Are Sabermetric Stats Getting Too Complicated? Fear Not.

In the beginning, we had Batting Average.  Shortly thereafter On Base Percentage and Slugging Percentage came along.  Over time, more and more percentage based baseball stats were created.  Easy to follow creations like OPS (on base plus slugging) and BABIP (batting average on balls in play) were soon over-shadowed by monstrosities like WAR (wins above replacement), LIPS (late inning pressure situations) and PECOTA (player empirical comparison and optimization test algorithm… Huh?).  Unless you’re like my buddy Chuck and have an advanced degree from MIT, you probably struggle to keep up with all this wizardry.  Sure, you can look up what someone else put together and see who scored the highest but can you really comprehend what these stats are telling you, REALLY?  I certainly can’t.

Last year a friend of mine said he had an opening in his fantasy baseball league and wondered if I’d be interested I taking over a team.  I had recently “retired” from amateur baseball after 29 years so I figured it would be a good way to keep in touch with the only game I’d ever really loved.  I looked at the team I’d be taking over and immediately noticed some glaring issues.  It was riddled with over-priced veterans who were past (or quickly passing) their prime.  The former owner was an Irish guy who happened to be in love with the Red Sox.  While loving the Red Sox isn’t inherently problematic in itself, the fact that this guy knew absolutely nothing about baseball was.  No wonder this donkey of a team had finished 7th out of 8 teams the previous season.  Of the 23 man roster I’d inherited, I kept just five players; Buster Posey ($33), Yan Gomes ($10), Anthony Rizzo ($26), Todd Frazier ($10) and Xander Bogaerts ($6).  This left me with $175 (of the initial $260 budget) to fill 18 roster spots.

Wanting to ensure that I gave a good account of myself and my baseball knowledge I set about creating an excel spreadsheet with all of the 2014 MLB stats.  Once I’d gotten all of the stats uploaded into my excel sheet I began pouring over them any chance I got.  I’m normally quite outspoken so when I went an entire episode of some asinine reality show without breaking in to a rant about how stupid the world is becoming, my wife knew something was up.  “What are you doing?” she asked.  “Looking at stats,” I replied without looking up from my laptop.  Satisfied with the uncharacteristic silence, she went back to watching her show and I to my stats.  Although useless in relation to the task at hand, a dog-eared copy of Michael Lewis’ Moneyball was never far away.  That thing has taken (and continues to take) a beating; such is the frequency that I consult it.  Although the frequency of use is similar, Jonah Keri’s The Extra 2 Percent is a little less vulnerable, nestled safely inside my Kindle (I couldn’t find a hardcopy here in Dublin).

After a few days, and many unsuccessful attempts to get my head around Peripheral ERA and the aptly named NERD, I realized that I needed to come up with some stats of my own that made sense to me.  Since I’d be creating them myself I would know exactly what was in them and why.  Sabermetrics are great, but if you don’t really understand them or know how to apply them when evaluating a player, what good are they?  The first thing I wanted to do was create a batting stat that gave a basic overview of how effective a hitter was.  Somewhere in Moneyball it’s stated that OBP is about three times as valuable as SLG.  A traditional OPS just adds the two together as if they’re equal so I wasn’t really keen on using that.  Partially because all of my opponents would have access to the same information and partially because it just wasn’t as efficient as it could be.  Some may point out the existence of wOBA (weighted on-base average) but as previously discussed, I didn’t really understand how this had been created or why a walk was worth less than a hit by pitch or a single.  The guy got down to first base, didn’t he?  I thought that was all that mattered, wasn’t it?  Anyway, I decided to try to create something a little less complicated for myself.  Despite most Sabermetrician’s distain for batting average, I chose to add it to my formula because at the end of the day it had to be worth something.  In order to weight them all correctly I came up with the following formula:

((AVGx1)+(OBPx12)+(SLGx4))/17 = TOE

I called it Total Offensive Effectiveness, or TOE.  I’ve used the player’s OBP value 12 times and his SLG four times to give me that 3:1 ratio described in Moneyball.  The reason this isn’t just 3:1 is because I wanted to add AVG.  No, I’m not saying that SLG is worth four times as much as AVG but this allows me to add AVG while keeping my OPB:SLG ratio the same.  I then divide the total by 17 as there are 17 values making up the number.  This then gives me a weighted average that can be used to evaluate players.  Obviously all the studs like Harper, Trout and Goldschmidt are up at the top of the TOE rankings, but once you get to those late rounds and you need to find value players for a few bucks, it’s a quick way to identify bargains.  Like when I snagged Peralta in the 2015 draft:

Jhonny Peralta, $2:         155 G, 159 H, 17 HR, 71 RBI, 50 BB, .275 AVG, .334 OBP, .411 SLG, .349 TOE

Troy Tulowitzki, $39:      128 G, 136 H, 17 HR, 70 RBI, 38 BB, .280 AVG, .337 OBP, .440 SLG, .358 TOE

Yes, I realize that Tulo would have had higher aggregate numbers had he not been injured but hopefully you can see the point I’m making.  While they’re not equal in terms of skill or production, Tulo certainly wasn’t 19.5 times better than Peralta, as his price tag suggests.  Peralta was overlooked by everyone until the late rounds.  When someone else nominated him, it was because his max bid was $1 and he needed a “filler” guy for his 2B/SS slot.  When I grabbed him at $2, nobody cared.

Here’s a snapshot based on stats accumulated in 2015.  These aren’t necessarily the top guys at the specified position, but just an illustration showing how certain players compare to some of the stars:

Top 5 Overall

  • Bryce Harper                      .497
  • Joey Votto                           .470
  • Paul Goldschmidt             .460
  • Miguel Cabrera                  .456
  • Mike Trout                          .440



  • AJ Pollock                            .395
  • Shin-Soo Choo                  .390
  • JD Martinez                         .385
  • George Springer                .383
  • Curtis Granderson            .380



  • Francisco Cervelli             .373
  • Russell Martin                   .354
  • Brian McCann                    .342
  • Jonathan Lucroy                .338



  • Eric Hosmer                        .382
  • Brandon Belt                      .380



  • Jose Altuve                          .376
  • Ben Zobrist                         .376



  • Maikel Franco                   .376
  • Jed Lowrie                          .322
  • Pablo Sandoval                 .307



  • Francisco Lindor               .381
  • Carlos Correa                     .380
  • Xander Bogaerts               .368
  • Ketel Marte                         .359
  • Brandon Crawford            .350


Again, this isn’t supposed to be a fool proof formula for success, but it can give you a bit of confidence when you’re feeling overwhelmed by all the super complicated stats that are out there.  By using this formula (or creating your own Sabermetrics) you’ll be able to pay a higher price for some of the premium players, knowing that you can still get decent value in the later rounds like I did with Peralta.  If nothing else it will at least help you familiarize yourself with different stats and how they may correlate to one another.


As always thoughts, comments etc. are welcome.




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