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Tuesday 20th Feb 2018

Don't look now but teams have played at least 100 games. That leaves fewer than 60 for most clubs. What we're going to do is look at the leaders in the standard hitting categories through May since that matches the time we have left this season. The reason for this is two-fold. First, you're reminded that there will be some surprises in the coming weeks - there always are. Second, you have a reasonable expectation what the best players may produce for the rest of the season so you can be practical and reasonable in your evaluations.


name AVG name HR name SB
1 Troy Tulowitzki 0.352 1 Nelson Cruz 20 1 Dee Gordon 34
2 Yasiel Puig 0.344 2 Edwin Encarnacion 18 2 Jose Altuve 20
3 Victor Martinez 0.340 3 Giancarlo Stanton 16 3 Billy Hamilton 20
4 Miguel Cabrera 0.332 4 Josh Donaldson 15 4 Eric Young  Jr. 17
5 Alexei Ramirez 0.329 5 Jose Abreu 15 5 Rajai Davis 16
6 Robinson Cano 0.327 6 Jose Bautista 14 6 Jacoby Ellsbury 15
7 Matt Adams 0.325 7 Troy Tulowitzki 14 7 Alcides Escobar 15
8 Angel Pagan 0.325 8 Albert Pujols 14 8 Ben Revere 15
9 Jonathan Lucroy 0.321 9 Victor Martinez 13 9 Brett Gardner 13
10 Chase Utley 0.320 10 Justin Upton 13 10 Elvis Andrus 13
11 Ryan Braun 0.320 11 Brandon Moss 13 11 Brian Dozier 12
12 Jose Altuve 0.318 12 Adrian Gonzalez 12 12 Starling Marte 12
13 Carlos Gomez 0.318 13 David Ortiz 12 13 Emilio Bonifacio 12
14 Alex Rios 0.317 14 Mark Reynolds 12 14 Carlos Gomez 11
15 Charlie Blackmon 0.317 15 Yasiel Puig 11 15 Alexei Ramirez 11
16 Giancarlo Stanton 0.316 16 Carlos Gomez 11 16 Desmond Jennings 11
17 A. J. Pollock 0.316 17 Mike Trout 11 17 Jonathan Villar 11
18 Nelson Cruz 0.315 18 Michael Morse 11 18 Angel Pagan 11
19 Jose Bautista 0.310 19 Brian Dozier 11 19 Everth Cabrera 11
20 Melky Cabrera 0.309 20 Paul Goldschmidt 10 20 Jose Reyes 11
name Runs name RBI
Josh Donaldson 48 1 Nelson Cruz 52
Troy Tulowitzki 45 2 Giancarlo Stanton 51
Brian Dozier 44 3 Miguel Cabrera 49
Jose Bautista 43 4 Edwin Encarnacion 48
Hunter Pence 42 5 Josh Donaldson 46
Paul Goldschmidt 41 6 Brandon Moss 46
Giancarlo Stanton 40 7 Jose Abreu 42
Nelson Cruz 39 8 Jose Bautista 40
Edwin Encarnacion 39 9 Yasiel Puig 40
Ian Kinsler 38 10 Michael Brantley 39
Melky Cabrera 37 11 Paul Goldschmidt 38
Matt Carpenter 37 12 Mike Trout 38
Christian Yelich 37 13 Michael Morse 38
Carlos Gomez 36 14 Troy Tulowitzki 37
Charlie Blackmon 36 15 Charlie Blackmon 37
Anthony Rendon 35 16 Adrian Gonzalez 37
Michael Brantley 34 17 Yoenis Cespedes 37
Freddie Freeman 34 18 Ryan Howard 37
Mike Trout 34 19 Alexei Ramirez 36
Daniel Murphy 33 20 Victor Martinez 34

When deciding between two players, the seemingly obvious question is "who do you expect to have a better rest-of-season. The problem with that is anything can happen over the last two months and while talent should win out, the choice is often the player you feel will play better, there are other considerations.

1. Playing time - This is my first filter at this time of the season. Any time I'm deciding between players anywhere close in potential, I'll choose the one I know will play more. This can be because they hit higher in the order. It can be because the team has no reasonable option, like a minor league prospect they want to audition. Opportunity trumps almost everything down the stretch.

2. Health - I just get done convincing myself to give Troy Tulowitzki nearly regular at bats the rest of the way and he reminds me why I hedged back in the spring. As will be discussed in a minute, upside is a consideration and some perennially injured players have significant upside, but the replacement pool for injured players is much weaker than it was in May. Be judicious with where you hang your hat.

3. Upside - Often, you're looking at two players where one has a higher floor but lower ceiling while the other has a higher ceiling but lower floor. Context is everything. if you're behind and need to make up ground, you may need the higher ceiling. If you're protecting a lead then the higher floor may be best.

4. Streaks - Riding the hot hand is fine, but not at the expense of a better player. There may be some new research that suggests streaks are real but regardless. what goes up must come down. Again - context - if you need to take a chance then riding the August/September version of Chris Colabello may be the play. But take a look above, where's Colabello? The talk of the early season didn't make the top-20 lists through May. So sure, play a hot guy and bench someone else but have a very short leash. Don't drop a better player for a hot player. Someone else in your league will pick up the dropped player and benefit while you're trolling the wire to replace the now cold player.

Good luck down the stretch. You've come this far; let's take it home.

Next week, Lawr and I will be in Las Vegas, at the Bellagio for the Fantasy Sports Trade Association’s (FSTA) winter meeting. I know, rough life. While neither of us are the biggest fans of Sin City, we manage to make it through the two-day event every year unscathed, in part due to our annual visit to the Carnegie Deli in the Mirage. In between bites of one of the biggest and best sandwiches known to mankind, we plan on talking strategy for the impending FSTA Expert’s Draft sponsored  (and covered) by Sirius XM though we usually end up talking about everything but baseball. Pastrami will do that to you.

This year’s trip will be special for two reasons. First, as Lawr documented last winter, he had a health scare that prevented him from traveling so we communicated via g-chat. It’s sure going to be nice to have his yang next to my yin in-person at the draft table, or as Lawr has so deftly named our team management style, Zen and Now will be back together. The other reason this trip will be special is when we’re interviewed by Sirius or prompted to give our pick, we’ll be referred to as defending champions. We can play the humble card all we want but we’d be lying if either of us said it’s not going to feel good being called champ.

The first opportunity for Zen and Now to brag a bit will be this coming Monday on Sirius SM Fantasy Sports Radio during The Drive, sometime between 5:00 PM ET and 7:00 PM ET.  The league sets the draft order via a lottery. A team is picked out of a hat and chooses their draft spot live on the air, usually providing a brief explanation. Last year we picked 12th out of 13 teams and tabbed Buster Posey as our first pick, causing a bit of a stir. We followed with Justin Upton, once again demonstrating you don’t win a league with your first couple of picks.

Lawr and I have briefly cyber-discussed from which spot we prefer to defend our title but haven’t finalized the plan yet. We’ll probably do so Sunday night after the football games have ended. Here’s my current thinking in terms of desired draft spot.

You can’t go wrong picking Mike Trout, Miguel Cabrera or Andrew McCutchen. I suspect we’ll agree that if a top-three spot is available to us, we’ll grab it in a heartbeat.

There’s a pretty good chance we’ll turn that into a top-four, adding Paul Goldschmidt to the mix. By the numbers he deserves to be there but even though his track record is the same length as Trout’s, there’s some uneasiness with Goldy that I don’t feel with Trout. But probably not enough to knock him from the four-hole.

Here’s where it gets a bit hairy as my choice for number five will be shared by some but not the majority as Jacoby Ellsbury is a love-him or hate-him type of player. I feel Ellsbury is accident-prone as opposed to injury prone thus expect enough playing time (and production) to warrant this pick. Others are leery of the health issues or strategically prefer to draft more power than speed early on. Unless Lawr agrees with my sentiments on Ellsbury, if the five spot is the first available at our turn, we’ll likely opt for a later pick.

If that’s the case, the question becomes how late. An advantage of picking early is the delta between players is greatest at the top (the decline is not linear) so you can gain an on-paper advantage with an early pick. Countering that is the possibility a higher ranked player falls to you towards the end of the round. Ellsbury is the perfect example. There’s a chance we slides into the double digits – a very good chance. So from my perspective, we may get a top-five player then get an early pick in the second round to pair with him.

What I like to do is decide how many players I would pick as early as possible (we’ll include Goldschmidt thus call it four) then determine how many other players I’d be satisfied with choosing in the first round. If the players are bunched together thus there is no discernible difference, I’ll pick as late as it takes to guarantee getting one.

After Ellsbury, the group I am comfortable drafting in the first includes Carlos Gonzalez, Adam Jones, Edwin Encarnacion, Carlos Gomez and Adrian Beltre. Note I said comfortable. Conspicuous by their absence are Robinson Cano, Chris Davis and Ryan Braun.

I favor reliability early even if that means sacrificing upside. Of the five names I mentioned as first round targets, Gomez may be questioned as the least reliable. I think his batting average is indeed risky but he’ll provide a treasure trove of counting stats. Still, there’s a chance he falls to the second so I’d eliminate him and set 9th as the latest spot from which I’d want to draft.

In this league, that means there’s a chance that we’ll be forced to pick 10th through 13th if our name isn’t taken early from the hat. If saddled with one of those spots we may get lucky and one of the top-nine may fall. There’s a very good chance Cano and Davis are taken ahead of us if we’re drafting 10th to 13th. In some leagues, Clayton Kershaw could go and he may go in this league.

This is the main reason Lawr and I talk more about other stuff than our strategy at the Carnegie Deli, we’re going to get a good player. Last year we literally chose Posey by default. We had a list of 11 names and had the 12th pick. We were sure someone else would sneak into the top 11. Wrong – so on the fly we ended up on Posey. And still won.

I’m still going to put some serious thought into both the draft spot and the possible targets because that’s what I do. But I’m not going to sweat our spot, regardless of where it is. I’d rather expend my energy savoring that pastrami sandwich, especially since I will have a brand new elliptical waiting for me to deflower when I return home. It’ll be the last time I spoil myself with pastrami for a long, long time.

You know, until Zen and Now are plotting our three-peat next year!

A few weeks back, I described a means to graph a draft by estimating the expected return per draft spot by averaging year end values for hitters and pitchers, grouping them together and blocking them off by rounds. Today I will present the actual data from 15-team mixed leagues and we’ll discuss some interesting repercussions. Here’s the data with the pick across the top and the round down the left side:

PICK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 DIFF AVE
Round 1 49 45 42 40 38 37 36 35 34 33 32 31 30 30 29 20 36
Round 2 29 29 28 28 28 28 27 27 27 27 26 26 26 25 25 4 27
Round 3 25 24 24 24 24 23 23 23 23 23 22 22 22 22 22 3 23
Round 4 22 22 22 21 21 21 21 20 20 20 20 20 20 20 20 2 21
Round 5 20 20 19 19 19 19 19 19 19 19 19 18 18 18 18 2 19
Round 6 18 18 18 18 18 18 18 17 17 17 17 17 17 17 17 1 17
Round 7 17 17 17 17 16 16 16 16 16 16 16 16 15 15 15 2 16
Round 8 15 15 15 15 15 15 15 14 14 14 14 14 14 13 13 2 14
Round 9 13 13 13 13 13 13 13 13 12 12 12 12 12 12 12 1 13
Round 10 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 1 11
Round 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 0 10
Round 12 10 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9
Round 13 9 9 9 8 8 8 8 8 8 8 8 8 8 8 8 1 8
Round 14 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 1 7
Round 15 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 1 6
Round 16 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 1 5
Round 17 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 1 5
Round 18 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0 4
Round 19 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 1 4
Round 20 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 1 3
Round 21 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2
Round 22 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
Round 23 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1

Also included is the difference between the first and last pick from each round and the average per round.

Note how steep the drop is in round 1 and how by round 4, everyone within the round is basically the same player according to APE. It’s also interesting to note how the descent of the average per round is also not linear; the slope is steeper at the top of the draft.

The first application of the above data is to realize that the number in each cell is the expected return for each pick. Look at how little return is needed at the end of the draft. Harkening back a few weeks, this is why I am completely confident at some point, you’ll find someone at every position that will at minimum break even with respect to their draft spot.

To help put things into perspective, if you take the typical year end standings, the champion of a 15-team league has the equivalent of $360-$380 worth of stats which is at least $100 more than the theoretical break even amount. In other words, the winner distributes at least $100 over and above the break-even return of investment numbers in the chart above.

This begs a very interesting question. Where is the best place to aim to get the maximum return on investment?

Think about this for a second. As incredible as Mike Trout’s season was last year, he only earned $45. Granted, it was almost all profit as most of his owner took him late, but if he were to repeat that season this year and earn the same $45, he would give you a negative return on investment if you take him #1 overall. While there are different degrees of expected regression, everyone agrees Trout’s numbers will fall (though some claim the added games will compensate). The point is, the risk far outweighs the reward when it comes to drafting Mike Trout #1 overall.

Due to the steepness of the slope in the first round, my personal objective is to shoot for the average, which is $36. In other words, I will choose the player I feel has the best chance of earning as close to $36 as possible. I am more concerned with less downside than I am with greater upside. I already need to find another $100 worth of stats in order to win. If I mess up the first round pick, that puts me even further in the hole. For those reason, I chose Prince Fielder as the sixth overall pick in mixed LABR. I’d rather lock in at least $30 and take a small $6 loss than risk an even bigger loss.

Personally, I feel the most efficient area to pick up the necessary $100 of profit is in the latter half of the draft, which is another reason why I don’t want to leave potential on the board by drafting for scarcity. For every dollar’s worth of stats you leave on the board, you need to add that onto the $100 you already need to find. If you take a chance in the 14th round and it fails, you’re out $7, which is a lot easier to make up than if you take a shot in the 2nd or 3rd round and put yourself $25 in the hole.

The title of this column is Chance Favors the Prepared Mind. One way to prepare for chance is to have a lower tier player at as many positions as possible so if someone were to emerge in season, you realize the maximum return of investment since they will be replacing a late round player not expected to earn more than a handful of bucks. If you put draft your middle infielder in the 12th round and you have Steve Lombardozzi on reserve and he becomes the second baseman for Washington and earns $15, you pick up a rather modest $6 of profit if you replace the 12th rounder. But if you put him in place of a guy you drafted in the 21st round, you realize a more useful $13 of profit. Having a few multiple eligibility players helps in this effort as well.

In short, the goal of your draft should be to set yourself up to pick up the necessary $100 worth of profit. Some of that is put on your roster via superior player analysis and game theory. Some of it is clever managing in season via free agent pickups and reserve management. Using the chart above can help frame your game plan to use your assets in the most efficient manner.

Next week, we’ll talk about the game theory aspect of the puzzle and the means I use to put more stats on my team (at least I hope) than my competitors.

As many of you know, I contribute content for ESPN Fantasy Baseball. I was recently tasked with generating my top-250 starting from May 16 through the end of the season. What’s done is done. The instructions were to rank performance going forward. My rankings, to put it kindly, caused quite a stir. So much so, in fact, that a reader felt compelled to honor me with a troll Twitter account dedicated to my stupidity.

Interspersed within the comments was the typical Pee Wee Herman rhetoric. Apparently I’m an idiot, a moron, stealing money from ESPN and would be welcomed into a slew of leagues. None of this bothers me. I’ve been called worse and completely understand what comes with the territory. However, there were a couple of insinuations that did get under my skin a bit.

There were several references suggesting my rankings were nothing more than a publicity stunt to draw attention to myself, perhaps in an effort to increase hits for my Insider columns. I was accused of pulling names out of a hat or using a random number generator to come up with my rankings. The irony here is other than maybe my colleague Tristan H. Cockcroft’s, mine were the only set completely formulaic, spreadsheet driven projections. I guarantee mine were the least subjective of the lot. This is not to say my way is right and the others were wrong, I’m simply pointing out the irony, and fallacy of the accusations.

Since the system I used to generate the ESPN rankings is the same engine I use to produce the Mastersball Platinum rest-of-season projection updates, I thought I’d kill the proverbial two birds with one stone and reiterate my philosophy with respect to projections. Platinum subscribers will be receiving a bit more detailing the actual procedure, but since I haven’t done this in awhile, this is a perfect time to wax poetic on my philosophy while briefly introducing the new means I am employing to compute in season projections.

The most important aspect of this discussion is to understand the true nature, meaning and application, of a projection. A projection is a weighted average of a set of logical outcomes. While conventionally a projection is offered as a static number, it is in truth a range. Projections are best thought of as a bell curve with the poorer outcomes to the left and the favorable outcomes to the right. What we call the projection is the apex of the curve. By focusing on a static entity, we often lose sight of the fact a bad year is really just an outcome to the left of the apex while a good year is to the right. Both are within the range of possible outcomes. But yet, if those of us in the business of prognostication say Prince Fielder will hit 35 homers and he hit 29, we were wrong. It’s not that Fielder ended up within the lower end of his range of possible outcomes. We were wrong.

Here’s where my philosophy isn’t universally shared and is annually called into question on our message forum. For me, a projection is completely objective. The secret sauce fueling my projection engine is 100 percent numbers driven. What’s good for the goose is good for the gander.

In order to abide by such a philosophy certain traits are necessary. You have to be disciplined. You need to be conscientious so the secret sauce is always reflective of the most current research. You have to be obstinate. But perhaps most importantly, you need to have incredibly thick skin so you can accept being wrong.

This may seem counter-intuitive and downright ridiculous if you don’t truly understand projection theory. The objective is not to be right (which is what the masses shoot for). Trying to be right introduces the subjective bias that I avoid. The goal is to identify the most probable outcome. This is the ultimate irony to some of the comments on my ESPN rankings. Because I did not follow the herd on several players, the interpretation was I was being too cute in an effort to delineate myself and be able to say I was right. Whereas, the reality is that’s where my completely objective spreadsheet said to rank the player.

By subjective bias, I am referring to the act of treating two players with a similar trait differently. How many times have you heard a player is in store for a good year because of a solid second half or even a great September? My response is to pick out another player with a strong second half and ask why they aren’t being afforded the same credit. What’s good for the goose is good for the gander. Either everyone gets a bump for a better second half or no one does. And if this is the case, the criteria is no longer subjective, but objective.

In an effort to be right, many project numbers to the left or right of the apex. To be clear, this is not the same as betting on the come, and purposely drafting or buying the upside of a player. I’ll do that all the time. I am speaking of subjectively projecting a player to do better or worse that what the number say for one reason or another.

This is where things get hairy and is exactly akin to the old school versus new age scouting conundrum. There was a time each spring where my cell phone would ring and someone who’s opinion I trust that may or may not have founded this site, moved on to work at ESPN and is now a professional scout would be on the other end, sharing a tidbit about a guy with a new pitch or a reworked swing to generate more power. I’d like to think I’m good, but I don’t have a way to work that into my secret sauce other than to subjectively change a strikeout rate or HR/FB ratio, etc.  And, we all do that. It’s just that some use less salient information all the way up to the extreme of a whim.

Here’s an interesting way to think about it. I’m going to roll a pair of dice 36 times. Based on probability, here is the range of likely outcomes:

  • 2 and 12 – 1 time
  • 3 and 11 – 2 times
  • 4 and 10 – 3 times
  • 5 and 9 – 4 times
  • 6 and 8 – 5 times
  • 7 -6 times
  • That's actually what a projection should look like.

    What if I were to say I am going to do a single roll and ask you to predict the outcome, what would you say? Objectively, you should say seven. Anything else is subjective. The analogy is far from perfect, but some projections will choose a number other than seven in an attempt to be right. I’m only going to do that if I know for a fact one of the dice is loaded. And even then, I’m going to feel dirty afterwards and apologize profusely to my spreadsheet for overriding it.

    With that as a backdrop I’d like to share a Cliff Note’s version of how I generated the in-season projections used in the ESPN rankings as well as for the Platinum subscribers. But first, a nutshell review of the general process is necessary. I’ll focus on hitters; the same principle applies to pitching

    Everything is skills based using the plate appearance (PA) as a foundation. Using BB/PA the number of walks is determined. Similarly, HR/PA yields the number of home runs while K/PA renders the number of strikeouts. Subtracting walks from PA leaves at bats. We already know how many of these AB are HR and whiffs. Using BABIP, the number of non-home run hits can be computed. These can be separated into singles, doubles and triples based on history. We now have almost everything except RBI, runs and SB. I have proprietary formulas that produce these stats based on team tendencies, batting order, etc. We now have our projection.

    I use the exact same means to generate the in-season projection. The trick is adjusting the skills based on the limited sample as well as fleshing out the luck element, particularly with respect to BABIP and HR/FB. But even those entail a skill element so that what is not skill is luck.

    I’ll spare the details, but there is some very interesting work out there with respect to when certain skills stabilize. In fact, very recently this work has been updated so the soon to be discussed regression is better defined. To give credit to where it is due, I am referring to the work of Russell Carleton (Pizza Cutter) and Tom Tango (Tangotiger). They’re both well respected within the SABR community. A Google search will avail the work to which I refer.

    What I do is use the skills stabilization data to regress the current skill level to the historical level. Let’s say one of the aforementioned skills showed 50 percent stability at 300 plate appearances. This means at 300 plate appearances, there is a 50 percent chance the current level is real. So when the player has reached 300 plate appearances, his new skill is an average of current and originally projected.

    Anything fewer than 300 plate appearances is treated linearly, even though the relationship is not truly linear. I just don’t have the ability to program the non-linear relationships into my engine. The difference is going to be minimal; regressing in the linear manner does the job just fine. Keeping with this example, after 100 plate appearances, the current portion of the weighted average would be 50 percent times 100/300 or 16.67 percent leaving 83.33 percent as what I projected coming into the season. I regress all the above skills in this manner and plug them into the black box to generate the new projections, which obviously also encompasses my admittedly subjective estimate of playing time.

    Different skills stabilize at different rates. What’s getting me into trouble over at the World Wide Leader is contact rate stabilizes very quickly and since Jay Bruce has opened the season by fanning at an elevated rate, this is captured by my engine and reflected in a low average resulting in a ranking being ridiculed left and right. Jay Bruce has a history – he’ll end up right where he always does and I’m an idiot for saying otherwise. Well, in another life I was a scientist and we’re trained to believe facts generated by research as opposed to intuition and the fact is it is probable that Jay Bruce will fan more than usual so once his lucky BABIP corrects, his average from here on out will be poor. Again, this is the most likely outcome based on current projection theory. Bruce may very well finish to the right of the apex with an outcome better than what I sent to ESPN. But I didn’t put him so low on a whim. I incorporated what I believe is the most current data germane to the analysis. And I stand by that result.

    It’s funny, the exact same analysis is saying Chris Davis will not revert to his historical strikeout rate of over 30 percent and has improved to still subpar, but more acceptable 25 percent. This has yielded a rest-of-season batting average much higher than orignally expected, but yet, no one is being chided for jumping Davis way up in the rankings.

    What's good for the goose is good for the gander. I’m perfectly fine if I end up with goose egg all over my face come September when Jay Bruce is hitting .260 with his usual 30 HR.

    After taking a break from my series discussing APE (ADP Principles of Equivalence), it’s time to jump back in and address what I deem to be the proper use of average draft position listings. Recall a couple of weeks ago, I showed that APE suggests that there are a multitude of equivalent players at each draft position. By means of a brief review, players within $2 of projected earnings are fundamentally the same player. If you take away a homer, run, RBI and steal from the higher ranked player and give it to the lower ranked one, their projected earnings are the same. APE is based on the empirical discovery that if you take the round where the player is ranked and multiply by three, that many players above and below the player are within the magical $2 limit. If a player has an ADP of 100 in a 15 team draft, this puts him at pick 7.10. So you take seven times three and 21 players above and below that player are worth the same. This means the player with the DP of 121 is just as viable a pick as player 100. As you proceed down the snake, this distance grows. The result is that it is not nearly as egregious to jump the ADP as some contend. The notion of taking a player too early is squashed as is the perception of a value pick, a player whose ADP is well before the actual pick. The full treatment is available for review HERE.

    With that as a backdrop, there is a very viable purpose of an ADP list, so long as you understand exactly what it represents. In short, the ADP is the market value of the players. Similarly, what most are willing to pay for a player in an auction is the market value of the player. It has nothing to do with his intrinsic potential to your team, which is the key.

    The intrinsic potential is how much the player contributes to your team’s ability to win. It is dependent on your team construct and your strategy. Different players may have different intrinsic potentials to different teams. It is your job as a fantasy owner to put as much potential on your team as possible.

    One way to do this is be better in tune with the player pool. Another is to know the market value of the players so on occasion, you can utilize this to wait on a player with greater intrinsic potential because his market value strongly suggests he will be there in a later round. This allows you to first take another player with a lot of intrinsic potential, but whose market value suggests won’t be available next time around. You need to be careful when doing this and it’s not likely you can play this game with every pick, but you can squeeze an extra player or two onto your roster by knowing the market value of the players.

    To give credit where credit is due, this concept was originally crystallized by KJ Duke, a very successful high-stakes player in a forum discussion from a couple of years ago. By day, KJ is also a very successful portfolio manager and compared buying stocks with the greatest intrinsic potential at the lowest market value to assembling a fantasy squad.

    Another use of an ADP could be to devise a general strategy in concert with tiered rankings. We’ll talk more about tiered rankings down the line, but the idea is to find pockets of players with similar intrinsic potential and see where they are likely to be drafted. If you pencil in taking a player at that position around that time, you can better decide what players or positions to take earlier. Again, we’ll talk more about this in future columns.

    Today’s message is short and sweet. Some live and die by ADP and feel it is the most accurate ranking of players. Others want to make a point and proclaim the ADP as useless. All that matters is what you think. As is often the case, the truth lies in between. ADP is a tool that if used properly, can assist in constructing your team in an optimal manner - nothing more, nothing less. To follow it blindly is a mistake. But so is categorically ignoring it.

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