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Anyone else out there have a theme song when they draft their baseball teams?

I draft hard (he drafts hard) every day of my life
I draft ’til I ache in my bones
At the end (at the end of the day)
I take home my hard-earned team all on my own
I get down on my knees
And I start to pray
‘Til the tears run down from my eyes
Lord, somebody (somebody), ooh somebody
Can anybody find me… ADP to love?

Just me? Alright.

Please, blog, may I have some more?

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See all of today’s starting lineups

# MLB Starting Lineups For Sun 4/28
ARI | ATL | BAL | BOS | CHC | CHW | CIN | CLE | COL | DET | HOU | KC | LAA | LAD | MIA | MIL | MIN | NYM | NYY | OAK | PHI | PIT | SD | SEA | SF | STL | TB | TEX | TOR | WSH

Last week, I introduced the goal of this series: utilizing data visualization to try and narrow in on fantasy baseball insights. We looked at ERA across the draft, finding some potential values based on ADP. Today, we’ll take a closer look at Starting Pitcher WHIP by ADP.

To begin with, what’s the context in which we should gauge whether an SP’s WHIP actually helps our team? Here are WHIP trends over the last 5 years:

Please, blog, may I have some more?

There is a LOT of information available for fantasy owners to try and digest these days. New writers and podcasts emerge every day (over 500 different fantasy analysts by last count). New stats and ways of slicing and dicing existing data are constantly emerging. Don’t get me wrong – I love the latest Statcast research as much as the next guy. But fantasy writers often pile up the acronyms and exotic statistics, as if 2000 words on spin rate has inherent interest just because it’s in-depth. It can be hard to find actionable fantasy moves in a table with 10 varying components of xStats.

I’m kicking off a new series today, utilizing data visualization to try and narrow in on fantasy baseball insights. Good visualization helps you achieve your goals by channeling success onto your subconscious until your reality lines up with your drea….I’ve been watching too much late-night Tony Robbins. Good data visualization takes complex raw data and translates it into easily-understood, actionable images.

Please, blog, may I have some more?

As I type this, I’m in a small, but expected depression as a disgruntled Mets fan living in Minnesota after today’s non-waiver trade deadline. In the middle of thermal packaging related activities, I saw deal and deal and deal swing by. All I get from both the teams that I follow most? A Kurt Suzuki extension. Oye. All that did was disgruntle me more, as I like Josmil Pinto quite a bit. I figured at least Bartolo Colon would get traded for some PTBL or a BoB (bucket o’ balls). Ah well.

On the other hand, if you’re a Tigers fan (I’m not a bandwagon A’s fan until the Mets are good, I decided today), you must be pretty excited. Drew Smyly wasn’t as dominating as a starter and Austin Jackson continued to short-come expectations. Instead you have an second ace, and can now appropriately consider Justin Verlander your number 3 or 4 or 5. [Jay’s Note: Or playoff closer?] Verlander has not been good, but he’s also been almost as unlucky as he’s been bad, or he’s hurt and isn’t saying anything/doesn’t know it.

July 1st, I noted the luckiest pitchers to date, but the one thing I didn’t do at that time was look at the pitcher’s luck/bad luck relative to their career rates. So for this post, for luck, I z-scored each pitcher’s luck stats relative to their career stats (homerun to flyball ratio, left on base% and BABIP). I weighed each z-score by the stats correlation to ERA. Therefore the luckies pitchers (using luck alone and excluding skill) as of 7/27 is: Josh Beckett, Jake Arrieta, Collin McHugh, Scott Kazmir, Garrett Richards, Zach Britton, Jordan Lyles, Drew Pomeranz, Dellin Betances, Alfredo Simon and Danny Duffy. Chris Young, Jason Hammel and Jesse Chavez (update: both Hammel and Chavez were rocked in their last start). However, this all excludes skill (contact rate, strikeout% minus walk% and ground ball to flyball ratio). Incorporating this, here are the actual luckiest pitchers as of 7/27:

Please, blog, may I have some more?

As I write this, I’m on a plane. I knew I wouldn’t have internet, so I asked myself what data could I pull and play with to help you play with your team. Let me play guarantee fairy again… I’m supposed to be writing about Deep Impact. I guarantee you can use this list to trade away pitchers that are over-performing for long term deep impact while targeting other pitchers that can provide you with more short-term value. Use the comments section below and I’ll scold or virtual high-five your trade offers.

Please, blog, may I have some more?