Last week, I posed the question if Shohei Ohtani should be pitcher eligible, despite serving as designated hitter 65 times last season, compared to just five appearances on the mound. For those concerned I’ve lost my mind, of course he should. That said, unless your commissioner is extremely prescient, I bet your rules don’t define pitcher eligibility. Further, I promise you, there’s a jealous owner in a dynasty league causing a ruckus, trying to screw over the Ohtani owner.
Obviously, pitchers are pitcher-eligible because they’re pitchers. There hasn’t been a need until now, and some may argue there still isn’t a need. Here’s why all rules could now include specific pitching eligibility. What happens if Ohtani suffers an odd injury that prevents him from taking the mound this season, but he’s able to hit, and is expected to pitch again in 2019? Is he not eligible as a pitcher since he didn’t pitch in 2018?
My proposal is defining a Hitting Class and Pitching Class. The eligibility within the Hitting Class is exactly as it is in your current rules. The Pitching Class is anyone taking the hill at all the previous season, provided your scoring service is set up to score their pitching stats along with a common-sense clause permitting eligibility for anyone obviously expected to pitch the upcoming season. Further, an individual player can have eligibility in both classes, to be deployed according league rules governing such players.
“Provided your scoring service is set up to score their pitching stats” is included is to cover the pinhead owner wanting to start Chris Gimenez (as an example). In some head-to-head formats, some may opt for no stats from the spot as opposed to risking a pitcher damaging ERA and WHIP.
The common-sense clause covers the Ohtani scenario. There’s also a chance a position player converts to a hurler without having pitched the previous season.
So, no, I haven’t gone crazy, at least not yet. Common sense should prevail, but we also should formally address this new quirk in our constitutions.
Now that most leagues have decided what to do with Shohei Ohtani, I have a question: Should he even be eligible as a pitcher to begin the 2018 season?
Stop rolling your eyes and stay with me. The rule almost everyone uses for players not appearing in MLB, or a minor league affiliate is assigning eligibility as the position played most in their league. In 2017, Ohtani appeared more at designated hitter than pitcher – WAY more. According to his page on Baseball-Reference, Ohtani appeared in 65 as a designated hitter, compared to only five as a pitcher. By the letter of the law, doesn’t this make Ohtani utility-only to start the upcoming season?
I’m not sure the answer is unequivocally “NO!”
The decision to make him pitcher-eligible is driven by the likelihood he’s a bigger difference maker from the mound than the batter’s box. What if the background were reversed and he profiled as a potential Triple Crown winner with less promising pitching skills? Would we be considering Ohtani as both, considering the 65 to five game disparity? I’m not convinced the answer is no-brainer, “YES!”
The thought emanated from a discussion with Ron Shandler and Brian Walton concerning how we’re going to handle Ohtani in the XFL, a keeper league where Ohtani has been owned as a farm player by a prescient participant for multiple years. Ron suggested we first need to define whether the player pool needs to be divided into a hitting and pitching class, then decide eligibility within each class. The final piece would be whether an individual player can have eligibility in both classes?
Even this isn’t cut and dried, since the designations need to cover all players, so someone couldn’t have Madison Bumgarner, as the obvious example, getting hitting stats.
Another major consideration is germane to keeper leagues. The mechanism in place to determine initial eligibility should also cover keeper eligibility. Precedence and consistency are two principles integral to a league’s constitution.
Finally, it can be argued we all caught a break with Ohtani signing in the American League as that mostly masks the issue whether Ohtani receives hitting stats the days he starts on the hill in an interleague affair played in an National League park. Especially in dynasty formats, there’s a strong possibility Ohtani plays for a club in the Senior Circuit sometime in his career.
I don’t mean to open Pandora’s Box, but the more I think about it, the more I can see Cousin Vinny making the case Ohtani should begin 2018 with just utility eligibility, then picking up pitcher eligibility after five games, or whatever threshold your league employs.
Now, can you tell us by what you see in this posting, if the defense's case holds water?
Or is the defense wrong?
Please feel free to chime in below in the comments or on the site’s revamped forums.
I know I shouldn’t let it bother me, but it does. Hearing/reading the following really bugs me.
“Projections are a waste of time.”
“All projections do is average past seasons.”
“I don’t trust any projections that don’t go out on a limb.”
“Projections are silly. They’re just guesses. I can guess as well as a so-called expert.”
I shouldn't, but I take these personally. I'm frustrated the comments are mostly naïve to what a projection truly is, as well as its purpose. That’s the nice way of saying it. For some it goes beyond naivety. Some are too lazy to educate themselves on the concept, while others are too arrogant to care. Regardless, they’re passing judgment on a subject while lacking the underlying understanding to tender a valid argument.
When someone contended they didn’t use projections to draft their team, I’d counter with everyone uses projections. Some may not be spreadsheet-driven, but how we feel about a player is our projection.
Then I looked up the on-line definition of a projection:
An estimate or forecast of a future situation or trend based on a study of present ones.
And compared that to the definition of a prediction:
Something said or estimated that will happen in the future.
It turns out I was wrong, we all don’t use projections, some use predictions. The difference is “based on a study”. Projections have a theoretical basis. Predictions may, but they don’t have to. It’s cliché, but it works:
All projections are predictions but not all predictions are projections.
What follows is my view of a projection, what it is and how it should be used. This is all as a precursor to bringing my projection methodology out from behind the firewall. Sorry, 2018 projections are obviously still privy only to subscribers, but hopefully understanding what they are and where they come from deepens your knowledge base and helps win.
There are several ways to consider a projection. My favorite is a weighted average of all plausible outcomes. Think of it this way. Let’s say each season were played a gazillion times. The projection would be the average of the gazillion outcomes. The weighted average encompasses all combinations of skills and playing time.
Let’s start with the latter. Possible playing time for everyone is zero plate appearances (PA) or innings pitched (IP) to not missing a game or start. The chances of either are remote, but over a gazillion seasons, all players will have some where they get hurt in the spring. Very few hitters can reasonably be projected for 162 games while a decent number of pitchers will start 32 or 33 times, though obviously only a rare few will be complete games. The rest of the playing time projections will range in between, with a cluster around historical numbers. Players with an injury history will have a greater number on the lower side, with fewer above their recent amount.
Let’s forget performance and focus on just playing time of full-time hitters without an injury history, getting days off here and there. This group usually receives about 650 PA, spanning 150-155 games. What should the correct playing time expectation be?
Over a gazillion seasons, the average playing time will be fewer than 650 trips to the dish. There’s about 150-155 chances for an injury truncating a season compared to only 7-12 opportunities for more playing time. Accounting for all gazillion seasons, the average is below 650. Keep this in mind when thinking about expected playing time.
Shifting attention to a batter getting 200-250 PA, totaling maybe 75 contests, there’s several pathways to get there. Some are part-time players, others lost time to injury. There’s always mid-season call-ups and those, bouncing between the majors and minors, etc. Each case is different, with a unique array of playing time possibilities. It’s impossible to answer the above question with 250 PA without more info. However, it stands to reason over a gazillion seasons, more 250 PA hitters will be projected for more than that as compared to 650 PA batters.
Let’s put the theory to the test. I did a study using the previous six seasons, yielding five data points. The test was looking at the number of PA at different levels, determining how many received more or fewer the following year. I used five and ten percent more (and fewer) as the target. So, a 600 PA hitter needs more than 660 to be counted as more than ten percent with a 200 PA guy needing only something above 220. Here’s the results:
|PA||#players||0-5% more||5-10% more||>10% more||5% fewer||5-10% fewer||>10% fewer||overall more||overall fewer|
The 651-plus subset encompasses the most players, yet only one hitter bettered their previous season’s total by at least ten percent. In fact, only 18 percent beat the number at all. On the other hand, a whopping 44 percent dropped by at least 65 PA. That’s pretty mind-boggling, at least to me, screaming extreme prudence when projecting playing time. However, keep in mind all players are subject to similar scrutiny, so if everyone is docked some PA from the previous campaign, on a relative basis, their fantasy ranking is unchanged. Still, this helps speak towards why projections rarely expect a great deal more production for players at this level.
As anticipated, the fewer PA garnered one season, the better chance of accruing more the next. Don’t read too much into so many being either ten percent more of fewer at the lower levels. Again, 10 percent more than say, 300 is 330, or seven to ten games.
Skills analysis is quirky. Not even considering all outcomes aren’t an exact translation of skills, we’re really looking at a range, not a singular measure. That is, if a player’s skill is “70”, some days he’ll play like a “65” and some “75”, without any influence from outside factors like luck. Bringing in luck while still ignoring playing time combinations, we need to account for a range of skills affected by a slew of luck-induced scenarios. Now take all this and play it out for every playing time possibility and maybe even a gazillion data points aren’t enough.
To get an idea how repeatable performance is, I did a study like the above using hitter’s home run and stolen base rates. It’s important to note HR and SB rate aren’t true skills, but they make a suitable proxy. The reason is there’s some happenstance involved with each. The homers are influenced by park factors as well as some luck with respect to weather conditions and in what part of the park the ball was hit. Steals depend on how often a player reaches base in a scenario conducive to an attempt and is given the green light. Both could be distilled further into more of a skill metric, but it’s not necessary for this discussion. It’s part of making the projection sausage which will be detailed in a follow-up discussion as mentioned earlier.
To qualify, a hitter needed 200 PA and at least 10 HR or SB during the season in question. Their performance was converted to a rate, using PA and compared to the average rate of everyone in the study that season, yielding an index. A player with a rate equal to the league average was scored as 100. Better than league average was over 100, worse was below. Doing it this way accounts for both playing time differences from season to season and league context each year. Hitting the same homers in the same PA in 2017 and 2016 aren’t the same since more homers were struck last season.
Incorporating six years of data, giving five data points reveals 60 percent of qualified batters crack homers at a lower rate one season to the next while 69 percent swipe bags at a poorer pace. Admittedly, this is vague, you no doubt have some granular questions concerning the results. Platinum subscribers will have their queries answered soon this will be written up in a chapter of the Z Book. For this discussion, suffice it to say more than half of fantasy-relevant batters perform at a lower rate than the previous season.
Keep in mind, this looks at rate of performance. More than half of hitters exhibit a rate worse than the previous season while probably playing less. This isn’t conjecture, it’s research-based fact. Those chiding the efficacy of projections do so without any inkling of this and is the crux of what frustrates me. To berate projections without truly knowing what they are is ignorant.
I can appreciate disagreeing with the importance as pertains to playing fantasy baseball. If you tell me your ballpark estimations of performance work for you, that’s fine. This means you excel at other elements of game play. You can gauge draft flow and react accordingly. You know your opponents and their tendencies. You have a keen sense of where your expectations relate to the market, and know how to take advantage to the fullest. You work your tail off in season, making sage pickups and manage your roster to achieve maximum points.
On the other hand, using projections as opposed to predictions isn’t a guarantee of success – not even close. I don’t want to put a percentage on it, but if each factor was in proportion to a pizza, eating the projection slice would leave me hungry. Very hungry. And envious of those eating the other slices, a metaphor for possessing the other talents.
As good as you are, all I’m saying is you could be even better with more refined player expectations.
Obviously, you can choose otherwise. All I ask is to think twice before denigrating a process without an honest understanding of what it’s all about and what it’s supposed to be used for. There’s method to projections madness. They’re not a waste of time. They’re more than guesses. They aren’t supposed to go out on a limb. They’re supposed to provide a plausible baseline of player performance. What you choose to do with it (which includes ignoring them) is your call.
Thanks for letting me vent a bit. For those interested, I’ll soon be posting my projection methodology in this space, as well as providing the complete research discussed above for Platinum subscribers.
I don’t usually say things like this, but I doubt there’s anyone in the industry with a more thorough understanding of projection and valuation methodology than your normally humble author. That said, the deeper I understand the concepts, the more I realize it’s what you do with them that matters. It’s an oversimplification, but projections beget rankings, which spawn a cheat sheet. However, drafting isn’t taking one from the top. Among other things, having an appreciation where a projection and ranking emanate help direct your draft pick or auction bid.
The Mastersball projection and valuation methodologies have always been available to Platinum subscribers. Since both need a refresh, and the site recently underwent a facelift of its own, I decided it was time to take these processes out from behind the firewall. Yes, it’s thinly veiled attempt at recruiting more subscribers, but I think there’s probably some interest, regardless, in these topics.
The rest of the discussion will detail how I generate hitting projections. A follow-up piece will describe the process for pitchers. As always, I’ll be happy to address questions in the comments or on the newly revamped message forums.
In a nutshell, past performance is distilled to a neutral per plate appearance basis. Regression, aging and team context considerations are applied, then the projection is generated by multiplying by projected plate appearances.
Let’s start with past performance. I’ve looked at how many years provide the best baseline and have landed on the same number many others employ: three seasons. Others use more, I doubt any use fewer. Within this three-year spread, most recent performance carries the greatest weight with the oldest contributing the least. Again, nothing ground-breaking here, though we all may deploy different weighted averages. With the recent power surge, I looked at my current weightings to see if they’re still optimal and ultimately opted to leave things status quo, with the option of overriding when warranted. In fact, the older I get, the more comfortable I am massaging here and there without the threat of being struck by lightning.
Most of you are likely aware of Major League Equivalencies (MLE), used to project players spending time in the minors within the three-year span. For those not familiar, an MLE takes the prospect’s numbers and translates them to what they would have been in the majors. They’re adjusted for park, age and league environment. Unless forced due to no other data, only Double and Triple A numbers are included. MLEs aren’t perfect, but they’re useful by adding another data point. I use a pseudo-MLE for players coming from Japan, Korea, Cuba, etc.
An area I’m sure I differ from everyone else is my procedure for applying park factors. The means of determining park effects goes beyond the scope of this discussion. Platinum subscribers can access a lengthy essay on the subject in the Z Book. In short, the calculation is designed to flesh out all team factors and biases, strictly measuring the venue. Since this is impossible, a three-year average is used when taking the neutral projection and accounting for home venue.
Park factors for batters are computed for left-handed and right-handed swingers. For switch hitters, the factor is a weighted average based on the average number of times a switch hitter bats from each side of the plate. Currently, switch-hitters hit left-handed 73 percent of the time.
The conventional manner to apply factors is as follows. Let’s say the park-neutral homer projection is 20 and a hitter’s home park has a factor of 120. This means that venue increases a neutral projection by 20 percent. However, since the hitter only plays half his games at home, he realizes only half of that, or ten percent, yielding an actual projection of 22 homers.
Intrinsic to this is assuming the sum of the road venues is neutral. Here’s where my treatment differs as I utilize composite park factors. What I do is use a weighted average for all 162 games. The result is close to the normal method, with sufficient differences to make the effort worthwhile.
The first step in the neutralization process is taking the actual production and fleshing out the park factors specific to that season (not the three-year average). Most are familiar with factors for homers, hits and runs. They exist for everything. I apply park effects to doubles, triples, walks and strikeouts as well as the others. Players on multiple teams in a season have the numbers adjusted per that factor. This can cause issues since the away games don’t match the composite factor, but the same error is present with the usual method, since the smaller sample of games could skew the away parks away from neutral.
At this point, I have the park-neutral projection for all major leaguers as well as the MLE for all minor leaguers. All the yearly components are summed to neutral performance for that season and the weighted average applied to each, culminating in single stat line. The number of plate appearances for each season are carried through the calculation. Here’s an example, using homers, and my 11:7:4 weights.
2017: 20 HR is 500 PA
2016: 16 HR is 480 PA
2015: 12 HR in 400 PA
HR: ((20 x 11) + (16 x 7) + (12 x 4)) / (11+7+4) = 17.2
PA: ((500 x 11) + (480 x 7) + (400 x 4)) / (11+7+4) = 475.5
So, the neutral projection for this player is 17.2 HR in 475.5 PA. This is done for all the stats involved in the projection.
Now it’s time to go through the individual stat projections. The final projection is a lot more than just taking the above, applying an aging and park factor.
There are two components to a home run projection: home run per fly ball (HR/FB) and fly ball percent (FB%). A hitter develops his own baseline for each. There’s a skill and luck aspect to each, though there’s thought to be more luck associated with HR/FB.
There’s a lot out there with respect to lucky versus deserved homers. Some studies look at fly ball distance. Others use park overlays or scrape Statcast data and use exit velocity and launch angle to derive an expected home run calculation. While I’m aware of the current research landscape, I don’t presently incorporate an algorithm to adjust for lucky homers. I’ll note players others have recognized as outliers and put them under the microscope, adjusting on an individual basis.
The subset I struggle with most are those known to have added loft to their swing. This is a recent occurrence, so the conundrum is whether last season, or possibly 2016 as well should carry even more weight than before the adjustment was made. One of the elegant aspects of the three-year weighted average is this happens organically. That is, the chance the player can’t sustain the change is accounted for. There are a handful of batters I decided to buy into the notion of a conscious elevated launch angle and those projections reflect that. It’s my expectation this will soon be handled with a refined expected home run algorithm, once more data is available. That said, the likelihood of the juiced ball is skewing things too. It isn’t skewing the calculations, but if the ball doesn’t jump off the bat as much, the hitter’s exit velocity could drop from what’s expected based on previous season’s measurements.
Hits (Part One)
You’re no doubt familiar with batting average on balls in play (BABIP). A player has some control over how hard they hit the ball (now called exit velocity) and the hit type (now called launch angle), but there’s little, if any control exactly where it goes and whether there’s a fielder in range to make a play.
Hit types can be parsed into several classifications. I have data for groundballs, fly balls and line drives, all hit hard, medium and soft along with bunts and pop-ups. That’s eleven classifications. I determine a global BABIP for each, then based on each hitter’s distribution, calculate an expected BABIP (xBABIP). This is done for the same three-year spread of data fueling the projection.
There are several reasons a player’s BABIP differs from their xBABIP. The notion of luck (good and bad) was just suggested. Speedy players can beat out grounders at a clip higher then league average while plodding hitter get fewer infield hits. The shift is also influencing BABIP.
Since I don’t know the exact reasons for a hitter’s delta between BABIP and xBABIP, I regress BABIP towards xBABIP, with .5 the initial setting. This amounts to an average of the two. It’s not perfect, there are some park factors that can’t be fleshed out since that requires a ton of work at the granular level, but I’ve found this treatment to be effective. Of course, I reserve the right to override the regression and force the projected BABIP to be more like the player’s baseline when apropos, usually for speedsters.
The equation for BABIP is
BABIP = (Hits - HR) / (At Bats – HR – K + Sac Fly)
Solving for Hits:
Hits = (AB – HR – K + SF) x BABIP + HR
So, to get the adjusted park neutral hits, a little more information is necessary. Let’s put this section on hold.
I project plate appearances then determine at bats by subtracting out the non-AB components. Specifically,
PA = AB + BB + HBP + SF
AB = PA – BB – HBP – SF
There’s a component missing and that’s catcher’s interference (CI). Jacoby Ellsbury’s record-setting numbers aside, there’s not enough instances to worry about. To wit, in 2017, there 43 CI calls, with Houston leading the way with nine. Ellsbury would approach that number by himself in his prime but even then, I didn’t make any adjustments.
Bases on Balls
As alluded to earlier, walks are influenced by park effects. Foul territory, batter’s eye and atmospheric conditions contribute to how a venue affects walks. The composite BB factor is applied to yield the park neutral walks.
Hit by Pitch
No park factor is applied so this is just the weighted average of the three years in question.
We now have all that’s necessary to compute the number of projected AB. There’s only one component of BABIP left.
The same thinking applied to walks is germane here. Punch outs are influenced by venue, so the park neutral adjustment is made.
Hits (Part Two)
OK, so now we have everything needed for the hits formula:
Hits = (AB – HR – K + SF) x BABIP + HR
This is the park-neutral, regressed hits projection.
Doubles and Triples
As mentioned previously, there are park factors for doubles and triples. As such, I treat them every other projected stat and adjust using composite park factors. It’s not perfect, since the regressed BABIP influences doubles and triples. I don’t sweat triples since the number is usually the same after rounding off. I’ll inspect the projection for hitters swatting a lot of two-baggers to make sure the number passes the sniff test. To be honest, usually, BABIP and xBABIP are close enough so doubles are usually the same after rounding.
Let’s take a moment to review all the stats projected to this point, all park-neutral: Hits, 2B, 3B, HR, HBP and SF.
Still left are singles, stolen bases, caught stealing, runs and RBI. Singles are necessary to project the others so let’s attack that next.
The singles projection is hits minus extra base hits. To get there, we need to take a step back and explain how to go from the park-neutral projection to the final projection. If you recall, plate appearances were carried through the weighted average, just like all the stats. As such, each can be presented per PA. Using the HR example above, 26.2 HR in 475.5 PA equates to .055 HR/PA.
Sticking with HR but keeping in mind this is the process for all skills-based stats, an aging factor is applied. Let’s say the player’s age factor is 1.02. The .055 HR/PA is multiplied by 1.02, yielding .0561.
The next step is applying the corresponding composite park factors. Remember, to get the park-neutral adjustment, the factor for just that season was used. Here, a three-year average park factor is employed since it’s the best reflection of how the venue will play in the upcoming season. For the sake of this example, let’s say the appropriate composite HR factor for a hitter of this handedness is 106. We take .0561 and multiply it by 1.06, rendering .0595.
The final step is multiplying .0595 by the projected PA. I’ll discuss that process later, but for the sake of an example, let’s say I’m projecting 517 PA. So, 517 x .0595 = 30.74. I round all projections to the nearest integer, so our mythical hitter is projected for 31 long balls.
As stated, this is done for all the stats, so to get singles, all the projected extra base hits are subtracted from the projected hits.
Using the actual stats, not park-neutral, I calculate stolen base opportunities (SBO) as a percentage of attempts per times the hitter reached first base (1B + BB + HBP). I realize this omits steals of third, but it serves as an adequate proxy for this purpose. I also calculate the success rate (SB%). Both are carried through the three-year weighted average formula generating a player’s projected SBO and SB%.
You can probably figure out the rest. The number of opportunities based on the projected 1B, BB and HBP is determined and multiplied by SBO to yield the number of attempts. From there, the number of swipes is calculated using SB%.
Since stats are so team oriented, if a player switches teams, I may adjust SBO as I see fit. I’ll also make tweaks for a someone expected to hit in a different part of the order.
Runs and RBI
The process for runs and RBI are the same. What I do is determine a index for each, using a formula I derived for expected runs and another for expected RBI. These do not consider place in the order or team context; they’re essentially how many runs and RBI, on average, a player would generate with their performance.
I determine the xRuns and xRBI using the park adjusted stats for each season. I then adjust their actual runs and RBI using the corresponding composite runs park factor. The index is expected/adjusted. This is then carried through the three-year average formula like the other stats.
Next, I go to the final projection and calculate expected runs and RBI, then adjust by dividing by the appropriate index. The team context and batting order position is baked into the index, so if the player’s situation is similar, nothing needs to be done. However, if the player’s role has changed, he’s switched teams, or even if he stays on the same club but the lineup is better or worse, like the Miami Marlins this season, I can tweak the indices to bring the runs and RBI in line with the new scenario.
Occasionally, a player’s batting average with runners in scoring position (BAwRISP) is out of sync with his overall average. I don’t believe in the notion of a clutch player. A high BAwRISP is simply a cluster of hits with ducks on the pond. Globally, BAwRISP is a little higher than overall average, likely due to pitcher’s working from the stretch with RISP, hence their skills drop a bit. I run a scan of player’s BAwRISP, and if there’s an outlier which isn’t softened by the three-year average, I’ll adjust the RBI index.
We now have all we need for a hitter’s projection. All that’s left is detailing the process, and philosophy for generating plate appearance.
There was a time I was a stickler for projecting exactly as many plate appearances per teams as are likely available. I have since come to realize this is impractical. The primary reason being injuries are predictable to a degree, but there’ still so unpredictable time lost that has to go somewhere. I call this the Ty Wigginton theory. For years, we didn’t know where Wigginton would pick up his 400 PA, we just knew he would. I could either project Wigginton for the 200 PA I could account for, or project 400 and take 200 from elsewhere, even though I had no clue where it would eventually come from.
The key is this is fantasy baseball and my job is to best prepare you for your drafts and auction. Projecting Wigginton for 200 PA would eliminate you from draft him. In deeper formats, this is a useful player. As such, I wasn’t doing my job but taking you out of the running for Wigginton, and other like players. Now, my approach is simple being honest with each playing time appraisal, even if it means projecting extra plate appearances for a team’s outfield or any other position.
Additionally, with the advent of the deep draft and hold format such as the National Fantasy Baseball Championship Draft Champions competition, I need to have a lot more names out there for consideration. This format consists of 15 teams with 50 roster spots, or 750 roster spots. Coincidentally, this is exactly how many players break camp and are on opening day MLB rosters. The thing is, the NFNC DC drafts from a pool outside of this 750. As such, I need to project everyone with a plausible pathway to 2018 MLB playing time. Obviously, this entails over-projecting the expected PA for each MLB team.
However, through it all, I make sure the playing time for the draft-worthy hitters is as practical as possible. To facilitate this, I employ a nifty double-grid method.
The first grid assigns the percentage of playing time each hitter should get per position, including allowances for pinch hitting. The second allots the percentage of time expected in each spot in the batting order. The two are crosschecked to make sure everything is accounted for.
Since teams with more prolific offenses turn the order over more, the hitter projected for 90 percent of the leadoff PA for the highest scoring team should be more than the like batter on the lowest scoring club. As such, I use a three-year weighted average of each squad’s PA per spot in the batting order as the target. When necessary, I’ll override if the upcoming season’s outlook is significantly better or worse than recent seasons. Two examples for 2018 are raising the targets for the Angels while lowering them for the Marlins.
Well friends, we’ve finally reached the end. I’ve been as transparent as possible. Every season I look at the process and tweak where necessary. As discussed, I envision home run projections to be refined over the next several seasons, but am confident there are ample checks and balances currently in place to capture power-hitting outliers and adjust accordingly. All that’s left is remind you the pitching process will be next, followed by valuation theory. Well, that and reiterating I’m happy to address questions in the comments, or better yet, the newly revamped forums.
The Hot Stove was lit early with a few noteworthy transactions. While more moves are expected at the Winter Meetings, there's mumbling things will be quieter than normal.
I'm admittedly a little late to the dance with my take, but here's how I see the recent flurry of activity in fantasy terms.
Chicago Cubs sign Tyler Chatwood
With John Lackey and Jake Arrieta hitting the bricks, the Cubs fortified their rotation with Chatwood. Some are looking at Chatwood's road ERA and WHIP over the last two season and expecting big things in Wrigley Field, a decidedly better place to pitch than Coors Field. Here's Chatwood's vitals, home versus road:
Obviously, Coors matters. i agree with the Twitterverse, the Cubs are a nice landing spot for Chatwood. However, I'm concerned about Chatwood's walk rate. Keep in mind, BB% differs from BB/9 in that BB% uses batters faced as the denominator and is the better measure of the pitcher's skill. Curiously, Chatwood's control as been better a mile high.
Walks are especially important since Chatwood is an extreme groundball pitcher so he's prone to a high BABIP. Granted, the trade off is fewer homers and more double plays, but you really don't want a groundball specialist awarding more baserunners.
Something overlooked is Wrigley Field increases bases on balls by seven percent, four percent more than Coors Field. This makes it that much harder for Chatwood to get his control under wraps.
Health is another factor is durability as Chatwood's 2016 numbers of 27 starts and 158 innings are career bests.
Obviously, this move is an upgrade. I just think it's a mistake to look at Chatwood's road numbers and assume a mid-threes ERA. I'll take a shot on Chatwood if he falls to me, but I'm not chasing him.
St. Louis Cardinals sign Miles Mikolas
Shohei Ohtani isn't the only pitcher coming over from Japan as Mikolas returns to MLB after spending three season with the Yomiuri Giants. Here's the 29-year old, righthander's stats overseas:
The key is obviously great control along with doing a great job keeping the ball in the yard. It goes without saying (but of course I'll say it anyway), these will be the important factors as Mikolas attempts to transition back to the majors as he won't miss many bats.
Japan ball is thought to be a little above Triple-A in terms of MLE (major league equivalency), but keep in mind he would be considered old for his level if he was in Triple-A. Still, when running the numbers through my little black box, Mikolas should be a fantasy asset in 2018, especially pitching for the Redbirds, who have a knack for getting the most out of their hurlers. My actual projection is available for Platinum subscribers, but given the choice of Mikolas or Chatwood, give me Mikolas.
Los Angeles Angels sign Shohei Ohtani
I've written a lot about this for Rotowire (co-posted in Mastersball Platinum) and need to be sensitive to customers, but there's still a few things I can discuss. First, let's look at Ohtani, the pitcher. Here are his numbers with the Nippon Ham Fighters the last three season.
As opposed to Mikolas, Ohtani is just 23, so he isn't penalized by the age to level translation. In fact, in 2015 and 2016, he would have been young for the Triple-A level. Ohtani has electric stuff and profiles as a top-end starter. Ignoring innings, his ratios in Angels Stadium put him in the tier below Max Scherzer, Chris Sale and Corey Kluber.
Here's Ohtani, the hitter the past three campaigns.
Let's ignore 2015. Digging into the numbers, the past two seasons, Ohtani had posted a 26 K%/.394 BABIP and 27 K%/.440 BABIP. The strikeout rate isn't too concerning considering his age, but we have no idea where his BABIP will regress. Scouts like to talk about his power, but it isn't off the charts and who knows how it might develop since he won't be focusing on hitting full time. I'll save the projection for subscribers, but playing half his games in Angels Stadium, his 2018 numbers translate to Sean Rodriguez.
The elephant in the room is Ohtani's health. He missed most of last season with ankle and hamstring issues, needing ankle surgery after last season. He initialy hurt his ankle in the 2016 Japan Series, causing him to miss the 2017 World Baseball Classic.
The injuries concern me on many levels. How many innings will the Halos let Ohtani throw, after tossing just 25.1 last season? Why expose the phenom to even more injury risk as a batter and baserunner when elite pitching is at a premium?
The window of potential innings is wide. I can make a case for 120 or 160. My lean is closer to 130, but I can see more. Regardless, before I'm comfortable drafting Ohtani, I want a sense of how the Angels plan on using him in the rotation. For example, if they say they plan on shutting him down for a stretch, controlling innings while letting him hit), that's a good thing for me since I'm comfortable using someone else in that span. If I'm nervous the Angels will decide to skip his start on a whim, I'm less interested. How his innings are parsed will go a long way towards how eager I am to draft Ohtani.
The New York Yankees acquire Giancarlo Stanton
Last season, Stanton hit 59 homers. Using composite park factors, that translates to 75. Hold on, pump the brakes. I'm not projecting 75, just pointing out, on paper, the difference in the respective venues, Marlins Park (aka the Aquarium) and Yankees Stadium. Everyone talks about the short porch in the Bronx, ignoring the the park is the most favorable for righty power - it has been since its opening. Part of the reason is its friendly to opposite field power.
For the record, composite park factors incorporate the road schedule. It's something unique to my process, at least I haven't heard anyone else doing it. It's a weighted average of all the venues on each team's schedule, home and away.
Park overlays suggest when Stanton hits a homer, it would go out anywhere. Still, I have to figure Yankee Stadium yields a few more homers to Stanton than Marlins Park.
With respect to playing time, on paper, Brett Gardner is the biggest loser with Aaron Hicks and even Gary Sacnhez negatively affected (less available time to DH). The caveat is Hicks durability isn't the greatest, opening up some more time for Gardner, though he's not really a center fielder anymore.
I'll save my Stanton ranking for subscribers, but I'll have a hard time pulling the trigger within the Top-10. This is more about an absolutely stacked first round than Stanton. The injury risk is just enough for me to fade him.
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