Stat of the Week: Isolated Power (ISO)
This should be a very straightfoward Stat of the Week because it is something you can easily calculate at home without any sort of complex math. This week, we’re talking about Isolated Power (ISO) which measures how good a player is at hitting for extra bases.
You’re likely more familiar with Total Bases (TB) which is a counting stat that picks up on a similar concept. ISO is essentially Slugging Percentage with the singles stripped out.
ISO = (2B+2*3B+3*HR)/AB
A rule of thumb scale puts average ISO around .145 and anything about .200 being great. You read Fangraphs’ write up on the subject here.
I wouldn’t recommend looking at ISO over wOBA or wRC+, but it I would look at it in addition to those two metrics. It can provide you a nice piece of information about how frequently a guy hits for extra bases. It contains the same problem that slugging percentage does in that it weighs doubles, triples, and homeruns improperly, but it’s a good way to separate how much of a player’s slugging percentage is driven by a high rate of singles versus real extra base power. It’s also not a particularly predictive stat over a small sample if you’re concerned about that type of thing.
For reference, the top 5 players by ISO from 2012 were: Josh Hamilton, Edwin Encarnacion, Miguel Cabrera, Ryan Braun, and Josh Willingham.
Stat of the Week: Weighted Runs Created Plus (wRC+)
After a break during the offseason, our Stat of the Week series returns today with an important offensive metric know as Weighted Runs Created Plus (wRC+). You can find this metric on Fangraphs with a full explanation here.
Last season I broke down wOBA which is OPS on steroids. The wOBA idea feeds into wRC. What wRC+ tells is how much better a player is than average when it comes to producing runs for his team. Simpler yet, it’s a catch all offensive metric that can be used for easy comparison between players.
Like WAR, this isn’t a perfect tool, but through some calculations based on the historical value of each plate appearance outcome, we can get an estimate of how much value a player brings to his team. League average wRC+ is scaled to 100, meaning that a player with a wRC+ of 120 is 20% better than a league average hitter. wRC+ is also adjusted for park and league effects, so if you play at Petco Park, you get a little boost because the park suppresses offense.
For reference, both Miguel Cabrera and Mike Trout posted wRC+ of 166 in 2012. The most average players in 2012 by wRC+ were Brett Lawrie and Rickie Weeks. Let’s look at Lawrie’s line to illustrate. He hit .273/.324/.405 with 11 HR in 536 PA. That looks about right for league average. wRC+ tells us Cabrera was 66% better than that, which makes sense given a .330/.393/.606 line.
You’ll need a big enough sample for wRC+ to tell you anything meaningful in a predictive sense, but as the season wears on take a look at the wRC+ leaderboard to get a sense of who the best offensive contributors are.
I encourage you to go back and read my wOBA breakdown because it stresses the idea that OBP and SLG are improperly weighted when you add them together to get OPS because a double isn’t really worth twice as much as a single. wOBA gives you a better answer to the question OPS tries to answer, and wRC+ scales it to league and park averages.
Go explore wRC+ for yourself and feel free to post any questions you may have. We at New English D are big believers in sabermetrics, not because we want to boil the game down to a spreadsheet, but because we always want more information about the game. More stats and metrics are a great way to learn more about the game and evaluate what you watch.
Skeptical? Here are the best 8 players by wRC+ last season: Cabrera, Trout, Braun, Posey, McCutchen, Fielder, Encarnacion, and Cano. The math might scare you off, but don’t let it. Just learn how to read the output. You don’t have to buy into everything you see on a sabermetric site, but I think that if you try it, you’ll like it. There is a ton you miss by staying with the traditional stats. And who wants to miss baseball?
Calculate it yourself!
Fangraphs and Baseball Reference Unify Replacement Level
Today, Fangraphs and Baseball Reference consummated a relationship we knew to be coming for the last few weeks. While the two sites have always calculated Wins Above Replacement (WAR) differently, they decided to discuss reworking a component of the metric. That component was replacement level, defined as the production of a player who is readily available as a minor league free agent or on the waiver wire.
Today it happened. Dave Cameron can give you all the specifics over at Fangraphs, and I can’t say I disagree with any of the changes. I like that the two leading sites are working to improve WAR and our overall statistical evaluation of baseball. This is a step in the right direction and it’s good for everyone involved.
But there is a weird result from today’s unveiling of the new replacement level that is freaking me the hell out.
Everyone’s WAR is slightly different than it was yesterday.
Now many who hate sabermetrics might use this as a point of assault, but those people who know better know that it’s just a shifting baseline calculation that marginally changes the precise point value of WAR. The substantive results are the same, just refined.
But for someone who reviews baseball statistics quite religiously, it’s trippy. For example, Justin Verlander gained 0.2 WAR for 2012. Buster Posey lost 0.4 WAR. Most of the exact changes are pretty small and don’t change the interpretation much, but when we’re dealing with something like WAR that is imprecise and on a relatively small scale, things get funky. A bunch of players shifted places in rankings. Not dramatically, mind you, just from 2nd to 3rd or 8th to 7th. It’s minor and doesn’t mean much, it’s just weird.
I woke up today and the past had changed. I mean, I know that isn’t true, but it seems like it. Justin Verlander was the best pitcher by WAR last season, but now he is the best by more. Perhaps this means nothing to anyone else, but it was interesting for me.
WAR got better today and given the people in charge of its design, it will continue to get better into the future. Let’s just hope I’m better prepared to cope next time and don’t spend an hour of my day staring at my computer repeating “this is weird” to myself.
But seriously, it was.
Stat of the Week: Fielding Independent Pitching (FIP)
One of the things I want to try to do here at New English D is to introduce sabermetrics into the common vernacular of baseball fandom. I think the biggest reason for resistance to new stats and metrics is that they are not commonly understood. It’s not because people are too stupid, they just simply aren’t looking to spend a lot of time learning new things that don’t seem relevant.
Basically, most baseball fans don’t really understand why the basic statistics are misleading them about a player’s true value.
I’d like to start with one of the more prominent sabermetrics for pitchers, Fielding Independent Pitching (FIP) which is essentially a stand in for Earned Run Average (ERA).
The problem with ERA is that so much of it is outside of a pitcher’s control. For example, if you have a terrible defense, your ERA is going to be higher than if you have an awesome defense, even if you make identical pitches for an entire season.
What FIP tries to do is factor defense out of the equation by presenting a formula that predicts what your ERA would be if you had league average defense and league average luck by looking at your strikeouts, walks, and homeruns allowed (things you can actually control as a pitcher). Generally speaking, the most contact hitters make against you, the more variation we could see.
The formula goes something like this and is based on long run averages in MLB history:
FIP = (13 x HR) + (3 x (BB+HBP)) + ((2 x K) / IP) + constant
What this formula does is give you a number that looks like ERA, but only responds to things inside a pitcher’s control and FIP is a better predictor of future performance than ERA. Generally speaking, it’s a great place to start your analysis. You want to dig deeper into batted ball data and other trends, but FIP starts you off with a number that is based solely on a what a pitcher can control.
For reference, an average FIP is 4.00 with an excellent one being 2.90 and a terrible one being 5.00. For a full explanation from the people who created it, see this.
To give you an idea, let’s take a look at the some ERA to FIP comparisons from the 2012 season. For a complete listing of FIP, head here.
MLB’s top five in FIP this year were Gio Gonzalez (2.82), Felix Hernandez (2.84), Clayton Kershaw (2.89), Justin Verlander (2.94), and David Price (3.05). That seems to jive with what you might think. Remember Gio and Kershaw get to face the pitcher, so their number is going to look a little better just like ERA.
So of the qualifying starts in 2012, whose ERA made them look better or worse than they are?
I’m picking a few examples to demonstrate FIP’s usefulness. Tigers’ sinkerball Rick Porcello seems an obvious candidate for an ERA inflated by bad defense, right? Very true. Porcello’s ERA is a robust 0.68 runs higher than his FIP. The Royals Luke Hochevar didn’t get much help either with an ERA a whopping 1.10 runs higher than his FIP.
How about guys whose ERA made them look better than they are? Jeremy Hellickson got a full 1.50 runs back from his defense per nine innings and extreme fly ball pitcher Jered Weaver, with the help of the crazy good Angels outfield, got 0.94 runs better in ERA than FIP.
Now four random examples might not convince, but I encourage you to take a look at the FIP leaders and start using that metric to learn a little bit more about how someone is pitching.
Two final thoughts. One don’t bother with RA Dickey because there are so few knuckleballer’s in history and the averages don’t control for how differently knuckleballs get hit.
Two, whose defense and luck has been the most average so as to keep their FIP in line with their ERA this year? That award goes to the Pirates’ James MacDonald who posted an ERA and FIP of 4.21, making him the only player to have both numbers equal.
Come back next week for another Stat of the Week and feel free to suggest some that you would like to learn about.
New English D 