Hitter Volatility Through Mid-June

Last year I reintroduced VOL, a custom metric that attempts to measure the relative volatility of a hitter’s day to day performance. It is far from a perfect metric, but at the moment it’s what we have.

If you recall, a lower VOL value is better in the sense that it indicates a hitter has been more consistent offensively. However, both good and bad hitters can be consistent, so a lower VOL always needs to be viewed in the proper context. The other thing to keep in my mind is that (as a reader pointed out) there is a strong correlation between VOL and PA/G, as we can see by looking at VOL and batting order position (for 2013):
< ? ??

Now, that isn’t the worst problem, since we see similar relationships between PA/G and overall wOBA and wRC+. Also, one nice feature is that as the average PA/G increases the correlation with VOL gets weaker. What I’ve done in this iteration is to limit the calculation of VOL to just those games where the hitter logged at least three Plate Appearances. When this is done the correlation between PA/G and VOL drops to -.25 — for now, I can live with that.

Here is the VOL leader board through June 16th (min 200 PA):

VOL+ is simple a player’s VOL relative to league average ([VOL/lgVOL] * 100).

Evan Longoria (4.3 PA/G) is currently your most consistent hitter, sitting at 28% better than league average. That’s great news for the Rays considering he has a 154 wRC+ so far in 2013. Not only is producing at an extremely high level offensively, but his performance has been quite consistent, game-to-game.

Contrast that to the Marlins’ Placido Polanco. Polanco (4.2 PA/G) has the sixth-best VOL so far this year, but he’s hitting 41% worse than league average. That means the Marlins are getting a steady dose of bad from their third baseman on pretty much a daily basis.

Sorting the leader board by wOBA also gives us a few interesting comparisons. One is Josh Donaldson and Ryan Braun.

Both players have averaged 4.2 PA/G this season and have logged relatively similar wOBAs (.378 and .376, respectively). However, Ryan Braun‘s VOL is 28% better than league average while Donaldson’s is 7% worse — a 35% difference. So while Donaldson has been marginally better in terms of creating runs, Braun has done almost equally as well with a greater day-to-day consistency than Donaldson.

For those interested, I also ran team-level VOL (sorted by least to greatest VOL):

Batting Order PA/G VOL
1 4.4 0.449
2 4.2 0.488
3 4.1 0.488
4 4.0 0.491
5 3.8 0.517
6 3.6 0.532
7 3.5 0.549
8 3.4 0.544
9 2.1 0.775
Team R/G PA/G VOL wOBA
Royals 4.1 38 0.491 0.295
Tigers 5.0 40 0.505 0.340
Rays 4.8 38 0.507 0.324
Orioles 4.9 39 0.507 0.330
Rangers 4.4 39 0.511 0.325
Mariners 3.5 38 0.517 0.297
Indians 4.8 39 0.520 0.323
Blue Jays 4.6 39 0.522 0.312
White Sox 3.6 37 0.523 0.288
Red Sox 5.3 40 0.526 0.343
Athletics 4.6 39 0.528 0.306
Angels 4.5 39 0.529 0.321
Twins 4.2 40 0.537 0.297
Yankees 4.0 38 0.540 0.300
Astros 3.8 37 0.542 0.298
Marlins 3.2 38 0.551 0.265
Giants 4.4 39 0.552 0.303
Cardinals 5.1 39 0.563 0.307
Brewers 4.1 38 0.564 0.295
Reds 4.6 39 0.567 0.313
Pirates 3.8 37 0.572 0.277
Diamondbacks 4.4 39 0.573 0.302
Dodgers 3.6 38 0.575 0.293
Phillies 3.7 37 0.587 0.308
Padres 4.2 38 0.588 0.295
Rockies 5.2 40 0.588 0.319
Nationals 3.5 37 0.603 0.280
Mets 3.9 39 0.611 0.274
Cubs 4.0 38 0.613 0.300
Braves 4.4 38 0.621 0.310

Tigers’ fans should be happy to see their team at number two on the list, as they have the second-highest team wOBA and do the second best job of replicating that performance on a game-to-game basis. Combine a consistently great offense with that rotation and it’s easy to see why Detroit should make another deep run this post-season.

The Braves offense turns out to be the most inconsistent — slightly better than average, but inconsistent. Now, given their pitching this may not be as big of an issue were they more of an average- to below-average run prevention team. However, it makes you wonder how they will perform in the different context of the postseason.

That’s all for now. Like the velocity loss leader boards, I will be updating VOL throughout the season, hopefully on a monthly basis.

————–

For a more complete list of hitters in 2013, see here.



Print This Post



Bill works as a consultant by day. In his free time, he writes for The Hardball Times, speaks about baseball research and analytics, consults for a Major League Baseball team, and has appeared on MLB Network's Clubhouse Confidential as well as several MLB-produced documentaries. Along with Jeff Zimmerman, he won the 2013 SABR Analytics Research Award for Contemporary Analysis. Follow him on Tumblr or Twitter @BillPetti.


Sort by:   newest | oldest | most voted
olethros
Guest
olethros
3 years 11 days ago

What’s the relationship between VOL and K rate, especially on the team level? Seems like it would be pretty high.

Andrew
Guest
Andrew
3 years 11 days ago

VOL doesn’t mean the results are good or bad, just that they’re consistent.

Mac
Guest
Mac
3 years 11 days ago

Fascinating topic.

One question raised in the article from last year (follow the first link above) was regarding the value of consistency. Thinking of it purely in BA, is it better to go 1/3 every day, or 0/3,0/3,3/3?

I can come up with reasoning either way, but have no strong gut guess either way at this point.

Baltar
Guest
Baltar
3 years 11 days ago

That is indeed the big question. Trying to reason it out would be fruitless. I need to see what, if any, correlation there is to winning before I could care about it.
A very rough eyeball comparison to winning and volatility in the team chart above doesn’t encourage me to think this is worthwhile.

Steve
Guest
Steve
3 years 11 days ago

On a team level, the answer would obviously be 1/3 every day. Obviously 1/3 is high, but even if we took 1/4. Basically the team is losing 3 out of 4, and then winning a blowout. 1/4 from everyone every day, and some days the other pitcher might dodge that and only give up 0-2 runs, but that is a better result than the 0-fers.

On an individual level, it just comes down to situation. One probably isn’t that much “better” than the other, as long as the hits come when they are beneficial.

mjmetro
Guest
mjmetro
3 years 11 days ago

Sorry if this is known, but why are we assuming lower volatility is better for an offense? Wouldn’t it be better to have a team that scores 4 runs in half of its games and 0 in the other half than a team that scores 2 runs in every game (assuming an average pitching staff)?

mjmetro
Guest
mjmetro
3 years 11 days ago

As a follow-up, isn’t it sort of a known that if two pitchers have equal ERA, the one with the higher volatility will be more effective?

MaxPower417
Guest
MaxPower417
3 years 11 days ago

Maybe this is an oversimplification but I think you can look at it like this. Two basic aspects to the game. Pitching and hitting. Both can be considered poor, average or good. Both can be considered inconsistent, somewhat consistent or very consistent.

All told you have 18 different “terms” (ex. consistent average hitting). I think you then have to see how they matchup with each other to see what is best option for one aspect of the game, given the other. For example, if you have consistent poor pitching and an average offence, than you want that offence to be volatile to have any hope of winning more than you lose.

rahi321
Member
rahi321
3 years 11 days ago

Is there any way to measure pitcher volatility?

frivoflava29
Guest
frivoflava29
3 years 11 days ago

Starling Marte at 81+ jumps out to me. If a player has been remarkably consistent at one point or another, will a prolonged slump not really affect the metric?

Peter
Guest
Peter
3 years 11 days ago

Interesting that there’s a total separation between AL and NL on the leaderboard

primi timpano
Guest
primi timpano
3 years 11 days ago

Day to day comparisons will accentuate volatility. I would think a larger sample set, e.g., rolling 10, 20, plate appearances, etc. would give a better measure.

Dan
Guest
Dan
3 years 11 days ago

As a Braves fan, this is not at all surprising.

Josh
Guest
Josh
3 years 3 days ago

I find this very interesting and have always wondered whether there is a way to take a measurement like this and use it better construct a lineup using the basic concept of diversification from portfolio theory. The thought stems from the idea that it seems that teams, rather than just players, are often inherently streaky and volatile. A ideal team therefore would want to minimize its volatility with the highest expected run output not have a bunch of low volatility players but have all highly volatile players, whose performance is lowly or negatively correlated to the other players (likely through other factors such as time of season, home/away etc)

I’m very much a newbie at this stuff so I’m sure this concept has been well thought-out. I guess a very basic example of this occurs with right/left hand platoon splits but I’ve never seen any analysis that attempts to identify other factors that create volatility and could be used on a team-wide basis.

wpDiscuz