Tag Archives: wOBA

The Nine Most Misleading Batting Averages of 2013

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On this site, we often discuss which statistics are misleading and which statistics are better at determining a player’s value. Over in our Stat Primer section we have tons of posts about why pitcher wins are bad and why RBI aren’t a good way to measure individual players. We also have a post about why on base percentage is better than batting average and why you should use Weighted On Base Average (wOBA) if you’re really only interested in looking at a single number because it weighs each type of hit based on its actual contribution to scoring. You want to look at stats that correlate more highly with scoring and OBP and wOBA are much better than batting average (just click the OBP v AVG link above to see for yourself).

All in all, for hitters I’d tell you to look at Weighted Runs Created Plus (wRC+) because that is a park and league adjusted version of wOBA, meaning that you can compare players at Petco and Camden Yards and players from 2010 and 1960. It’s simple to understand as 100 is average and anything above it is that % better than average and anything below it is that much worse. One of our main goals is to make advanced statistics more popular in the broader baseball community because we think they will improve your enjoyment of the game.

Simply put, many of the traditional stats were invented before we knew enough and had the ability to calculate better ones. It’s always important to move forward. Today, I’m offering a bit of evidence from the 2013 season about why you should look beyond batting average when judging a player. Below you will find The Nine Most Misleading Batting Averages so far this season. The rules are simple. These are players who have a below average batting average and above average offensive value or an above average batting average and below average overall value. Players who had really good batting averages that still undersold their value (think Cabrera) are not included because that would just be a list of the best hitters in baseball and that would be boring.

Think of these players as guys who either walk a ton or not at all and/or guys with lots of power or no power. Batting average treats every hit the same and ignores walks. That’s not a good idea. Batting average is a fine stat, but it should only be a compliment to on base percentage if you want something simple and wOBA or wRC+ if you want something more daring. This is a plot of AVG and wRC+ to show you that while batting average is important in determining offensive value, there is a lot it doesn’t explain. About 54% of the variation in wRC+ can be explained by average. Walks, power, and ballpark make up the rest and we shouldn’t ignore them:

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It’s important to note that I make no adjustment for position, meaning that these are all deviations from league average (.257 AVG) and not based on their respective positions. These are batting averages that don’t tell the whole story about a player, not batting averages that explain positional value.

Rank Name Team PA BB% AVG OBP SLG wOBA wRC+
9 Jose Altuve Astros 436 5.50% 0.284 0.323 0.362 0.298 87
8 Brandon Moss Athletics 346 9.50% 0.241 0.321 0.456 0.338 116
7 Pedro Alvarez Pirates 402 8.00% 0.240 0.301 0.490 0.337 117
6 Chris Carter Astros 390 12.30% 0.214 0.315 0.432 0.327 107
5 Adam Dunn White Sox 418 12.90% 0.217 0.323 0.457 0.337 109
4 Raul Ibanez Mariners 350 7.10% 0.249 0.303 0.520 0.353 126
3 Jose Bautista Blue Jays 457 12.90% 0.252 0.348 0.496 0.365 130
2 Alexei Ramirez White Sox 455 3.10% 0.277 0.302 0.349 0.285 73
1 Dan Uggla Braves 422 13.30% 0.198 0.315 0.419 0.325 106

What you have is three types of players. One are players who have a high average but never walk and don’t hit for power. Two are players with great walk rates. Three are players with a ton of power. Each of those qualities makes batting average deceptive. We don’t have to get rid of the stat, but it’s important to understand that walking and extra base hits are very important and just dividing hits by at bats doesn’t equal offensive value.

I say this because the person who wins the batting title is only the best offensive player 30% of the time. We call that person the “Batting Champion,” but that’s not really true. Offensive value is more complicated that H/AB and it’s important to start moving towards stats that capture that, especially because we already have those stats and it only requires a few minutes to learn about them.

Stat of the Week: Weighted Runs Above Average (wRAA)

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When we talk about offensive statistics, the ones we usually talk about on New English D are wOBA and wRC+ which take the actual value of each offensive action and weight them properly, which OBP and SLG do not do. I encourage you to clink the links and read about those statistics if you have not already done so. However, those two statistics are rate stats and not counting stats. Rate stats tell you how well a player has performed while they’ve been on the field, but counting stats are also good for telling you how much value a player has actually added to his team.

If you have a 150 wRC+, but only have half the plate appearances of someone with a 120 wRC+, you’re not as valuable. You need to be both a good performer and a player who stays healthy and on the field. With that, I’ll introduce Weighted Runs Above Average (wRAA) to do just that. Weighted Runs Created (notice the absence of the plus sign) is a similar statistic, but it is just scaled differently. The concept is the same, but let’s stick with wRAA.

wRAA is the offensive component of Wins Above Replacement (WAR) and is based on wOBA and is rather simple to calculate if you have all of the necessary numbers.

((wOBA – League Average wOBA)/wOBA scale) * (PA)

A player’s wOBA and PA are pretty obvious and the league average and wOBA scale be found for each season quite easily here. The idea behind this statistic is how many runs a player is worth to his team above average and ten runs is equivalent to one WAR. Here is the full explanation from Fangraphs but the idea is pretty simple. How many runs above average has a player been worth to his team. Average, therefore, is 0 and anything above 10 is good and above 20 is great. It is also a counting stat, so players accumulate them throughout the season as opposed to wRC+ and wOBA which are rate stats.

I generally like rate stats better, but counting stats are an important comparison. Here’s a quick example:

Miguel Cabrera has a 193 wRC+ and .456 wOBA in 325 PA while Matt Tuiasosopo has a 186 wRC+ and .446 wOBA in 88 PA. Cabrera and Tuiasosopo have very similar rate stats, but you can distinguish their value based on how many PA they have using wRAA. Cabrera has 36.9 and Tuiasosopo has 9.3.

I wouldn’t tell you to use wRAA over wRC+ or wOBA, but it is nice to use in tandem if you’re trying to compare which players have been more valuable to their team, but stick with the rate stats if you care about determining who is actually the better player.

Stat of the Week: Weighted Runs Created Plus (wRC+)

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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)

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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.

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