Welcome to the 3rd Inning of our Hard Nine Series. By now, you might have found a little groove and settled in a little. You might have had a little success with our DFB guide. You might be left scratching your head at how to go about doing your own research. This installment “Critical Stats” aims to help explain some key DFS statistics to you and teach you to better apply them to your baseball research. For a complete overview of the series, click HERE. We are building an extensive course for the newest player to learn and become competitive quickly in DFS baseball. Whether you are brand new or brushing up, thank you for reading our series.
What you see above is called a Venn Diagram. It illustrates the intersection and overlapping of concepts, and I think it applies directly to statistics in the world of MLB DFS. We use statistics as a means to measure a player’s abilities and talents. We use them, over lengths of time, to weed out inherent variance and grasp a player’s true value. Then, we apply statistics and use them to compare one player to another and build our winning lineups. In short, statistics are the place measurements, variance, and comparison come together.
However, not all statistics are created equal. Many people know the common batting average and know it’s a measure of how often a hitter makes contact with the ball and reaches base safely, and many know it is simply expressed as a percentage of times a hitter has achieved this feat. A lot of people also know that OBP stands for “on-base percentage” and that it is similar except that this percentage is simply the percentage of times a player has reached base including times he never hits the ball (BBs or “walks” for instance). These statistics are fine for everyday use, but they assume all hits are created equal…..which the DFS player knows they are not. A walk is not as helpful as a home run. A double isn’t even necessarily twice as important as a single, but we can all agree it’s better. These archaic statistics led to the evolution of more advanced metrics to not only measure how well a hitter swings a bat, but also how much his hits help his team.
Slugging percentage measures power. Quite literally the number of bases a player reaches per AB (at bat). This number was considered “ok” but not complete enough because guys like Mark McGwire were all-or-nothing (and carried a huge SLG) and a guy like a Joey Votto likely helps his team more because he reaches base more often AND hits for good power (but doesn’t have as high a SLG). So, the OBP and SLG were added together to get OPS (on-base plus slugging) and a supposed better representation of the type of hitter that helps his team most, one that reaches base and can hit for power, too. This was accepted as a better measure of both reaching base and power. OPS is our first really good statistic, and it’s rather mainstreamed by now in that most people know what it is and roughly how to use it. The issue with OPS is that it still treats things equal that aren’t. OPS treats OBP and SLG as equal parts by simply adding them together. In reality, SLG (hitting for power) is about twice as important as simply reaching base (OBP). So, adding them together does not put any weight on this inequality……rendering it imperfect. You can see there is a quest for the perfect statistic underway. This evolution has certainly found it’s way into DFS because we want to look at a simple number/metric and accurately evaluate a hitter. Who doesn’t, am I right?
Above is a chart of OPS ratings for your memory. Don’t memorize it. Just note that anything over .900 is great and anything over 1.000 is elite. You can get pretty far just looking at OPS. But, it won’t take you as far as the next one…..wOBA.
wOBA is all the buzz right now, and in my opinion, for damned good reason. It just freakin’ makes sense! wOBA is “weighted on-base average” and is my favorite catch-all statistic for offensive production. wOBA’s founding principle is “not all hits are created equal.” Most metrics assume they are. wOBA rewards guys for walks and doubles and home runs and even getting smoked in the chin by some music (see that?). While OPS asks the right questions, wOBA answers them a little more comprehensively. OPS and wOBA will often lead you to very similar conclusions about a hitter. The flaw in OPS, to me, is that it undervalues getting on base to extra base hits and also fails to assign weighted value to those extra base hits. But, when looking at OPS and wOBA, I honestly still look at both in conjunction with each other. If you’ve followed any player picks I’ve done lately, you’ve noticed I talk more about these stats than any other.
Here is a chart of wOBA above. It is scaled to provide similar numbers to batting average and OBP. The actual calculation varies from year to year, but for the sake of our purposes, just roll with .320 being average. Over .375 is just a monster.
Perhaps the next statistic I look at is ISO and it’s purely a power number. It’s commonly calculated by simply taking the player’s SLG and subtracting his AVG leaving behind only hits that went beyond first base. If a guy hits all singles, he will have a .000 technically. My softball ISO is .000…….for real. I had one great year where I had gap power, but now I just needle them and beat out grounders. But, I digress.
Using ISO is pretty easy. Around .140 is average and over .200 is pretty powerful. However, it isn’t perfect. Not all .200 ISOs are created the same. Player A has a batting average of .250 and a slugging percentage of .450. Player A’s ISO is .200. Player B has a batting average of .350 and a slug of .550. Also an ISO of .200. But, Player B is clearly the better player. Also, ISO needs a larger sample size to sort itself out. Math nerds tell you about 500 ABs is needed for it to be a decent representation of a player’s power. So, I largely disregard 7 day and 30 day ISOs when looking at them, but I do take a peek as I’m looking at numbers.
Now, we can get into another evolution of stats. RC (runs created) was invented to measure a player’s total offensive value to his team by converting his contributions like average, slugging, walks, stolen bases, etc into runs he created for his team. That was quickly accounted for by wOBA in putting weighted significance on extra base hits accordingly. This became wRC (weighted runs created). Another evolution came to pass where wRC was injected with factors to adjust for ballparks and shown in relation to a league average number. This became wRC+ and average is 100. 130 is pretty darned good and above 160 is truly elite. Honestly, it’s not a number I’ve incorporated just yet into my workflow. But, I can glance at it and tell you a dude is a great contributor. I can also do the same with a .410 wOBA so I fail to see the reason at deeply looking into two numbers.
Another I’ll go into is BABIP (batting average on balls in play). This is handy because it measures luck. It measures how often a ball that goes into play earns a hit for a batter. Strikeouts don’t count towards it. Neither do homeruns. But, neither do catcher’s interference, reaching via error, intentional walks, etc. Crudely stated, we BABIP tells us whether a player is getting his hits from facing poor defenses, bloop hits are falling, or conversely if rocketed line drives are simply being caught. League average is around .300 usually, so a player with a BABIP of .400 can be considered as “getting a bit lucky” and this indicates he is likely due some regression soon. Same with a player with a BABIP of .200, but he is due some good luck because he’s likely been getting robbed.
I glossed over a few because I don’t use them a lot in limited time researching players. You can certainly dig into them more if you like, but I don’t see it being worth more than a basic understanding of what the numbers are telling you. I don’t very often get this far down into the tie-breaking when looking at a few hitters for these numbers to matter for me in any given day.
Other things you can look at are K% (percentage of times a player strikes out), BB% (percentage of times he walks), Ground Ball %, Line Drive %, Fly Ball %, Hard Hit %, Soft Hit %, etc. For about the best source ever, you can run over to Fangraphs.com and read until you go blind. It’s an incredible site for stat nerds like us.
I will insert the pitching portion of stats as I get them complied. I wanted to release this first to give everyone something to chew on for awhile.