As of this morning, the Reds have completed 5.5% of their 2015 season. Here is how they’ve done at the plate:


It is still too early to make much use of these numbers other than as justifications for our own beliefs. For example, I find it hard to believe that Devin Mesoraco will continue to put up a -19 wRC+ after posting a 147 wRC+ last year. But at what point should we be worried? Or at what point does a player’s hitting line start to mean something about their future performance?

First question: how much stock should we put in last year’s performance? Numbers that are consistent from year to year can loosely be interpreted as consistently measuring a hitter’s underlying talent. If there is a wide variation in year-to-year performance (not due to injury) then the metric could be picking up a lot of noise and therefore won’t be very useful for predicting future performance.

In the case of hitting statistics, we might be interested in knowing if a player has a high batting average one year, does that mean the hitter will put up a similar batting average the next year?

Bill Petti researched the question and offers a pretty good answer. His analyses uses eight years of data and only includes hitters that posted at least 300 PA in back-to-back seasons (the full correlation table can be found here).

Here is the table from the article:

Hitter Metric Year to Year Correlation
Contact % 0.90
SwStr % 0.89
Swing % 0.84
K% 0.84
Z-Swing % 0.83
O-Contact % 0.81
Z-Contact % 0.80
BB% 0.78
BUH 0.77
GB/FB 0.77
GB% 0.76
O-Swing % 0.75
ISO 0.73
HR/FB 0.73
FB% 0.73
SLG 0.63
OPS 0.63
OBP 0.62
wOBA 0.61
IFH 0.59
IFFB% 0.56
F-Strike % 0.56
Zone % 0.52
IFH% 0.44
Batting Average 0.41
BABIP 0.35
BUH% 0.24
LD% 0.22

When using previous performance numbers in hitting projections, we need to use metrics that have a high correlation from year-to-year. As pointed out in the Petti data, the numbers that show the highest predictive power are plate discipline stats (these are about 75% predictive, with contact percentage at a 90% correlation).

Based on this table, predicting future performance on batting average is not a very wise thing to do because it changes so much every year (as pointed out in the linked article, this is mostly due to variation in BABIP, which is largely out of the hitter’s control).

In contrast, looking at a players BB%, ISO, and contact numbers are relatively stable from one year to the next.

Second question: How soon can we state that a player is having a good or bad year? Or, when does a “slow start” become more than just a slow start?

Russell Carleton has been publishing data in this area for years, often under the name Pizza Cutter. He’s produced an updated set of numbers. (Warning: That article is math-y, but it’s fun).

The bottom line (taken from Carleton’s article):

Statistic Definition Stabilized at Notes
Strikeout rate K / PA 60 PA
Walk rate BB / PA 120 PA IBB‘s not included
HBP rate HBP / PA 240 PA
Single rate 1B / PA 290 PA
XBH rate (2B + 3B) / PA 1610 PA Estimate*
HR rate HR / PA 170 PA
AVG H / AB 910 AB Min 2000 ABs
OBP (H + HBP + BB) / PA 460 PA
SLG (1B + 2 * 2B + 3 * 3B + 4 * HR) / AB 320 AB Min 2000 ABs, Cronbach’s alpha used, Estimate*
ISO (2B + 2 * 3B + 3 * HR) / AB 160 AB Min 2000 ABs, Cronbach’s alpha used
GB rate GB / balls in play 80 BIP Min 1000 BIP, Retrosheet classifications used
FB rate (FB + PU) / balls in play 80 BIP Min 1000 BIP including HR
LD rate LD / balls in play 600 BIP Min 1000 BIP including HR, Estimate*
HR per FB HR / FB 50 FBs Min 500 FB
BABIP Hits / BIP 820 BIP Min 1000 BIP, HR not included

From these numbers, it looks like the first two months will provide a sufficient number of plate appearances to start making judgments about strikeout rates (watch out for Jay Bruce, currently at 37 plate attempts and a 29.7 K%), the power stats (ISO, HR/FB, HR Rate), and walk rate (only 120 At-bats, one of the lowest).

At the mid-season mark, slugging, hit by pitch, and singles rates start to come into focus.

This data demonstrates that it takes a long time for some noisy stats, like batting average, to stabilize. This helps to underscore that imprecise measures (or those that rely on many conditions, such as batting average) take a long time to become a reflection of underlying talent.

Now, this second chart is an imperfect fit for our purposes because it provides an answer to a similar, but not completely comparable question to the one we are asking. This chart lets us know when a metric stabilizes, but does not provide an answer for when we can use that metric to demonstrate a difference in performance. For example, if Joey Votto has a higher strikeout rate this season than his career through 60 at-bats (he doesn’t, by the way), does that mean that he is going to have a poor season?

No, these results are not refined enough because they are about measuring the stabilization of a number, not the difference between two periods of time. And this gets us to the staggeringly difficult question of how to falsify a probabilistic estimate. Furthermore, how can we both incorporate a player’s past performance (when it came from a different season, perhaps different team, perhaps different age) into their current performance? Are they really different, or is it random variation?

Statisticians have struggled with this question for quite a long time, so no sense trying to resolve it now.

Yet what we can say is this: within the first two months, players’ walk rates, strikeout rates, and power stats are instructive, but not definitive. These are important stats because the two major “skill” areas for players are controlling the strikezone (K%, BB%) and power (ISO). Combining this with a divergence in contact rates from one year to the next (first chart) would point to a durable decline that will continue down the stretch.

Playing better is always preferable to playing poorly, but it takes awhile to know when a set of outcomes becomes representative of a larger trend.

21 Responses

  1. wkuchad

    Am I reading the chart correctly? Phillips has both the worst walk % and best strikeout %. He must be putting a lot of balls in play.

    • Steve Mancuso

      Good point. He’s swinging early in the count, usually at the first pitch. That drastically reduces walks and strikeouts.

  2. Steve Mancuso

    Interesting data. One point that stands out to me from the first chart is how much more reliable past walk rate ( BB%) is than past batting average for predicting how a player will perform in the future.

    That’s a crucial lesson for a GM. If you want players with good on-base skills, look for ones with track records taking a lot of walks, because chances are they will continue to do that. The data shows the perils of signing a player based on batting average (cough, cough, Willy Taveras) and hoping AVG will serve as a big component of OBP. Players can’t necessarily repeat their AVG performance – in large part because of its dependence on luck factors (BABIP) that vary from year to year.

    The durability of the BB% stat is another reason organizations should stress that skill in their development.

    Needless to say, the Reds have been terrible generally in this regard, both in terms of development and acquisition.

    • bhrubin1

      I totally agree with this point, but would offer one caveat, which is that while BB% is much less susceptible to BABIP luck, it is also, one would assume (I have numbers here but would love to see somebody look at them) much more susceptible to variability in the pitching a player faces. Therefore, might we not expect a player whose BB% stays relatively stable while he’s on the same team (facing the same pitching) to see that same stat fluctuate a bit more if he moves to a new team (especially one in a different division/league)? Just a thought.

      • Steve Mancuso

        That’s a good point. The league approach to a hitter could change and that might affect the BB%. The stat is only correlated at 74 percent, so there’s room for deviation. Also, as hitters age, you start to see their BB% go down as they cheat a bit to get around on a fastball.

        But the broader point is still there. BB% has less variation from one year to the next. If a league changes approach to a hitter it can also affect his AVG.

  3. BigRedMike

    The SO and BB %’s for Byrd are not encouraging. Pretty low BABIP at this point.

    Phillips is swinging a lot, interesting that he has ISO of 0, yet, bats #4

  4. B-town Fan

    In terms of ISO all of Phillips hits have been singles so far.

  5. Matt WI

    Encouraging that Frazier and Mes have crazy low BABIP right now. That’s going to get a lot better. Throw Byrd in there too.

  6. zaglamir

    That chart is one I’ve always wished I had the time to make. Fascinating statistical analysis. I’m going to find Mr. Pizza Cutter and buy him a beer.

  7. Steve Schoenbaechler

    I love this information. But, still, as it shows, there is still no guarantee. 0.90 correlation for “Contact %” means that there is a 90% positive correlation between “contact %” and higher batting numbers. But, what about the other 10%? Also, the numbers don’t tell types of situations. Were the numbers taken in high-leverage situations? Low leverage situations? Men on base? Trying to drive in the winning run? Starters? Bench players?

    Don’t get me wrong. I love these stats. Much better work presented with this than I have seen. However, there is still something to be said about situational settings. For instance, if I have my best hitter coming up to bat with men on 2nd and 3rd, 1 out, down by 1 and someone who thinks they are a stud fireballer on the mound so they are going to pitch to him, there’s no way I want my best hitter to go up there thinking “BB” and having the next batter in the order, a worse batter, trying to win the game with force out/double play potential at every base. I would want my best hitter ready to swing at anything close in attempting to drive in those runs and win the game. Given situations like that, does the player perform, a la (if I may) “Michael Jordan”-like game winning shots?

    Or, the thing many here have said, about how Chapman should be coming in to get the other team’s best hitters out, regardless of 7th, 8th, or 9th innings, instead of just coming in the 9th inning. All closers, they come in the 9th, they get their save stats padded by potentially pitching to the other teams worst hitters, at a time where the other hitters essentially have to be swinging the bat and, thus, to the pitcher’s advantage. But, the closers are suppose to be the best relievers the teams have? Then, why aren’t they being used in the higher leverage situations?

    Or, in turn, with this same situation, the play calls for the pitcher to IBB the batter. That IBB goes against his walk rate. Should it? Nope, but it does for many, because they only consider the numbers and not the situations.

    The games still have to be played.

    • lwblogger2

      Funny you mention that specific scenario. Bumgarner challenged Goldschmidt last night in the later innings w/ 1B open. The first fastball ran in a bit on Goldschmidt’s hands and he missed it, fouling it off. The next one got more of the plate and also with Goldschmidt knowing he was going to be challenged he was fully ready for it. It ended up way back in right field for a 3-run HR and a 4-1 D’backs lead. Bumgarner got off the hook for the loss because the Giants came back and forced extras, losing in the 12th.

      • lwblogger2

        Whoops, I mean way back in LF, not RF.

    • jdx19

      Read the “math-y” article Michael linked and most of your questions will be answered.

    • jdx19

      “The games still have to be played.” Not sure why that needs to be stated. No person, fan or GM, would ever state anything contradictory to that.

      The analysis of stats helps you make informed decisions instead of guessing, which is what people do in the absence of data.

  8. jdx19

    You saw my comment last week! Either that, or we are metaphysically linked! Either way, great article!

    Very important for every baseball fan to understand this sort of info.

  9. kmartin

    Mike, this is an excellent and informative post. Are statistics such as Contact % and SwStr % available for retired players? I would like to see these numbers for Rose and Morgan and see how they compare with Votto.

    • Michael Maffie

      Thank you for reading the article. I have looked around the internet over the past few days to find those numbers for players before 2002 but cannot find them.

      Pete Roses’ interview on opening day was interesting. Rose said that Votto reminds him of Joe Morgan because Votto rarely swings bad pitches. Rose went on to contrast this to himself, as if chasing bad pitches was a defect in his game.

      I have read that some teams are starting to experiment with biometric data to see if they can associate certain traits to player performance (such as better pitch recognition).

      Sadly, I think the Reds are more interested in RBI machines than MRI machines.

  10. Matt

    I keep coming back to this article for reference because it is excellent.

    It’ll be interesting to see if Votto’s drastically lower strikeout rate this year (13.8% as of this post, versus 18.4% career) is something that will indicate future performance at all (and if K rate stabilizes around 60 PA as you’ve posted, we’ve already reached the point where a lot of the SSS noise is removed). If it does, holy crap, does that mean Votto’s getting even *better*?