This post is a joint effort from Ryan Fickes and Travis Sherer.
So, you have a fantasy baseball team in a dynasty or keeper league. Maybe it’s the first year of the league and you want a strong start. Maybe you’ve been putting up with everyone else taking the crown for a decade or more and you sit there each year, silently seething at their interminable luck as they cackle in delight when your careful plans fall to ashes and their random flyers become MVP candidates. That all ends in 2020, because New Decade; New You™. You’re going to do whatever you have to do to win now and take the crown. 2020 is your year.
Rule #1 | Don’t do this (at least, not exclusively)
Baseball is too variable. We implore you to use a different strategy. Rarely have we seen a manager with a win-now strategy satisfied with the way things went. Even if he or she did win one title, the win-now strategy generally ends one year too soon.
Before proceeding to give more advice, we want to get one thing straight: What does “win now” mean to you? Hopefully you are not being too literal and thinking of winning in 2020 only. This would be a mistake. Drafting to win this year only and then immediately taking on a rebuilding project seems like an exercise in masochism (although we’re not here to judge if that’s your thing).
Also, there is absolutely no way to guarantee success; fantasy baseball just doesn’t work that way. You can have the perfect team and still miss the playoffs due to scheduling, injuries, and players (especially pitchers) just flat-out having off years. For the sake of this article, we will be discussing a strategy that puts your team in the position to compete during a three-year window in order to maximize your opportunity for a championship, even with the variability of baseball.
Rule #2 | Acquire Ronald Acuña Jr.
The most important thing any fantasy baseball owner can do when approaching a dynasty league with the intention of winning the upcoming season is to acquire Ronald Acuña Jr., followed closely by cloning Ronald Acuña Jr. multiple times. Mike Trout is, obviously, an alternative, as both players are absolutely fantastic across the board. The reason to lean towards Acuña is that 2020 will be his age 22 season, so hopefully he is still on the upswing for his career and can improve his ratios as he gathers more experience, while Trout will be 28, which is hardly ancient, but is typically the start of a downward trend for players. If anyone can beat the aging curves, it’s Trout, but why not hop on board with the player most likely to be the next Trout? Acuña 2020 is basically Mike Trout 2013.
Rule #3 | Understand how players compare to each other
First, let’s look at z-scores. Z-scores are fundamentally how the well-known ESPN Player Rater tool works. Turning to Wikipedia for an official definition: “In statistics, the standard score is the signed fractional number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured.” Fundamentally, this is a value that tells not just whether Player A is better than the average player in the relevant categories, but by how much, and can be used to rank players based on overall statistical value. There’s a lot of statistical theory behind this concept, but that’s about as much as you need to know to use it in a fantasy baseball context.
One of the first things to do during the offseason as you begin to develop your strategy is to create a spreadsheet that pulls all players that played in 2019 (usually filtered to anyone with more than 10 PA to keep the spreadsheet from being too large). For example: Google Sheets.
Come January and February, projection systems such as Depth Charts and ZiPS are released, and this same methodology can be applied to projections as well as the previous season’s results. It’s a fantastic idea to set this up for multiple projection systems to get a more complete picture of the upcoming season’s player pool.
From this data, z-scores can be calculated for every player in each category relevant to your specific league or any category for which you may wish to have a statistical ranking of players. The formula to do so is simple:
Z-Score = ( [Player’s Total for Category] – [League Average for Category] ) / ( [Standard Deviation for Category] )
To put this in spreadsheet terms, if the player is in Row 2 and the category is in Column B:
Z-Score = (B2 – AVERAGE(B:B)) / STDEV(B:B)
That’s it. Apply the formula to every player and you now have not just a ranking of every player in that category, but a relative ranking showing how each player relates to the average for that category. You can then sum the z-scores for each category and achieve an overall player value. You no longer have to go with a gut feeling of whether Kyle Schwarber with 82 R, 92 RBI, 38 HR, and a .250 AVG has more value to your team than Starling Marte with 97 R, 82 RBI, 25 SB, and a .295 AVG, because you’ve quantified the overall value of each player’s production.
Note: With categories where a lower number is better, such as ERA or WHIP, reverse the player total and the league average in the numerator. ( League Average ERA – Player ERA ) / Standard Deviation of ERA
Another Note: You may want to adjust ratio categories with a PA or IP modifier, either multiplying the ratio by the z-score for the relevant “time played” category, or by multiplying by [Player PA] / [Average PA], for example.
There is value in doing this manually rather than just looking at ESPN’s Player Rater (especially if your league uses another hosting service). Players with 10 PA aren’t super relevant to fantasy baseball, so you may want to limit the population to players with 100+ PA or another number that you think is relevant to the player pool (rostered + likely free agent or waiver adds) in your league.
Additionally, while your league may use a standard category setup of R, RBI, SB, HR, and AVG (or another common setup with OBP or SLG), you may want to go deeper into player profile stats like BB%, ISO, BABIP, or K-BB%. This way, you can begin to understand how to compare players based on profile, rather than their simple fantasy category production which may have been a product of “good luck” or “bad luck.”
Once you’ve done this for the previous season’s numbers, do it for projections for the next season. Depth Charts, ZiPS, and Steamer from FanGraphs can all be used (even creating a custom blend, if that’s your thing). Google Sheets has a fantastic ability with =IMPORTHTML to directly pull in the projection tables.
Now you’ve got a solid base for comparing players to each other beyond the eye test.
Rule #4 | Understand your league
If your league is brand new, this complicates things. You may or may not know how the other owners in your league operate. Are there streamers? Are there owners who punt one category in order to try and dominate another? That’s something that only time can tell. However, you can still draw some basic conclusions about the relative value of categories.
- Hitting categories are less volatile than pitching.
For the most part, any fantasy-quality player in all but the deepest of leagues is going to be a fairly regular starter for an MLB team. That means 4+ starts per week and 4+ PA per start on average. Absent injuries or performance related issues, your best hitter is going to get a steady 25 to 30 PA in your Week 1 matchup, your Week 2 matchup, and so on. But your ace? Well, he’s probably going to get two starts in your Week 1 matchup, but just one in your Week 2 matchup, and from there on out, who knows? That makes projecting your IP for each matchup extremely difficult, and IP is going to have a massive impact on whether you can compete in both counting and ratio stats in a given week. When you get deeper into your roster, maybe your fourth OF is more of a platoon bat and faces a run of lefties that limits his playing time, but you should be accounting for that anyway.
- High total categories are less volatile than low total categories.
Categories like R and RBI will have far higher totals each week than SB and HR. Note that ratios are a different beast altogether, but do share some similarities when looking at the higher SLG versus the lower OBP or AVG. The impact of this is that it’s easier to build a team that consistently wins categories where the impact of a single increment is a smaller percentage of the total than one where it is larger. If a team averages 35 R per week, losing a single R decreases the total by less than 3%, while if a team averages 5 SB per week, losing a single SB decreases the total by 20%.
If your league is several years old, you now have historical data which you can use to guide your category prioritization. Getting back into statistical analysis, we are going to make use of the Pearson correlation coefficient. Again from Wikipedia: “The Pearson correlation coefficient is a measure of the linear correlation between two variables X … a value between +1 and −1, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation.” Examining this correlation for each category your league uses versus winning percentage can help determine which categories historically contribute to better teams. For example, consider a league which has existed in its current state since 2013. That gives seven years of data to analyze when setting up for the 2020 season. Historically in that league, R and RBI both sit at an approximate .70 correlation coefficient with winning percentage year after year, while SB is .57, making R and RBI more likely to contribute to a winning record than SB.
What this means is that when looking at two players with similar overall zTotals for 2019, such as Jonathan Villar at 12.27 and Cody Bellinger at 12.21, we can determine which player was more likely to help your team win your specific league. Using the example of the previously mentioned league, Bellinger had a zR of 2.80 and zRBI of 2.70, whereas Villar was at 2.49 zR and 1.36 zRBI. A ton of Villar’s value as a player comes in his SB, at 6.29 zSB, but SB aren’t as likely to impact your fantasy team’s winning percentage as R and RBI (don’t forget the variability inherent in lower weekly total categories). Even with a slightly lower zTotal, Bellinger is clearly the better player in this scenario. You can even multiply the correlation coefficient by the z-score as a simple modifier to build this into your analysis.
This isn’t a foolproof system, because of course some players exceed projections and others fall short, while other players get injured and some get traded or moved to a different lineup slot. However, the importance of this system is to help provide more tangible evidence for the potential value of a player to the specific type of team you need to build for your league. When you’re looking at the middle and lower rounds of any type of draft or auction, or even when you’re in a league with few players available and you need to make trades, you can start to directly compare the impact of players that appear nearly similar in production.
Of course, whatever any numbers say, get Ronald Acuña Jr.
Rule #5 | Bank wins
When analyzing your league and during the auction/draft process, try to get an understanding for how the other managers are approaching their team construction. It is entirely likely that trends will develop where the majority of managers are neglecting one of the categories, most often SV/HD or SB, but sometimes strikeouts or one of the hitting counting categories.
This opens the door for you to put your team in the position to have a “banked win” every week by stacking your team with players who excel in that category. Don’t go for complete one-trick ponies like Billy Hamilton, unless that player is filling one of your last roster spots and is someone you can afford to platoon, but if two players have similar zTotal scores, but one is stronger in the category you want to dominate, grab that player. Feel free to add a multiplying modifier to that category in your player ranking spreadsheet to get a clearer picture of which players will help you bank that category win. It’s a massive advantage to go into a matchup with an 85%+ chance at taking a category, as it limits the variability that can contribute to unexpected losses. You’re establishing a performance floor for your team, meaning even in a down week, which will absolutely happen, you’re likely to go 4-6 rather than 2-8.
Rule #6 | Do not punt any category
Simply put, you do not have the luxury of giving away any single category if you plan to win now. All that is doing is increasing the odds you will lose. You absolutely should prioritize categories, but based on projection models, you should strive to never have worse than 50/50 odds of taking any one category in a given week. In the same way that you want to raise your team’s performance floor, you never want to place a cap on your team’s performance ceiling, because running up the score against lower-tier teams is a fantastic way to grab a high seed (and the potential bye that comes with it) in the playoffs.
Rule #7 | Closers (or firemen in a SVHD league) are more important to you
Good general advice about relievers is to take the best reliever, regardless of current role. In a dynasty league, there is time/roster space for the best relievers to rise to the top and take the most important roles eventually (health willing). If you are trying to win now, however, you do not have as much time to wait for teams to realize who their best relievers are and use them accordingly. While relievers may only throw a couple innings each week, they’re a good way to provide a tailwind for your team’s pitching ratios while putting together a dominant bullpen can help bank a SV/SVHD win for your team. The best RP can even help smooth out your K totals or poach a W when your ace has just one start for the week. A completely reasonable week for someone like Josh Hader is 2.1 IP, 5 K, 0 ER, 0.75 WHIP, which is enough to turn a 6 IP, 4 K, 4 ER, 1.50 WHIP start from Mike Fiers from a major drag on your pitching for the week to tolerable.
For example, a few popular non-closing names that will be floating around in 2020 drafts are: Nick Anderson, James Karinchak, Andrés Muñoz, Colin Poche and Diego Castillo. While many of these names could possibly have value on your team if you are applying a reliever-heavy strategy (see: Delosh Betader), you should be going for elite closers now who likely have two or three years of longevity. This narrows down your list to:
- Josh Hader
- Roberto Osuna
- Aroldis Chapman
- Will Smith
- Taylor Rogers
- Liam Hendriks
Why not all-world Kirby Yates? Even if you are winning now, Yates will be 33 before the start of the 2020 season. There simply aren’t many (meaning any who aren’t currently in the Hall of Fame) elite closers 35 and over. Chapman is pushing it in the age department, but he’s been elite for so long, so there isn’t any reason to believe another two years is out of the question. Edwin Diaz probably should be on this list too, but after such a horrendous 2019 campaign, you won’t really have enough leeway to draft him as high as you’d probably need to.
Rule #8 | No more than a third of your lineup should be older than 31
Part of your draft strategy should be to scoop up older players at a discount in your draft, but don’t get carried away. Once players turn 31, it is a crapshoot as to how long they continue to be valuable. You need to mitigate how many players decline fast over the next three years. Catching players who are 28-30 should be your sweet spot, as it’s when age hasn’t taken too detrimental of a toll on production, but experience is likely to help the player break out. Other owners taking a 3-5 year view will likely be going all-in on players who are in their early-to-mid-20s, driving up their price and leaving fewer resources for, or even completely ignoring, the likes of Tommy Pham, Danny Santana, or Mike Moustakas.
Rule #9 | Stolen bases are more important to you (Controversial!)
Travis Sherer’s view:
Stolen bases are generally at odds with a win-now strategy because it is typically younger players that steal more bases. All the teams employing a win-now strategy in your league will likely be willing to pay a higher price than you would want to for younger players who steal bases. Additionally, much like saves, you just do not have the time to wait for that speedy guy (Myles Straw or Vidal Brujan) to finally get his shot … or just get traded.
With that said, there are a lot of well-rounded players out there who also steal bases. Those are the ones who get picked very high in drafts. That said, if you don’t get one of them (Trout, Acuña, Christian Yelich, Francisco Lindor, Trevor Story, Jose Ramirez, Trea Turner), it will be very hard to compete in this category. In order to compete in SBs on an annual basis, you must have a player who is capable of more than just stealing bases, unlike many of the known speedsters. (Jarrod Dyson, Elvis Andrus, Mallex Smith, Dee Gordon, Delino DeShields, etc.)
Once you get one of those multi-dimensional players, you should be taking gambles on base stealers like Whit Merrifield, Victor Robles, Yasiel Puig, Scott Kingery, and so on. Either players who have shown the glimpse to do more than steal, or have been one of those players in the past but are now declining. The reason you need one of these consistent base stealers is that, even for many of the best thieves on the basepaths, SBs can be fleeting. Take Acuña for example. Will he be stealing 25+ bases in three years? Probably not. Look at all of the players who went 30/30 since 2000 and what they’ve done three years later:
|1st 30/30 Season||Player||Age||30/30 HR||30/30 SB||3 Years Later HR||3 Years Later SB||# of 30/30 Seasons|
|2012||Mike Trout||20||30||49||41||11||1 (So Far)|
|2018||Jose Ramirez||25||39||34||??||??||1 (So Far)|
|2018||Mookie Betts||25||32||30||??||??||1 (So Far)|
|2018||Christian Yelich||26||44||30||??||??||1 (So Far)|
|2019||Ronald Acuña Jr||21||41||37||??||??||1 (So Far)|
There are several reasons why many of the 17 players above — some of the most valuable players of their time — were limited to just one 30/30 season, and it likely wasn’t talent. Stealing bases takes a toll on the body, and at least five of the names were ravaged by injuries. Another couple names (Rollins/Ellsbury) happened to have fluke power years and kept stealing bases. More generally though, outside of the sneaky and insanely talented Bobby Abreu and fantasy god Alfonso Soriano, if a player shows the kind of power Acuña has, he no longer hits leadoff. Instead, he’s now in the three or four hole, looking to drive in runs — not start rallies. That player also just doesn’t get as many opportunities to steal. Does that mean that Acuña won’t steal 40 bases next year? Yes. Does it mean he won’t steal 30? No, but the history doesn’t look good. More than half of the players on this list continued to steal 20-29 bases for a few years after posting their 30/30 season, but many also had sharp declines. For example, consider Mookie Betts, who stole 30 bases in 2018 versus just 16 in 2019, despite playing more games.
Ryan Fickes’ view:
Stolen bases are a high-variability category, in that winning totals in a given week can be anywhere from 2 or 3 to 10+. Additionally, even the best base-stealers in the league these days average perhaps 2-3 SB per week. Unfortunately, the production isn’t consistent and they’ll often come in clusters due to matchups, depending on how good an opposing battery is at controlling the running game, as well as if the base stealer is fully healthy in a given week or not. Based on trends I have observed, SB totals do not correlate well with fantasy success because of this variability.
However, if you notice that SB is a category the rest of your league is neglecting, go ahead and apply Rule #5 here. It may be a little harder to get SB into an 85%+ win situation, but 75%+ is entirely possible.
There really isn’t any such thing as a fully win-now strategy in any kind of league that stretches across multiple seasons, because even if you build a regular-season powerhouse, anything can happen against another quality team in a given week, so the playoffs are nothing more than a weighted roll of the dice at best.
That being said, there are ways to improve your odds beyond “get better players.” Try to develop a more holistic understanding of your league and the player pool available. Make decisions based on concrete metrics rather than gut feelings. If there’s one constant in fantasy baseball, it’s that any given player can get lucky in a season, but being the most informed person in your league is the way to ensuring that every season presents a “win-now” opportunity for your team.
And don’t forget to get Acuña.
Featured image by Justin Paradis (@freshmeatcommr on Twitter)
I have Acuna, Yelich and Mondesi, plus some cheap glue guys like Matt Chapman and Amed Rosario. The SP is a bunch older 3rd and 4th tier guys–Morton, Lynn, Darvish–and injury plays–Ohtani and Heaney. No RP at all. Is this promising for a win this year strategy?
You have followed Rule #2 perfectly, so that’s a great start.
You’re well on your way to following Rule #5 and #9 for SB, too. My problem with Mondesi is league dependent. Is your league AVG? Then he’s fine. Is it OBP and / or SLG? Then he’s a liability and you might be able to sell another owner on his potential and grab a stud for this year in return.
Your SP is in solid shape. It’s not going to dominate most likely (although the ceiling is really nice). You’ve got five decent to good guys and that’s hard to do in 2020.
Also important, what are your resources available for filling out the rest of your roster? Do you have a salary cap or draft positions? Can you grab a bullpen with what you have available? Can you fill out your lineup with some more R and RBI so that you’re closer to banking those as wins each week?
Thanks for the reply. It’s a 16 team draft league and most of my picks are tied up with the players and pitchers listed, plus a few eg Robles, and prospects, eg Roberts, Hampson, Royce Lewis. I have one pick in the 100-125 range and several in the last third of the draft. The prospects are trade bait if need be.
I have an offer of Patrick Corbin for Mondesi.
If you’re truly going all-in for 2020, then you would want to consider converting players like Roberts, Lewis, and probably Hampson to more current assets with their expected peaks in 2020 and 2021 instead of 2022 and beyond.
I kind of like the Corbin for Mondesi trade, although given the hype around Mondesi, I think you could get more. Based on R, RBI, SB, OBP, and SLG (the settings my long-term league uses), Mondesi is projected (via Depth Charts) as the 10th strongest player at 12.01 zTotal. However, 10.21 of that comes from just SB. That makes him a much more one dimensional player than anyone else in the Top 50. I have Corbin projected as the 14th SP, very strong in W and K, strong in ERA, and good in WHIP. Now, pitching is inherently more variable and risky, which is also why you might want to seek a bit more in the trade.
Thanks again. You have dissuaded me from an all in approach. I got this far by buying cheap and waiting. Acuna was a WW pickup, which will tell you how long I’ve had him. I have a bandaid on every position but CI, even if the bandaid is named Garrett Hampson.
The league is 6×6 with holds and saves as separate categories. Given that I can pick up one really good RP, what should I focus on in late rounds, filling out the bullpen?
One technical question. How do you choose the group of players for z scores? The good players are easy, since they have only one position. The good utility players can have four, as will marginal players. Who is in and who is out for computing mean and sd?
With HD and SV as separate categories, building a dominant bullpen is even more important. I’d get every last solid RP you can get your hands on in order to try and turn both categories into “banked” wins. I’m not sure what your roster size is, but I’d devote as much of your bench to RP as possible.
When I am determining my player population, especially when building it with projections like Depth Charts, I’ll typically use a projected IP minimum of 30 and projected PA minimum of 150. That being said, there’s certainly an argument for setting your population size to: [ Teams in League ] x [ Roster Slots ] * 1.25 to have a better idea of what players that actually have a chance of being rostered in your league look like. Then you can select the top [X] players for your population based on projected WAR or some such.
That’s a plan, and it makes sense. The first time I won this league I had only three good SP, though one was CY. I had two dominant closers and filled slots with good ratio RP. I also had Jacoby Ellsbury’s best season, so SB was strong.
Just an update. The daft has started. Prior to the draft I traded rights to Yu Darvish and Kevin Newman for Hector Neris, Josh Hader and draft compensation in 2021. I then used my first pick to draft Carlos Carrasco.
FWIW 238/480 players were kept