Chicks Dig The Long Ball

Last weekend, I traveled a few hours north to our Nation’s Capital to take part in the DARF (DC Area Roto Fantasy) 2023 live draft.  DARF is part of EARTH Fantasy Baseball – The Local Area Fantasy Baseball League Competition.

For those unfamiliar with the EARTH competition, it is a collection of regional fantasy baseball leagues across the US and one in Canada.  Patterned after the NFBC Main Event, EARTH is unique in that participants compete to win their league and the leagues collectively compete against one another to win the overall EARTH Championship.  Participants include top analysts, industry contributors, and average joes from the league area.  There are two main stipulations to be part of EARTH: live drafts and donations to charity.  This is my first year in the competition and I couldn’t be more excited to be part of this great group.  Follow along all year at @EarthFantasyBB and root on your favorite DARF competitor!

The getaway to DC allowed me to spend the weekend with a close friend and fellow baseball junky.  Between the DARF draft, plentiful libations, and enjoying the World Baseball Classic (WBC) until the wee hours of the morning, my buddy (let’s call him, Pat) and I immersed ourselves in baseball talk.  Pat is a huge Phillies fan so naturally, we spent a fair amount of time critiquing their offseason moves and the NL East as a whole.  While I’m more of a fantasy baseball player, Pat prefers DFS, prop bets, and his annual HR Derby pool.  In between draft discussions centered around my recent experiences taking part in the @TGFBI and RazzSlam drafts, we also discussed strategies for tackling his HR Derby.

The HR Derby format is straightforward: 18 participants, 7 rounds (top 5 HR producers are scored), highest HR total wins.  So, Pat’s goal is to identify the top 126 HR hitters this season and be ready to pick the best one at his draft slot.

The HR Derby is not new to Pat, and he’s achieved great success in that competition over the years.  He certainly doesn’t need my advice to succeed.  During our discussions though, he presented me with a dilemma he’s been chewing on, allowing us to bounce ideas off one another.  The question, “How best to compare/contract HR projections from multiple resources, all of which you trust?”  Pat stated he always finds it easy to draft HR hitters in the early rounds of the Derby draft, but he wants to better identify potential HRs in the latter rounds.

This dilemma isn’t unique to HR Derby competitions, as all of us Roto and H2H fantasy leaguers are also presented with the question of what to do with multiple projections and finding value later in drafts.  When faced with this dilemma before our drafts, many of us simply select the resource we like best and run with it.  Perhaps others will average the projections, projected HRs for example, and use that value in developing our tiers.  Some of us even go a step further to make our own tweaks to the data here and there.  Traditionally, Pat has followed similar approaches and achieved success but wanted to think “outside the box” this year.

So, with the WBC on the tube and enough liquid nourishment to fuel our mental gymnastics, we put our heads together and came up with a “new” way for him to assimilate the resources and hopefully find an “edge” to help him win.

The results of our discussions will be the focus of our Analytics Anonymous session this week.

THE EXERCISE

The most simple and straightforward approach would be to average the projections for each player.  We quickly passed on that approach for three reasons, (1) too easy (remember the old saying, “if it’s easy, everyone would do it”) and won’t provide unique insight, (2) it does a poor job at differentiating players, and (3) if one of the three projections was significantly higher or lower than the others, it could artificially skew that player outside his proper draft window.

To illustrate these points using current projections from Pat’s three sources, let’s look at these six players:

The first thing you’ll note is the average HR for each player, across the three sets of projections, is exactly 30.

If we rely solely on the first resource, we’d select Player D.  With the second, Player F is the clear choice.  With the third, we’ll be picking between Player A and C.

As you can see, taking the average is not particularly helpful here.  Although certainly easy to pull off, it clearly does not differentiate the 6 players. And in one case, the HR projection is noticeably skewed relative to the other two.  Remember, we “trust” all three projections.

We need to find a way to differentiate the players to draft the one with the most potential to achieve, or better yet, surpass this mark.

We settled on an approach that normalizes each set of projections individually before combining to generate a standardized list.  Author’s note: this approach really isn’t “new” to assimilating data but provided Pat a “new” way to look at his HR projections.

In this exercise, the first task is to rank each player’s HR total within the same set of projections (i.e., Resource 1, Resource 2, etc.).  The full list of common players in each of the three projections is 516.  So, the result is to assign a rank from 1 to 516 to each player.  Those having the same number of HRs given the same rank.  Here is what that looked like for our six players in each set of projections:

The next task is to add up the respective ranks:

Since the lowest number is theoretically better, we see our 3 trusted resources collectively project the most confidence in Player A to reach an average of 30 HRs.

Remember Player F with the 36 projected HRs from resource 2?  It was easy to tell in the first table that projection was an outlier.  However, this exercise allows us to see what that outlier looks like in proportion to the other players.

Again, we’re not splitting the atom here.  This exercise just allows us to analyze data in a way to help stratify the players projected HR totals.

Can we do more?  Absolutely.

ADDITIONAL ANALYSIS:

So far, we’ve just stratified the players in a way that normalizes their projected HRs from multiple sources.  This helps differentiate players with similar HR averages, but we can easily add other analytics to help our decision-making.

Some of the analytics that may be useful for a HR Derby include ISO (Isolated Power), BRL% (Barrels per Batted Ball Events), LA (Launch Angle), EV (Exit Velocity) and HR/AB (Home Runs per At Bats).

Putting this methodology together as a whole, it looks like this:

NOTE: The highlighted players represent Players A-F, respectively, in our example.

For illustration, I added the average HR/AB calculation, derived from each resource, as an additional metric to consider.  Remember, use any (or all) additional data points to help you make your decision.

In this case, the HR/AB calculation may be useful in the situation above when you’re deciding between Eloy Jimenez and Paul Goldschmidt, both of whom ranked 17^th in the stratified HR projections.  You’ll note Eloy is projected to hit HRs at a slightly higher rate to Goldy.  So, if you have a hunch that Eloy gets more ABs than projected, maybe you’re taking him and essentially betting the over.

SUMMARY

The exercise presented here is intended to provide an alternative method to evaluate multiple data sources.  Despite being fueled by an abundance of liquid nourishment and lack of sleep (everything sounds brilliant then, right?), it still seems like a logical approach several days later, so I’ll stand by it.  Perhaps it’ll be useful to you as well.  Remember, we’re just looking for an edge against our competitors.

One of the data sources included in this assessment comes from Razzball and I recommend you include those in your analysis as well.  Visit the Razzball rankings page (https://razzball.com/2023-fantasy-baseball-rankings/) for everything you need to prepare for upcoming fantasy baseball drafts.

Do you find this useful?  A load of crap?  Either way, I always enjoy your comments.  Don’t be shy!

Lastly, you can follow me on Twitter: @Derek_Favret.

Until next time, my friends!