where you want to try to find high upside guys, or maybe even take some risks. In taking Leclerc and Odor, I’m taking risks. Leclerc could end up not being the closer in Texas, his ERA could spike, or both! Odor is a batting average risk to a point where he may be batting under the Mendoza line. Those two players could also turn out to be very good. Christian walker had elite batted ball numbers from last year, so I’m hoping he takes a leap forward in production this year. Renato Nunez was another panic pick as the time wound down on me. I’m wishing I had picked Kendrick, Castro, or Caleb Smith there. I loved the Bumgarner, Renfroe, Lowe, Boyd, Smith, Kendrick, Mercado, Castro, McKay, and Gallen picks from these rounds. Smith and Boyd my be risky in terms of ERA, but they’ll strike out 200 batters in a normal year. Gallen has a lot of upside in Arizona. And McKay is a two-way player that could actually be productive on both sides of the ball. I’ve written about Castro previously, and I think Mercado could hit 20/20 next year. Some picks I thought were a little too risky, even in the late rounds were Santana (who is a prime regression candidate for this year), Britton, Ryu, Mallex Smith, McMahon, and Hosmer.

The full team I ended up with is

C – Garver, 1B – Bellinger (OF), 2B – Altuve, 3B – Rendon, SS – T. Turner, MI – C. Seager, CI – J. Turner, OF – Yelich/Gallo/Pham/Laureano/Kepler, UTIL – C. Walker, SP – Morton/Greinke/Darvish/Lamet, RP – Chapman/Osuna/Hendricks/Diaz/Leclerc, Bench – Odor/Nunez/Dozier

I’d grade this as a solid A-, maybe a B+. I followed my strategy and ended in the top 3 in every power category, as well as the 3 pitching categories I targeted. I put myself in a good position to win each week. I think my bench could use some upgrading. I also need more positional flexibility. Aside from that, I’m pretty happy with how things turned out.

Next week, I’ll go over any updates to the league. I’ll also begin my series on pitching stats that I look for when scouting and how I factor those data points into my drop/add, start/sit, and trade decisions. Thanks for reading.

Let’s Rewind…

Over the last year, I’ve spent time breaking down several sabermetric and standard statistics that I believe are central to understanding a batter’s true potential and whether or not what they are producing is legitimate. I have been outlining ways that you can look at the data to see if the numbers players are producing are above, below, or right on what they should be doing. I have also provided you with the tools to find players who are undervalued or overvalued, and which players to target. To review, you should be looking for

1. Hard Contact %
2. BABIP
3. LD/GB/FB %
4. HR/FB and GB/FB %
5. Plate Discipline data (O-Swing%, O-Contact%, Z-Swing%, Z-Contact%, Contact%, and SwStr%)

The final hitting stats that complete the puzzle for me are BB% (Walk Rate), and K% (Strikeout Rate). Including these data points with the others will give you a solid picture of what kind of hitter you’re looking at. By combining your analysis of all these statistics, you’ll be able to project how a player will do, which will allow you to field the best possible fantasy baseball team.

The Stats

These are very simple statistics and very simple definitions. BB% or Walk Rate, is the percentage of at-bats that a player walks. And K%, or Strikeout rate, is the percentage of at-bats that a player strikes out. I like to include these percentages in my analysis because they deepen the understanding of what type of hitter you’re analyzing. If you see a high BB% and a low K rate, you know you’re looking at a disciplined hitter, who should be doing well. If those are reversed, you’re looking at an undisciplined hitter, and likely someone you want to avoid. Obviously there are some exceptions to this rule.

Why Are These Stats Important?

These stats can be tricky because you can look at them on their own and think you’ve found a stud and other times you can look at them on their own and think you’ve found someone to avoid. But really, you HAVE to combine your analysis of these stats with your analysis of other data points because these stats can be misleading if you don’t understand them. There are a lot of different types of BB% and K% combinations that can show a lot of different types of players. But it’s really all the other stats in combination with these that help you see the whole picture. I’ll give you a few examples…

High K% and High BB%: Yasmani Grandal (White Sox C/1B), 22K% and 17.2BB%. By looking at just these numbers you don’t necessarily know what to think. He shows patience and discipline in his walk rate, but his strikeout rate is pretty high too. BUT! If you look at all the other stats, you’ll see he makes hard contact, his 2019 BABIP is in line with his career numbers, he has excellent LD/GB/FB numbers, his HR/FB and GB/FB percents are very good, and he shows good plate discipline. And in fact, he is improving with age in many of these areas. So why are the BB and K rates important? They help solidify the type of player I’m looking at, and reinforce the fact that he’s a patient hitter that will get on base. He strikes out too much to ever be a truly elite hitter, but he can be productive.

Low K% and Low BB%: Hanser Alberto (Orioles 2B/3B/OF), 9.1K% and 2.9BB%. This is crazy. So in 2016, Alberto had 58 at-bats and walked 0 times. ZERO. He also struck out in nearly a third of his at-bats that year. In 104 at-bats the year before, he walked 1.9% of the time. The good thing with him is that his K% has been improving. Looking at these percentages in isolation would make you think he is a pretty disciplined hitter for the most part – a tough out because he doesn’t strike out that much. This is where you really want to look at the other numbers. He has a horrible 24% Hard contact rate. His ground ball rate is nearly 50%. Combine those stats with a very limited walk rate, and I see a player to avoid. He’s not someone that is going to get on base a ton, and he isn’t someone who is going to be very productive when others are on base. Don’t let the .305 batting average from last year fool you either. With everything accounted for, including BABIP (which was about 20 points over his career BABIP), I’d expect a .280 average to be his norm, but I expect him to be unproductive in other categories.

High K% and Low BB%: Domingo Santana (Indians OF), 32.3K% and 9.9BB%. Traditionally, these are the types of players you want to avoid, and Santana is no exception to that rule (Yes, there are exceptions…see Javy Baez, Franmil Reyes, Pete Alonso). Santana has the power to be good, but lacks the discipline. He whiffs on too many balls 14% SwStr and 78% Contact last year. He has a relatively high BABIP for his career, probably due to good LD and FB numbers, but it isn’t helping him turn that into a good average, or consistent production. He’s a player where if his K% was even 10% lower, his batting average and other traditional statistics would reflect a very good hitter. Instead, he strikes out too much and lacks the discipline for me to trust him enough to roster him. If you do roster him, know that you’re likely getting a REALLY streaky hitter who is going to have some decent weeks of power, but who is going to strike out. A LOT.

Low K% and High BB%: There’s only one player who fits this mold from last year. And this is the type of player you want to target. This type of player is one that shows patience and discipline. You could find others who sort of fit this, but the one player who exemplifies the low K rate and high walk rate from 2019 is Alex Bregman. He’s a stud, and I don’t think I have to get into his advanced metrics. So, I’m going to move on.

How Do I Find These Stats?

This one’s easy. Go to Fangraphs, and you can either

1. Click leaders at the top right, select 2019, and then just scroll down a little bit

OR

2. Select a player by typing their name into the search bar at the top left, and then scroll down a little bit

The walk and strikeout numbers are in the standard dashboard settings. Here’s a look at the leaderboard with BB% selected so that it sorts by the highest walk rate (no surprise who’s at the top):

Like we’ve been doing for a few weeks now, what I want to do is combine the practice of finding this stat with some analysis. I’d like you to have a window on your device open to Fangraphs, and have another open to your fantasy team from this year (if you’ve drafted), or last year’s fantasy team or some players that you’re targeting in this year’s draft (if you haven’t drafted yet). For the sake of the exercise, I’m going to pick a player who I’m curious about in my league’s upcoming draft, and that is Starlin Castro. So I’m going to type his name into the search bar, then after I click on his name, I just scroll down a little to find his BB and K rates.

Starlin Castro, now of the Washington Nationals is an interesting player. He’s a career .280 hitter, but has shown potential to bat .300, although in the years where he’s batted .300, his BABIP has been astronomical. There are also years where his BABIP has been well below his career mark, and accordingly his average has dropped to the .270s and .280s. But, let’s focus on his BB and K rates. Historically, he doesn’t walk that much. His career walk rate is 5%, and he only walked 4.1% of the time last year. And his 16.4% strikeout rate was right in line with his career rate. He doesn’t strike out a ton, but also doesn’t walk very much. He’s interesting because you can’t really tell a lot from just these numbers. You have to dig deeper.

Hard Contact: 41.9%… I really like this. It’s also important to note that this improved from the year before, showing that something changed in his swing to help him hit the ball harder. Added power = added production.

LD/GB/FB: 19.1%/47.9%/33%… I like the FB%, and the LD% is okay. He hits a lot of ground balls though. I wonder if coming to the Nationals and having Kevin Long as his hitting coach will help him improve on launch angle and adjust those numbers. I’ll be watching this season (hopefully) to see if these numbers get better.

HR/FB: 12.5%… This is pretty good for they type of hitter he is. He is not a typical power hitter, so you can’t expect a 20% HR/FB rate, but it’s a good sign that he’s turning fly balls in to home runs.

GB/FB: 1.45…This is a number I don’t like; however, it’s one of his best GB/FB ratios in his career. So hopefully we’re seeing a turning point, and that number continues to get closer to 1.

O-Swing/O-Contact/Z-Swing/Z-Contact/Contact/SwStr: 34.5%/70.2%/69.4%/90.9%/82.5%/8.6%… For the most part, I like these numbers. His chase rate is a little high, but is in line with what he’s done for his career, and that’s resulted in only 1 bad season for him. He makes contact with pitches inside the zone, which is good. He doesn’t swing and miss very much, and his overall contact is good, not great.

Overall, I think he’s draftable for sure. I wouldn’t have him as a middle infield starter, but I’d love to have him as part of the rotation there – either a bench player or someone I can slot into the utility role.

Who Do I Target

Strictly based on searching for BB and K rates, I would suggest that you target 1B Carlos Santana from Cleveland. Especially if you play in an OBP league, this guy is a must have. He consistently walks a good amount (15.7% in 2019, and 15.4% career), and doesn’t strike out too much (15.7% in 2019, and 16.5% career). So you know you’re getting a guy who will get on base, and who will be a tough out at the plate. This often leads him to have productive years in the Runs and RBI categories. In fact, 2019 was a career year for him in Home Runs, RBI, and Runs. He did benefit batting 3rd and 4th in the order behind Lindor, Jose Ramirez (who sometimes protected him at 5th in the lineup), Franmil Reyes, Oscar Mercado. His other numbers solidify him as a solid hitter. His hard contact rate was 43% last year, and all of his batted ball percentages were excellent. His plate discipline numbers show that he doesn’t chase a lot of balls outside of the strike zone, he doesn’t swing and miss very much, and when he does see pitches in the zone, he makes good contact. This is a guy who doesn’t quite make it into the top 10 first basemen for me in fantasy baseball because of the batting average risk, but if the timing is right and I need a first baseman, I won’t hesitate to take him.

Well, that’s all for today. Thanks for reading.