Digging for Gold: Analyzing Data for Undervalued Players - Second Base

My last post was dedicated to finding an undervalued player at the first base position and how I showed data on Max Muncy was something that could and can be replicated.  Somehow people are afraid to rank him high on their lists due to his relatively small sample size.  I am personally going to wait and draft him later when most of the usual suspects are gone and then reap the rewards later.  Hopefully this next batch of players at the keystone can help you too!

Time for our blind analysis: Player A vs. Player B. 
*Side note - Player B's data is from 2017 due to injury last year, being traded, lack of rhythm.  I'll explain more on this.


BB%K%Z-Swing%O-Swing%SwStk%LA (degrees)EV 95 MPH+E.V (mph)Barrels/BBEGB%FB%LD%
Avg.8.5226730.510.612.334.387.16.444.922.725.1
A.823.764.635.410.411.737.987.87.141.139.319.5
B.5.2217437.113.81337.287.8741.937.220.9

If we take a look at this first chart on plate discipline and exit velocities, both players are very comparable.  However, what catches my eye is that Player A is below average at swinging at pitches in the zone.  I think this due to some changes in their approach at the plate as they upped their walk rate from 2017 from 4.9% to 8% in 2018.  With this new patience, they are leaving pitching in the zone.  Based on this chart, both players are eerily similar.  It's too hard to decipher who is the better option.  Let's move on.

Here is the table on their Batted Ball data.


BABIPGB%FB%LD%HR/FB%Pull%Center%Oppo%Soft%Med%Hard%
Avg..30044.922.725.110403525205030
A..30541.139.319.513.746.635.917.51539.845.2
B..33041.937.220.917.745.834.319.9234136

Again, comparing these two players based on their batted ball data is difficult as well.  However, Player A is shown to make a lot more hard contact.  Both players pull the ball more than league average and this can play into the strength of the defense.  They are prone to fly balls as their line drive percentage is below league average as well as their ground ball percentage.  Again we are left with similar data on each player, but let us look at our final table.

Here is the table on their Expected Outcomes data.


BABIPBAxBAOBPSLGxSLGwOBAxwOBAwRC+
Avg..300.248.243.318.409.396.315.311100
A..305.253.235.305.424.393.325.30897
B..330.293.270.338.503.438.355.332122
We can tell from this data which player seems to pull away.  Even though both players should have lower expected outcomes, Player B has the better batting average, slugging, and weighted on base percentage.  In a league where you need BA, Player B is your man without a doubt.  Overall, the data on these two players is so similar that you would think they would be close when trying to rank each other.  Unfortunately, they are not.

Time for our player reveal.  Player A is Rougned Odor, while Player B is Jonathan Schoop.  If you scroll through many different websites that rank these two players, you can always find Rougned Odor around #10 or close to it.  However, when you try to find Jonathan Schoop, he is nowhere to be found.  Well he is, but not many people look that far down their lists.  Here is what I like about Schoop and why I think he is in for a rebound year.  Last year was a funky year for him.  He was hurt, playing for a team that was absolutely rotten, got traded, struggled and didn't play.  That is a big mental hurdle for anyone.  Now he gets to play for a Twins team that has drastically improved from last year and he gets to play 2B without any worries.  Although I don't see him drastically changing his approach, I do see a player in a good offense than can put up 20 + homers easily with solid counting stats.  I'll take that any day from a player that I can get next to nothing on draft day!

References:
www.fangraphs.com
https://baseballsavant.mlb.com/statcast_leaderboard
www.wikipedia.com


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