I’m finally at the point where I need to start working in replacement level production into my projections. Today’s focus is on hitters will miss a set time frame or just assume will miss some time. I’m not going to work in players who will miss time here and there (e.g.the Ryan Braun special) for this or that nagging injury. Instead, I’m focusing on batters who can be DL’ed and someone else can take their place.

To evaluate these players, their time off needs to be determined (my goal today) and then replacement level stats can be added in for these off weeks. The replacement level stats will be an average of the available waiver wire batters. While these replacement level players aren’t great, they will provide some production until the rookie/injured/suspended player returns. With every league being unique, owners are going to need to find this talent level for their own league.

And remember, these are my estimates (link to on-going updates). Each owner should make their own adjustments for their own risk tolerance.

Injured with a known time frame

Gregory Polanco: He had shoulder surgery in mid-September and had a seven to a nine-month recovery time frame with a mid-April to mid-June return.

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Yesterday, I examined some hitters with the largest plate appearance projection discrepancies using five separate sources. Today, pitchers take center stage.

I’m using the projected playing times from FanGraphs Depth charts and four other sources. Again, I won’t name them as I don’t want to debate the merits of each. My goal is to just find and evaluate different takes on the same players.

The differences aren’t nearly as drastic as the hitters, especially for the top arms. Most of the major differences involve around unknown roles and injury risks. I listed the top-200 at the end while the values for free agents and foreign signings may be off or missing. I’m going to focus on the biggest differences in some top-ranked pitchers.

Ross Stripling
Range (Max-Min): 67
Average: 112
Standard Deviation: 33

I’m not surprised one bit on Stripling. Unless the Dodgers get hit with a ton of injuries to their starters, he’s going to begin the season as a reliever or in the minors. He’s got the talent to be a top-20 starter but the number of starts he’ll get is unknown. Owners can draft him and hope his talent forces the Dodgers hand. The other issue is that he’s on the Dodgers who manipulate their roster to give pitchers scheduled days off via demotions or “injuries”. Good luck guessing right with him.

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It is projection churning season and today, I’m going to investigate hitters in the top-200 who have the largest variation in projected plate appearances. I’m trying to see who seems off and any adjustments I’d make to their projections.

Normally, I just use a plate appearance average of several unique sources for my projections. I don’t have time to adjust each player. For now, I’m using five sources who constantly update their projected playing time. One is FanGraphs but I’m not going to reveal the other four as I don’t want to debate their merits. More importantly, it’s tough to know for sure who is wrong and who is right. In most instances, a reasonable explanation can be drawn for any total. Besides the players with the larger variation, I’ve included my top-300 hitters at the article’s end with their plate appearance differences.

Note: I ignored catchers and will look at them in detail at a later date.

Eloy Jimenez
Range: 336
Standard Deviation: 238
Average: 446

I’m not surprised with Jimenez being divisive. I sort of expected projections to have him being promoted either in mid-April (extra year of service) or early June (miss July two cutoff). One source was extremely low with a sub-300 value increasing the range. For me, I’d split the possible callup dates and go with a 525 plate appearance total and adjust it as more news becomes available.

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The amount of stats available to analysts today is overwhelming. At least it’s getting that way with me. I’d prefer everything to be available to investigate an idea. But no one has the time to go over every single stat to see if a player has changed for the better or worse. I’m going to eliminate all but 10 stats to focus on those few that matter the most.

The key behind my analysis is to find if a pitcher or hitter has changed. A few dozen projection sets exist to set a talent baseline but once the season starts, most people want to throw them out (some even before the season) if a hitter is batting .150 or a pitcher has a 6.00 ERA. Players are human and creatures of habit so most won’t change. But some do and being able to focus on these few can help to find values.

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I love the idea, almost to a fault, of taking one or two of the top 5 SPs in the first and/or second round. The reason I like getting these top arms is that they provide a ton of strikeouts with great ratios. This base in ratios allows me to ignore pitching for almost 10 rounds and then bottom feed. One problem I’ve run into with this strategy is that I won’t have this option available if I’m picking in a draft’s first few spots. I’m not passing on the five-category hitters and according to the current NFBC ADP (average draft position), the top Aces will be gone by the time my second pick comes around. If I’m forced into this situation, I’m considering going with the what I’ll call the “Streaming WHIP” plan.

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Yesterday saw some fantasy applicable free agent signing occur which deserve a discussion.

A.J. Pollock signs with the Dodgers

I can’t quit Pollock, almost to a fault. I keep latching onto his 2015 season even though he hasn’t played over 113 games since then because of injuries. Now, he is going to a team notorious for limiting playing time thereby making his value is tough to unwrap.

He was productive during his stretch of limited playing time. Prorating his three-year stats down to 600 PA, he would have 88 Runs, 23 HR, 73 RBI, 23 SB and a .261 average. It’s tough to find a player with similar 2019 projection but here are a few. Ronald Acuna is at 28 HR, 24 SB, and .276 AVG. Braun at 25 HR, 13 SB, .265 AVG. Tim Anderson with 17 HR, 21 SB, .252 AVG. If playing a full season, he’s a top-50 hitter.

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The 2018 season opened my eyes to the risk of young players losing playing time because of massive platoon splits. Usually, it’s the established major league veterans like Matt Adams who get platooned. Manual Margot was such a player who usually led off against lefties but was dropped in the batting order or even to the bench against righties. Those lost plate appearances can add up over the course of a season whittling away a hitter’s value. Today, I’m going focus the most talented hitters who are most like to be platooned.

For Tanner’s and my book, The Process, Steamer projections were kind enough to include every hitter’s projected OPS versus right and left-handed pitchers. From the book’s research, we found teams have historically started platooning hitters when one side of the platoon’s OPS drops below .650 and definitely at .600. Here are some the hitters who may end up in a platoon situation if their team decides to go that route. Besides the risk of sitting out games, some teams move these players up and down the lineup based platoon differences. I’m likely going to give some of these hitters a playing time adjustment.

Note: The projected platoon OPS are league and ballpark neutral and may seem off compared to the current projected OPS.

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It’s time to get nerdy. Very nerdy. While I’m going to post some auction values end the article’s end (I just lost 99% of the readers), the focus of the words is to concentrate on setting a pitcher-hitter split by just using Standings Gain Points (SGP). It’s not going to work with every league type. Not even close but it’s a theoretical solution which can help an owner the best chance to get the most value out of every draft pick or auction dollar. It’s definitely a better option when trying to set in-season values.

The following process is definitely ripe for discussion and argument. While writing The Process, Tanner and I debated this idea and even brought in others to determine if the pitcher-hitter split can be done by using SGP values. We published without including the concept as we were still ironing it out. We are still not in 100% agreement but here is the idea.

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Catchers … what a dumpster fire for a fantasy position. I can’t remember it being worse. It probably was at some point but with the increased knowledge of pitch framing, more horrible hitting catchers are accumulating plate appearances. Today, I’m going to go through the natural catcher tiers for various league types and show where owners need to buy for the best values.

Before I go any further, I need to explain a few concepts. I’m going to be using Standing Gain Points (SGP) to differentiate the various players. The main reason I like to use SGP is it takes all of player’s Roto stats and combine them into a single value. This important with differentiating the value of a speedster leading off or a middle of the lineup slugger. Additionally, I’m using The Process’s 15-team, two-catcher formula for all the following comparisons. It may not be the perfect formula when comparing different league types but it will provide a nice baseline.

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It’s time to start discussing pitcher rankings and fantasy managers need to know it’s a new landscape for two reasons. First, the idea of every team having one or two 200-inning starters is *over*. The top arms are putting up similar stats to the past. The change now is with the floor. It has just fallen. Starters are just not going as long and the Wins and Strikeouts they accumulate are gone. Second, many bullpens are now going to more of a committee approach where there aren’t 30 set closers but more like 20. The lack of Saves in a concentrated few closers boost their value and the overall value of every Save.

It’s time to get to the rankings. I used the 15-team Standings Gain Points (SGP) Formula from The Process to create these rankings. I used FanGraphs Depth Chart projections (stats in the table) along with three other sources (not to be named). I ranked them by the average SGP value and also included the standard deviation in all the values.

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