Introducing the Prospect Scorecard

I’ve recently completed two deep Ottoneu minor league drafts and am reminded just how difficult successful “prospecting” can be.  Regardless of how informed you may feel heading into an MiLB draft, you’re often left selecting players based upon small sample sizes, second hand reports, maybe a few brief videos and, as much as we want to deny it, a bias towards “scouting the stat lines”.  At the end of the day you’re making a decision using limited information that could have serious long term consequences for your dynasty team.

Prospecting is actually a lot like another critical business skill: hiring good employees.  The inputs might be a little different (scouting reports might be job references, batting practice videos might be one hour interviews, and stats, however limited, might be the resume sitting on your desk), but the challenge is the same: make an important, timely decision based on limited information, first impressions, and gut feel.

There’s certainly more at stake hiring future employees than there is with building fake baseball rosters, but the analogy works.  Consider this statement on hiring:

However, when it comes to interviewing, many of us have biases that cause us to not even realize how biased we are. When a candidate ends up being successful, many people in the organization believe and claim that they spotted her or his talent early on. And when a candidate does not succeed, suddenly it seems that the candidate was hired despite widespread doubts. To paraphrase an oft-repeated saying, success has many fathers, but failure is an orphan. Selective memory therefore makes it hard for us to accurately recall our impressions of candidates at the time we interviewed them, which in turn makes it hard for us to learn about our biases and to have an accurate assessment of how skilled we are as interviewers.

These biases ring true for “prospecting” in fantasy too, don’t they? Well, the good news is that, as this excellent HBR article suggests, we can begin incorporating some quantitative processes to help create a prospect “scorecard” designed to more accurately match player projections to our specific fantasy formats.  That’s what we’ll do today.

I’ve identified five criteria that are essential in identifying the “best fit” prospect for your roster.  I’ve also assigned a scale of 1-5 to each, where “5” represents the most positive rating, and the highest overall score represents the prospect best suited for your team.  The goal here is to compare prospects across these criteria in a way that results in a less biased, more quantifiable evaluation that you can use to maximize value for your dynasty team.  These five criteria are:

Scouting (1-5)

“Scouting” is everything that goes into evaluating the true talent of an MLB prospect.  Age, ability, stats, rankings, “makeup”, and Eric’s excellent scouting reports all play a role here.  It’s the input of information that causes you to ask about the player’s ceiling, their floor, and what might be realistic in between.  What are the risks, and how serious are they? Is this prospect regarded more for their defensive talents than offensive? What MLB players might they compare to?

Scoring (1-5)

“Scoring” is honestly assessing whether the prospect’s skills and talents effectively translate to the specific scoring format of your fantasy league.  Maybe this sounds obvious, but I continually see owners fail to make this connection in the way they draft their fantasy teams each season.  While Billy Hamilton might be an exciting buy in a 5 x 5 auction, he might not even be rosterable in something like Ottoneu’s FanGraph Points format, which is based on linear weights.  In order to be more successful in building our dynasty rosters, we need to always “scout” within the context of our scoring format, which is what this rating is designed to do.

Impact (1-5)

“Impact” is more than estimating a reasonable MLB ETA for your prospect.  While isolating a realistic debut is helpful, it is far more important to give some thought as to when your prospect may actually contribute (in a significant way) to your team within the context of your fantasy format.  Do you expect your prospect to be an above-replacement level contributor for you during the season in which you’re built to win? If not, you might be better served trading them away for MLB players that are already at peak performance.  “Will my prospect help me when I need them most?” That’s what we’re getting at when rating “impact”.

Hype (1-5)

“Hype” is a measure of perceived prospect value within the industry (usually in the form of rankings or press) but especially within your own league.  It has to do with the trend of a prospect’s value over time.  Is it growing, like Nomar Mazara, or is it waning after injury or a “disappointing” debut like Joey Gallo? The careers of both these young players could go in any direction at this point, but perceived value is just as important as real fantasy contribution value.  “Hype” is an attempt to measure the demand for this prospect within your league, which usually takes the form of constant trade interest.

Cost (1-5)

“Cost” is the price at which a player is owned, both in the present and the future, and it can never be overlooked.  Cost is always a variable in fantasy prospect valuation.  Always.

In keeper leagues, cost is usually a function of the draft round required to keep a player, or in auction leagues like Ottoneu, the salary point at which you own them for future seasons given a roster budget.  Again, perception is reality within your league, as there may be a substantially different reaction (demand) to a $10 Blake Snell compared to a $5 Blake Snell.  For the purposes of our prospect scorecard, a lower number here represents a higher cost.

So, attempting to improve our fantasy “prospecting” process by quantifying our valuations, we have our base criteria for our scorecard: Scouting, Scoring, Impact, Hype, and Cost.  Let’s see how it works.

Fantasy Baseball Prospect Scorecard

Austin Meadows, Aaron Judge, David Dahl, Brett Phillips, Max Kepler – here are five OF prospects I’ve come across this spring that for whatever reason all seem somewhat similar in my mind.  Who do I choose? Which player is best for my team, given the option to draft one of them? The Prospect Scorecard offers guidance.

I’ve rated each prospect according to the criteria above on a scale of 1-5 with 0.5 increments.  These are my ratings, based on my impressions, information, and evaluations, so while we’re never able to weed out all the bias, it’s important to understand that the process is just as important as the result.

For this particular example, I’m comparing these prospects within an Ottoneu league with deep 40 man rosters and the FanGraphs scoring system that is heavily scaled towards wOBA.  I’ll walk you through a few of my own thoughts in the process:

  • Austin Meadows “Scouts” the best, with a strong hit tool and good chance for solid OBP rates as he nears MLB, which is why you’ll likely see him closer to the top of prospect lists than the rest of these outfielders.  Meadows offers perhaps the safest profile with the highest floor of the bunch.
  • Aaron Judge has no comparison here in the power department, which plays perfectly in Ottoneu points leagues, so he gets the highest rank in “Scoring”.
  • Max Kepler actually gets the bump here in the “Impact” category simply because he may be the closest to being ready to contributing in 2016 in a meaningful way.  Meadows has no real place to play in that excellent PIT outfield, Judge may have early contact issues that prevent him from getting to his power consistently when I need it, and neither David Dahl nor Brett Phillips are likely to push for an MLB role before September, if not 2017.
  • David Dahl gets a slight penalty from me in the “Hype” category, but probably by no fault of his own.  His injuries last year have depressed is value somewhat in my leagues and the reality that he’s probably more of a 15 HR guy at maximum instead of 20+ as he was being described just 18 months ago by many publications.  Because prospects are a hot commodity in many Ottoneu leagues, I always want to be holding the most attractive player if given the option.
  • Meadows and Judge are consistently costing a few dollars more in Ottoneu auctions compared to the rest of the group (largely do to the “Scouting” impact), but all these guys are pretty close, which is one of the reasons why they make a great test group for the scorecard approach.

Scorecard conclusion: While I may like one or two of these guys a bit more, the takeaway for me is that Max Kepler might actually represent the best prospect for my roster, and he might also represent a slight bargain heading into 2016 considering his excellent OBP skills and the way they are likely to translate quickly in Ottoneu in a season in which my team appears to be a contender.

The Prospect Scorecard is not a finished product.  As I said earlier, this is just as much about process as it is about the result.  It’s hopefully a more thorough way of evaluating prospects within the specific context of your dynasty league that helps you maximize the value of what you’re scouting/drafting/buying.  Is this process perfect? Absolutely not, and I’m sure your comments can help push this exercise into an even better place.

Which prospects are you considering for your upcoming dynasty prospect drafts? Is the Prospect Scorecard a tool you can use to help “hire” a better future for your team?

You can find a version of the Prospect Scorecard here.  Just save your own copy and use it to run your own prospect comparisons.

Trey is a 20+ year fantasy veteran and an early adopter of Ottoneu fantasy sports. He currently administers the Ottoneu community, a network of ~1,200 fantasy baseball and football fans talking sports daily. More resources here:

Newest Most Voted
Inline Feedbacks
View all comments
8 years ago

Looks like you need a section for “Injury” – Meadows has an orbital bone fracture that’ll cut into his development time.

Will Hannonmember
8 years ago
Reply to  O'Kieboomer

I would assume that falls into figuring the “Impact” value.