Alright. Welcome to my TED Talk. Thank you for being here.
We use a number of advanced stats at Royals Farm Report and, while we talk through them in-depth at times on Twitter and on the RFR Podcast, I think it’s fair to put something down that folks can reference at all times in terms of what’s important, what matters, what doesn’t, etc. I want to be clear that I am NOT an expert when it comes to baseball analytics. I just happen to take an interest in it, listen to a lot of people smarter than me, do as much research and reading as I can, and try to make sense of it all as I go. I actually think my not being an expert is a great reason for you to keep reading this. Because I’m not an expert, and just someone who follows the game and stats more closely than is probably sane, I think I can help the average baseball fan along in the analytics department. What I’ll try to do semi-briefly is give you a list of advanced statistics, what they mean, how they’re used, and why they are important. Make sense?
One thing to remember before we get started: STATISTICS AND ANALYTICS ARE NOT THE SAME THING. Statistics are measurable. They are objective. If a batter reaches base 4 times in 10 plate appearances, his On-Base Percentage (OBP) is .400. That is a stat. It is factual. It is measurable. It is NOT an analytic. Analytics are how we APPLY the statistics. For example: saying that Player A is better than Player B because his OBP is higher, that’s an implication based on statistics. An analytic take, if you will. But OBP is not an analytic by itself. Make sense?
Alright. Here goes nothing. Remember: I am not an analytics expert. I’ll do my best to relay the way I understand the stats in layman’s terms to be as helpful as possible.
wRC+ is a great stat to use when you’re trying to sum up a player’s overall offensive production with one number. 100 is average, 105 would be 5% better than average, and 95 would be 5% worse than average. This is super useful when you are trying to compare two hitters across different leagues or different years. For example, here was the slash line of a 100 wRC+ (league average) hitter in the High-A East division in 2021, compared with a 100 wRC+ hitter in the Triple-A West division in 2021:
– Jacob Rhinesmith (High-A East): .253/.345/.383/.728
– J.J. Matijevic (Triple-A West): .245/.325/.504/.828
If we didn’t have wRC+, you might look at the OPS of these two players and say, “Well, Matijevic had an OPS 100 points higher than Rhinesmith, so he must have had the better season.” What wRC+ tries to do is compare hitters to the other hitters in their league, and then spit out one number telling you how they compared to a league average hitter IN THAT LEAGUE. Both Matijevic and Rhinesmith were exactly league average for the leagues they played in last summer, despite an OPS difference of 100 points! Like I mentioned before, this is great for comparing hitters in different leagues, and in different eras where pitching or hitting may dominate at different times.
OPS (On-base Plus Slugging)
Speaking of OPS, a hitter’s “on-base plus slugging” is quite literally just that. It’s the sum of a hitter’s on-base percentage and their slugging percentage. This is a useful stat if you just want to quickly compare two players, but it’s quite a flawed statistic as well. The reason for this is that 1 point of on-base percentage is actually worth more than 1 point of slugging percentage. For example, let’s say you had these two hypothetical hitters:
– Player A: .300/.400/.300/.700
– Player B: .300/.300/.400/.700
While these two players both have a .700 OPS, Player A would (theoretically) be much more valuable to his team because 100 points of OBP is worth FAR more than 100 points of SLG. Again, I’m not an expert per se, but this is the way I understand the flaw with OPS. Useful stat. Good for quick comparisons. By no mean is it perfect, nor should it be taken as gospel.
ISO is a great stat to see how powerful a hitter has been. ISO is calculated by subtracting Batting Average (BA) from a player’s Slugging Percentage (SLG). Slugging Percentage (SLG) is often used to comment on a player’s power output, but the problem with SLG is that it’s propped up a bit by Batting Average (BA), as SLG takes into account every hit a player gets, including singles. Here’s another hypothetical example:
– Player A: .200 BA, .500 SLG, .300 ISO
– Player B: .300 BA, .500 SLG, .200 ISO
Just looking at their SLG, you might think that Player A and Player B hit for similar power outputs. Using ISO, we can tell that, while Player A had a very low BA, that also came with a ton of extra base hits. This DOES NOT mean that Player A was inherently more valuable to his team, just that he hit extra base hits at a higher rate than Player B did. Hopefully that makes sense. Long story short: ISO = Slugging Percentage (SLG) – Batting Average (BA) and we do this to get an idea of how much “ISOlated” power a hitter hit for in any given year.
SwStr% (Swinging Strike Percentage)
This one is pretty self-explanatory I think. SwStr% is calculated by dividing the number of times a batter swings and misses by the total number of pitches that batter sees. So, if Whit Merrifield sees 5 pitches in an at-bat, and swings and misses at one of them, his SwStr% would be 20%. It’s important to keep in mind that you can use this for pitcher’s too. Only, it would be like if Danny Duffy threw 5 pitches and the batters swung and missed at one of them, Duffy’s SwStr% would be 20%. Perhaps obviously, pitchers want their SwStr% to be very high and hitters want their SwStr% to be very low. SwStr% is good for identifying pitchers who have good “stuff,” but maybe they aren’t having the most success right now. With hitters, if a hitter hits for a lot of power, it’s normally good to check their strikeout rates and SwStr%. Lots of power can be neutralized with lots of swings and misses (see: Matias, Seuly). But lots of power with very few swings and misses can be very exciting (see: Pasquantino, Vinnie).
K% and BB% (Strikeout Percentage and Walk Percentage)
These are also good for both pitchers and hitters. For hitters it’s pretty self-explanatory. You want to walk a lot and strikeout very little. For pitchers, I’m adding this in here because I like K% and BB% a lot better than K/9 (strikeouts per 9 innings pitched) and BB/9 (walks per 9 inning pitched). Lots of folks use K/9 and BB/9, which is fine, but K% and BB% are a little better to use when analyzing pitchers. The reason for this is pretty simple if you really think about it. If a pitcher gives up 7 hits in one inning, but strikes out 3 batters, his K/9 would be 27.0. That’s a perfect K/9, but we all know the reality of how that inning went. The other team probably scored a few runs, right? A K% of 30% would tell a better story of, “Yeah, that guy had three strikeouts, but he only struck out 3 of the 10 batters he faced.” Ironically, a 30% K% is really good for a pitcher, but you get the point. K% > K/9 and BB% > BB/9.
EV (Exit Velocity)
Another stat that I think is pretty self-explanatory by itself, but maybe has been misunderstood a bit in terms of analytics. Exit Velocity is the measure of how hard a ball is hit off the barrel of the bat. Super simple statistic. The way teams use this varies. You can use average Exit Velocity to get an idea of how hard a hitter hits the ball (or a pitcher gets hit) on a regular basis, and that’s normally pretty good. The idea is, if a player has really good exit velocities and isn’t having a ton of overall success, you can probably bank on that player having more success due to luck, or maybe just an adjustment or two. Another way to use it is with “Max” Exit Velo, or the hardest a player hits a ball one time. Darryl Collins, a Royals prospect that played in Low-A in Columbia last year, is a great example of this. Darryl Collins had a very low ISO (.091) in 2021 and thus a very low SLG (.338). That’s…not great from someone I consider to be a really exciting prospect. However, if you dig a little deeper you’ll see that Darryl Collins had a max exit velocity of over 113 mph last year. That’s right up there with MJ Melendez and Nick Pratto’s max exit velos. So what does this tell us? Darryl Collins may not be hitting for much power right now, but it’s definitely in there somewhere! He’s got a great BB/K ratio, he’s got a great SwStr%, and we know he can hit it hard. Now we just have to teach him to hit the ball hard more often! (Keep this in mind when our rankings come out and Collins seems to be higher than you expected. ;))
LA (Launch Angle)
Whoa! I swear, that’s not a curse word! Every single ball that has ever been hit has a launch angle. Even bunts have launch angles! It is so funny to me listening to people talk about launch angle who so clearly don’t understand what they’re talking about. LAUNCH ANGLE IS JUST A MEASURE OF HOW HIGH OR LOW A BALL LEAVES THE BAT. It’s not even an “advanced” statistic really. It’s just never been able to be easily measured before. Thanks to new technologies like Hawkeye, we can track this pretty easily now.
We can kind of circle back to Darryl Collins here really quickly. Take a player with really good exit velos who isn’t hitting for much power at all. Why could that be? Maybe he’s hitting too many ground balls. Maybe he’s hitting the ball too high in the air. Who knows! Every case is different. Launch Angle offers up possible answers to some of the questions we have, but certainly doesn’t tell the entire story. Remember, every single batted ball, even bunts, has a launch angle. It’s just a measurement.
xwOBA (Expected Weighted On-Base Average)
Okay…so…here’s the thing. I like wOBA a good bit. It’s a useful tool for analytically minded people like myself, but I really don’t know how much I think the average baseball fan should care about wOBA.
I do however think that using xwOBA can be a useful tool for baseball fans when you compare it to a player’s wOBA. Here’s a terrible way of putting it, and also the gist of why you should be familiar with xwOBA in my opinion: if you look at a player’s Statcast page, and his xwOBA is significantly higher than his wOBA, that player might be getting unlucky. xwOBA tries to remove luck from the equation. “What should Player A’s wOBA be if he was getting about league average luck?” Something like that. I almost didn’t throw xwOBA into the article, because it can get confusing, so I won’t try to dive into it too much. Just know that xwOBA is a good indicator of whether a hitter could be due for more or less success in the future based on (among other things) how hard they’re hitting the ball at present.
FIP (Fielding Independent Pitching)
This was the darling of pitching analytics for a while and is (finally) falling out of favor a bit. FIP is fine. It’s a useful baseline for some pitchers. FIP basically tries to spit out an ERA-like number based solely on the number of home runs, strikeouts, and walks a pitcher allows. FanGraphs uses FIP, not ERA, in their WAR calculation for pitchers, which is why I personally prefer to use Baseball Reference’s version of WAR for pitchers because BRef uses ERA, not FIP.
Look, FIP is fine when used correctly. It can generally tell you how successful a pitcher has been when it comes to striking guys out, not giving up free bases, and keeping the ball in the yard. It does a horrible job of accounting for literally everything else. FIP can sometimes be a good indicator of a pitcher getting lucky or unlucky, but should not be taken as gospel. I like FIP a little bit, but I think folks got carried away with it for a while. Like ERA, the lower a pitcher’s FIP, the better. Like ERA, FIP is a useful tool with a ton of flaws. Like ERA, FIP can be used to answer some questions, but not all. Make sense?
BABIP (Batting Average on Balls In Play)
This is a pretty interesting stat that can be super useful when trying to get a gauge on how successful a player may or may not be in the future. Yermin Mercedes was the perfect example in 2021. He was lighting the world on fire early on but was running up a BABIP over .400. It seemed like he’d come down to earth eventually, and then he came crashing down all at once it seemed.
So what is BABIP? BABIP tells you what a hitters’s batting average is when the ball is in the field of play. So it excludes home runs and strikeouts. It tries to boil down to how often a hitter gets a hit when the fielders have a chance to make an out. If Whit Merrifield goes 3-12 in a series with 1 strikeout and 1 home run, his batting average would be .250, but his BABIP would be .300 (3-10) since we’re excluding the K and HR, two events where the fielders didn’t have a chance to make any sort of a play. Make sense? A player’s BABIP can have several factors. Luck, hard hit rate, exit velocity, launch angle, shifts, the speed of the batter…all of these things can factor into a player’s BABIP being high or low. Generally speaking, if a player’s BABIP gets really low (.220-.240), we can expect that get better with time. Same goes for BABIPs that get crazy high (.370+). We’d generally expect that a hitter won’t get that lucky, that often, for much longer.
So, there ya go. That’s roughly the 10 advanced stats I most commonly use personally or see used online by others. I have a feeling there will be a question like, “So which stats are the best for evaluating prospects?” The answer is…all of them. I know it sounds cliche, but there is SO much that goes into it, no one stat can tell the entire story. They don’t even come close. One of the most important things to consider for prospects is how old they are compared to the level they’re playing at. That’s right, age is one of my favorite statistics when evaluating prospects. Strikeouts are big, wRC+ is great, but you need ALL of it to evaluate a prospect, as well as the ability to watch a player and identify things in their swing/delivery that may or may not work at the next level. There’s a reason the best scouts in the world get paid handsomely to watch baseball. It’s a tough job. Hell it’s borderline impossible. But with the advanced stats that we have today, it makes that job a little bit easier, if nothing else just to confirm what our eyes are already telling us.
This may not have been helpful at all, in which case I legitimately apologize that you’re still reading this. If you follow the site and have ever wondered what the hell we’re talking about when we string a few random letters and a symbol together, hopefully this helps a bit. The biggest thing to keep in mind is that all of these stats listed out are just that: STATS! They are objective units of measurement. The idea of “analytics” comes into play when you try to analyze stats and make them useful. Hopefully this helped you even a little bit. It would be so much easier to explain this in person, but unfortunately we won’t always have that opportunity. If there’s one thing I would say to anyone interested in advanced stats, it’s don’t be scared of them. Most of them make plenty of sense once you give it five minutes of reading/research. Spend an hour one day reading about baseball analytics and you’ll find that your experience watching the game is all you need to start getting into this new era of stats!