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!
Stat of the Week: Weighted On-Base Average (wOBA)
For this installment of Stat of the Week, we’re talking about weighted on base average (wOBA), which is OPS on steroids.
OPS is a simple stat used by a lot of people to measure offensive quality, but it is a messy and inefficient way to do that. OPS is On Base Percentage (OBP) PLUS Slugging Percentage (SLG), but OPS captures the flaws in each of those statistics and does nothing to fix them.
OBP is superior to batting average because it includes walks, but it still treats singles, doubles, triples, and homeruns equally. To OBP, all hits are created equal even though they are not. SLG has the opposite problem in that it weighs hits improperly. A triple is not worth 50% more than a double and a homerun is not worth 4x as much as a single. Those numbers, while simple to understand, do not accurately reflect each type of hit’s outcome on run scoring.
So how does wOBA help? Basically, using linear weights (i.e. math), wOBA properly aligns each hit to a proper value. The formula looks like this and is adjusted each year to reflect changes in the game:
wOBA = [(0.69 x BB) + (0.72 x HBP) + (0.88 x 1B) + (1.26 x 2B) + (1.60 x 3B) + (2.08 x HR)] / PA
Try not to memorize the numbers. Try to understand the ratios because the precise values vary year to year. Here’s a calculator with the 2013 constants for you to play along at home.
What you can see here is that a single is worth about 60% of a double as opposed to half. And a double is more than half a homerun. This might seem counterintuitive at first, but if you think about it, it makes sense. A double will drive in as many runs as a triple, so the only difference is how often the batter would score. Heck a double drives in as many as a homerun except for the batter.
wOBA looks a lot like the other slash line numbers, so here’s a scale to judge. .290 is bad, .320 is average, and .400 is great.
wOBA is a great metric because it tells us what we want OPS to tell us, but it does so in a more accurate way that reflects how things really work over the course of a season. If you’re looking for a number to judge a player’s offensive output, this might just be the one.
A couple downsides, which are evident in other stats, are that wOBA doesn’t include any corrections for era or park. We’ll have to wait for wRC+ to include that stuff.
So next time you want to see how a player is performing, try wOBA and you’ll have a lot more information than batting average and even OPS.
Stat of the Week: Wins Above Replacement (WAR)
Let’s get this out of the way early because it’s going to come up in a couple weeks when SABR Toothed Tigers New English D hands out a very controversial MVP award to someone not named Miguel Cabrera.
This week’s Stat of the Week is Wins Above Replacement (WAR). It’s been in the news a bit lately, so let’s all get on the same page about it.
First, there is something you should know. There are two different WAR. One belongs to Baseball Reference, one belongs to Fangraphs. They are different primarily because they use different measures of defense (more on this later). I will always cite Fangraphs on this site, but only for the reason that I like how they present their data.
The concept behind WAR is the same for both sites. How many wins does a given player add to his team above what a replacement level player would? A replacement level player is defined as a widely available AAA type player. Think Mike Hessman, Jeff Larish types if you followed Tigers minor leaguers in the last decade.
This is a pretty simple idea. What is the difference in wins between Prince Fielder at 1B versus Jeff Larish at 1B if everything else were equal? That is WAR in the abstract.
More concretely, a team that only played replacement level players would win about 50 games per season. As we’ve mentioned a lot here, even terrible teams win sometimes.
So what does WAR look like? For position players, you want to post at least a 2.0 WAR in a season to be considered a “starter.” Below that and you’re a backup or a minor leaguer. 2.0-4.0 WAR is considered solid, 4.0-6.0 is pushing All-Star to superstar levels, and 6.0+ is MVP type guys. You can roughly use the same scale for starting pitchers. Relievers are much different because they play so much less. Better than 1.0 WAR for a reliever is good, 2.0 is great, and 3.0 is excellent.
Let’s talk theory first. The common retort to this is that “Miguel Cabrera has to be worth more than seven wins to the Tigers! If you took him out, they’d suck!”
This isn’t really accurate. Think about it. The Tigers won 88 games, Cabrera posted a 7.1 WAR. Let’s round up to 8.0 WAR to be generous as that difference is attributed to what WAR considers poor defense (more on this later). If the Tigers did not have Cabrera and replaced him with a minor league player ala Ryan Strieby, the Tigers would go 80-82 according to WAR theory. That’s actually pretty realistic if you just look at it. That’s 9% of their wins concentrated in 4% of their roster.
80-82 isn’t very good, but it’s not horrible. After all, they Tigers have a good team around him. Let’s take away Verlander’s 6.8 WAR (rounding up to 7) and we’re at 73-89. Good for fourth worst in the AL. Essentially, if we take Verlander and Cabrera off the Tigers according to WAR, they would only be better than Cleveland, Minnesota, and Boston in 2012.
You have to buy that. They still have Fister, Scherzer, Sanchez, Fielder, Jackson, etc. They would be much worse, but still not a minor league team. Take away Fielder’s 4.9 WAR and we’re down to 68-94. Only the Twins were worse. That sounds about right when you really think about it.
So that’s the theory, but what about the practice. How do we calculate WAR? What WAR seeks to do is combine hitting, baserunning, and defense into a single number calibrated to the only thing we actually care about, wins. Each action earns a “run value” based on how often that action contributes to run scoring and the accumulation of 10 runs is about equal to 1 win.
WAR takes into account how much better than average a player is offensively using wOBA and coverts it into an overall run value, wRAA, based on the number of plate appearances a player has had. You take that wRAA and divide it by the Run to Win value of that year (usually about 9 to 10). That gives you offensive WAR. Baserunning has a similar type formula based on how many bases you take and how many you steal. Defense is based on UZR for FanGraphs and DRS for Baseball-Reference, which all come out in run values converted into wins in the same way. Overall WAR is also adjusted for the position you play.
For pitchers, FanGraphs uses Fielding Independent Pitching (FIP) and includes the number of innings pitched, park effect, and similar adjustments and Baseball Reference uses runs allowed and controls for the quality of your defense.
Simply put, WAR is trying to measure the total contribution a player makes with his play on the field. It obviously doesn’t measure things like leadership that reflect on other players (or moving to a new position!), but everything they do on the field is captured. Surely no one can challenge this concept.
WAR takes various statistics and combines them and scales them to churn out a number. The math is based on baseball history and what has been shown contributes to winning. For example, WAR values OBP over AVG because walks are important, but missing from average. It doesn’t care so much about RBIs because you can’t drive in runs if no one gets on base ahead of you. The math behind this, which I won’t subject you to any more unless you really want me to (here’s Fangraph’s page on WAR), is rooted in the game’s history and they adjust it every year to pick up new information, but it’s always scaled to that year’s replacement level so you can compare across eras.
WAR is not a perfect, exact measure of a player’s value, but it is a good one if you sum a team’s WAR and compare it to their actual win total + 50 (again this number has been slightly adjusted). It’s not a be all end all. If a player is 4.6 WAR and another is 4.5 WAR, they are essentially equal. There is margin of error. But WAR does give you a good sense of how much this player helps his team win with his own performance.
The argument against WAR is twofold. First, it’s complicated. It turns out a pretty good number, but it’s hard to grasp. You can’t watch a game and immediately see how Player X’s WAR is impacted like you can with HRs or average or walks. It’s not a stat, it’s a metric. It weighs the value of each action based on how those actions normally lead to wins for your team. So it’s hard to follow. You have to look at the numbers, you can’t figure them out and follow them as well. I’m not arguing we throw the others out in favor of WAR, but when you want to compare players who player different positions and on different teams, WAR equalizes that through a positional adjustment and other devices.
The other problem with WAR is defense. Defense is really hard to measure. Fielding percentage is not a good measure because that only tells me how often you make errors, it doesn’t tell me what kind of errors. It doesn’t tell me about your range.
WAR uses UZR (Ultimate Zone Rating) or DRS (Defensive Runs Saved) to measure defense. They are both metrics based on range and execution, with human viewers judging every play based on if it should be made routinely and how much harder or easier a play is from average difficulty. So there’s some subjectivity, but it’s much better than any of the traditional numbers. Plenty of people criticize these numbers because they fluctuate a lot and give some weird results on occasion.
Essentially, defense is WAR’s weak leg, but it’s getting better and is much better than anything traditional. But this means we can’t use WAR as a final word. We have to look at other things and use our eyes.
Don’t run from WAR because it is complex math. You can check it yourself by seeing what it turns out in a given year compare to a team’s actual results. The Tigers position player WAR and pitcher WAR sums to 43.9 this season. 50 + 43.9 = 93.9 which misses the Tigers win total by a whopping 6%. Not bad. It works even better with bigger samples.
Question WAR because it may be imperfect. It’s not trying to boil baseball down to a spreadsheet, it’s trying to correctly value on field actions. Having more RBI doesn’t make you a better player than someone else. Hitting more homeruns doesn’t either. If no one gets on base for you, you can’t drive them in. If you play in San Diego instead of Cincinnati, you’ll hit fewer bombs.
WAR is an equalizer. It allows us to compare individuals playing a team game. It’s a good thing. Don’t take it as doctrine, take it as information. The concept is great, the execution is pretty good and getting better. What WAR does is trying to measure value accurately, rather than based on old statistics that were invented before we had a good idea about what mattered in baseball. Check out our Stat Primer page to learn all about what stats are good and which aren’t so good.
And here’s a WAR calculator. Learn how to use it here.
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 