A few months ago, I read an article on FiveThirtyEight by Rob Arthur about a pitcher’s ability to suppress hard contact. One of his conclusions was that some pitchers are better at limiting hard contact than others. This makes good sense, and we can see that suppressed contact in guys like Johnny Cueto and Chris Young. He used the Statcast dataset to find, in MPH, how much faster or slower, on average, a ball would come off the bat from a given pitcher. While the Statcast dataset is still a work in progress, and the metrics may not be super reliable at the moment, the basic idea that pitchers can suppress contact quality, and therefore hits, remains.

That’s all fine, but these statistics would only be useful if they are predictive. I want to see if contact quality is consistent from year to year. I went back through the FanGraphs leaderboards and pulled pitcher seasons from 2010-2014 with at least 200 balls in play. I chose 2010 as the start year because it was the first season Baseball Info Solutions (BIS) used an algorithm to determine contact quality, instead of the video scouts’ judgments. I wanted to see how the Hard% compared from one year to the next, so I took the 20 best and 20 worst pitchers by the metric in each year and matched them with the next year’s data.

Now, since I used a 200 ball in play cutoff, some of the top 20 for a given year did not qualify for the next year, so I only used pitcher seasons that qualified in consecutive years. I did the same thing for Soft%, but not Med%, as nobody cares about who gave up the least medium contact. I had to do all this relative to the league average in that season because league average changed drastically each year (league average Soft% was .1716 in 2010 and .2417 in 2011 for pitchers in my sample). Starting with Soft%:

This table is not the easiest to read because, but the columns to focus on in each table are Diff, Diff Next, and Change. Diff is the difference between the Top/Bot 20 average and the league average for that year. Diff Next is the difference between how those same pitchers perform the next year and the league average for next year, and Change is the difference between Diff and Diff Next.

On average, the top 20 pitchers by Soft% had a Diff of .0436 in year one, and .0094 in year two. In other words, they generated 24.2% more soft contact than average in year 1, and only 5.1% more the next year. Similarly, the bottom 20 pitchers generated 21.3% less soft contact in the first year and 3.0% less the next year.

Here are the same results for Hard%:

The 20 pitchers who allowed the most hard contact allowed 14.9% more than average in year one, but only 3.1% more in year two. The 20 best pitchers by Hard% allowed 16.0% less than average one year and 3.9% less the next.

It is obvious that some regression should be expected for these over- and under-performers. For both metrics, the top and bottom 20 pitchers in one season come much closer to average the next. These quality-of-contact metrics are similar to BABIP in that they are highly volatile from year to year.

The numbers, however, don’t come all the way back to league average in year two. The top 20 pitchers stay slightly above average the next year, while the bottom 20 guys similarly stay slightly below average. This suggests, which is often the case, that a year of these highly variable quality of contact metrics can still carry some predictive value. It is hard to say just how much predictive power they have without knowing how much to regress someone’s Hard%, for example, given some number of balls in play.

While there is some predictive value in a season’s worth of batted-ball data, there isn’t much, so it’s hard to attribute an extremely high Soft% to talent. More likely, these metrics behave similarly to BABIP, in that one fortunate season is not enough to determine the talent level of a player. Batted-ball profiles and BABIP are closely connected, as hard-hit balls tend to fall for hits more often than softly-hit balls.

Groundballs, line drives, and fly balls also have their own expected BABIPs, so we could combine this entire batted-ball profile and come up with an expected BABIP for a pitcher, both within a season and for a career. While we know how many groundballs and how much soft contact a pitcher gives up, we don’t know how many soft groundballs a pitcher gives up. Ideally, we could classify each batted ball into flight type and speed. This is what Statcast tries to do with its launch angle and launch speed data, but that system still has a ways to go. For now, don’t put too much stock into a pitcher’s ability to suppress hard contact in a single season, the same way we don’t put too much stock into a pitcher’s low BABIP for the year.

In the past few years a number of high profile pitchers have gone under the knife for Tommy John surgery (TJS). This surgery involves reconstructing the ulnar collateral ligament (UCL) in the throwing arm to re-stabilize a players elbow. I’ve heard a few stories about TJS — firstly, pitchers who get the surgery are able to throw harder after the procedure and another where college pitchers were voluntarily undergoing the procedure and sacrificing a year of pitching due to the belief that they would be able to throw harder or have more stamina. Whether either of these are actually true I have no idea, and I didn’t do any digging to find the answer. Instead I wanted to take a closer look at some pitchers who’ve undergone the procedure in the last couple of years and compare their performances before and after the surgery. In the table below I’ve included 4 players who missed the entire 2014 season or a significant portion of it. Matt Harvey underwent the procedure in October of 2013 while the other pitchers had the surgery sometime in 2014.

In 2013 all of the pitchers had pretty good years. They all made at least 26 starts and threw at least 150 innings. Fernandez and Harvey were both striking out more than one batter per inning, while Moore and Corbin still posted very respectable numbers. Now Harvey and Corbin didn’t pitch at all in 2014 and the other two suffered their injuries early in the 2014 season. Matt Moore only pitched 10 innings so it is tough to draw any conclusions due to small sample size, while Jose Fernandez threw 51.2 innings before he was shut down. His 2014 season was looking very promising posting very high K/9 numbers with a low ERA and his FIP and xFIP were even more favourable.

Now lets jump ahead to 2015. If you want to check over their 2015 stats they are in the table above. I’m not going to regurgitate them for you, but I will give a quick synopsis of each player. Harvey is having an excellent first year in his recovery, and in limited sample Corbin and Fernandez are also throwing really well. Matt Moore has had a season to forget so far, but he is just about return from a stint in AAA where he posted pretty strong numbers so the jury is still out.

Any time a player is coming off a major injury it is entirely within reason that psychological issues, fitness/conditioning or lack of practice has an effect on their performance. Without any first-hand knowledge of their unique situations fans always want a pitcher to just step right back in and perform at previous levels without any decline in performance. It’s tough to only compare stats from a before and after season and say with confidence whether a pitcher has lost any ability. So I wanted to go a step further and look at some PITCHf/x data and take a look at how their fastball, breaking ball and change-up velocities have changed, as well as any changes in the movement of their breaking balls.

Let’s start off with fastball velocities. As you can see from the table above Matt Moore has data going all the way back to 2011. His fastball velocities have decreased each year which should be a cause for some concern. The remaining 3 pitchers have all shown increased fastball velocities since their rookie years. Whether this is proof that TJS has an effect on increasing pitch speed I’m not sure and I’m not going to speculate, but I would welcome any comments from people who may have some theories. I’ll let you read through the rest of the table, but in general, Moore is showing decreased speed for all of his pitches this year and everybody else is throwing their stuff just a little bit harder.

OK now that’s enough looking at tables, let’s move on to some pretty graphs. Who doesn’t like a nice graph? So the first one from the set of pitch trajectories that I’m going to show you are the mean fastball trajectories from each pitcher with different colours showing a trajectory from different years. Now I’ll admit that I don’t know much about trajectories and how to analyze them, but the interesting part that I found from these was the release point. Matt Harvey has been remarkably consistent with his fastball release point; Fernandez and Corbin haven’t changed all that much either. But look at how Moore’s arm slot has dropped in the last three years. Now again I’m certainly no expert in pitching mechanics but something seems to be going on there that might be related to the drop in velocity that we saw above.

On to the curveballs! There doesn’t seem to be too much going on with arm slot changes here. Fernandez looks like he changed up his arm slot from the 2013 season and his release point has been almost identical in 2014 and 2015. Harvey on the other hand has slightly dropped his arm, but from my standpoint it doesn’t seem too significant.

Lastly we come to the sliders. Look at Harvey and Corbin! If the pitches weren’t different colours it would be very difficult to tell them apart based on the release point. Moore seems to have dropped his arm slot from the 2013 season, but his release point has remained the same the last 2 years. Corbin is definitely targeting the bottom corner of the strike zone with his slider; it looks like he may be trying to get hitters to chase. Moore and Harvey look like they are also doing a good job of keeping those pitches down in the zone.

For those of you who are not too familiar with stats, I’m going to give you a quick lesson about confidence intervals. In the plots below I’ve included the 95% confidence intervals. Basically if the ends don’t overlap from the coloured bars you can consider the differences from year to year to be significantly different statistically (boring!). On to the fun stuff — the year after Fernandez and Harvey had TJS, the spin rates on their curveballs are considerably lower. I know it’s a little tough to tell if the bars are overlapping on Harvey’s curveball, but trust me, the lines aren’t overlapping. Maybe both pitchers are a little worried about their elbows or maybe it’s just advice from the doctor, trainers, coaches, their parents, who knows. Harvey is also showing a decreased spin rate on his slider from 2 years ago. If we ignore 2013 for Moore, then Moore and Corbin have maintained consistent spin rate from their last season.

And finally we get to our last plot; hopefully I’ve kept you all interested up to this point. This is looking at the pitch movement (in inches). The decreased spin rate illustrated above for Fernandez and Harvey’s curveball has also led to less movement. Fernandez has lost just a little over a 1/2 inch from his curveball since last year, but about 1.5 inches from his 2013 curve. That seems like an awful lot, but I don’t know if there has been any change in the effectiveness of his curveball in that time. Oddly enough after TJS the sliders are showing more movement. Maybe that elbow is a little more stabilized, or maybe it has something to do with increases in velocity, but unexpected on my end to see that.

From what I can tell Harvey, Corbin and Fernandez haven’t lost a step. Moore is somewhat of a mystery though. It’s tough to tell if anything has changed, but he only threw 10 innings last year so any direct comparison to last year would be useless. I’m a little alarmed at Moore’s decreasing fastball velocity since 2011. He’s going to need to start relying on his secondary pitches if he’s going to be successful going forward. But the basic conclusion that I’m going to draw from this analysis is that players are able to come back from Tommy John and still be effective. I’m sure there are articles that argue in favour and against my conclusion, but by showing you some information about pitch speed, release point and spin rate you can go ahead and make you own conclusions.

Prior to the August, 2015, non-waiver trade deadline, the Toronto Blue Jays sent their leadoff hitter Jose Reyes to the Colorado Rockies for Troy Tulowitzki, a classic middle-of-the-order bat. Everyone assumed from his career power numbers that Tulowitzki would slot in the heart of the Jays order, but with Josh Donaldson, Jose Bautista, and Edward Encarnacion already comfortably set at 2-4 (over 200 RBIs between them at the time) they instead used him in the vacated leadoff spot. The move seemed to work as Tulo went 3 for 5 in his first game, and the Jays proceeded to rattle off a tidy 11-0 streak with their new top-of-the-order guy.

Troy Tulowitzki Shortstop B/T: R/R .297 / .370 / .510 29 HR 100 RBI 8 SB

José Reyes Shortstop B/T: B/R .290 / .339 / .432 12 HR 65 RBI 50 SB

One doesn’t mess with success, but everyone knows Tulowitzki is not an ideal leadoff hitter, never having batted there before in his 10-year MLB career, and with all of 3 stolen bases in the last 3 seasons. His above-average pop suggests a traditional run-producing spot: 29 HR and 100 RBI career numbers over an averaged 162-game season (Baseball-Reference.com), but with the Jays on a 22-5 tear, Tulo, touch wood, wasn’t moving anywhere.

A leadoff hitter naturally gets more at bats per season, one reason Jays manager John Gibbons gave for putting Tulowitzki at the top of the order, given his career .297 BA and .370 OBP. But tradition and common sense dictate that top RBI men are more valuable with men on base, impossible for a leadoff man in the first inning, and presumably sub-optimal afterwards. As Tulowitzki’s new teammate 3B Josh Donaldson noted in the midst of an August run that saw the Jays go from 6 back of the Yankees to 1 1/2 up in the AL East, “I feel like every time I’m coming up I have someone in scoring position or someone on base.” Exactly.

Fine-tuning a lineup is an argument for the ages, but can we determine where a power hitter should bat, where his numbers best fit 1 to 9? Should high-average batters hit before the sluggers, or should we just bat 1-9 in order of descending batting average (or OBP)? Can we calculate how to arrange a team’s lineup to maximize the optimum theoretical run production?

Enter Monte Carlo simulations, used to model the motion of nuclei in a DNA sequence, temperatures in a climate-change projection, even determine the best shape and size of a potato chip. In Do The Math!, Monte Carlo simulations were used to calculate where a Monopoly player will most likely land (Jail and Community Chest, followed by the three orange properties: St James, Tennessee, and New York), and whether to hit or stick in Black Jack against any dealer’s up card.

In some cases, algebraic probabilities are difficult (using Markov chains, a continuously iterative system with a finite countable sample space), whereas brute force computation does the trick over a large number of trials. If a picture is worth a thousand words, a simulation is worth a thousand pictures.

BOO V1 (Batting Order Optimization Version 1) is a Monte Carlo program written in Matlab that randomly selects a hit/out event over a 9-inning, 27-out game, averaged over a large number of games, e.g., 1 million. It uses a flat lineup where all hitters have a .333 OBP (roughly the Jays average), but doesn’t include errors, hit batsmen, sacrifices, double plays, stolen bases, etc., or opposing pitchers’ numbers. (In Part II, I will include the hitting stats of a real lineup: 1B, 2B, 3B, HR, BB, K, GO/AO.)

The mathematical guts are fairly simple, essentially a random number generator and some modulo math (think of leap-frogging 3 or more chairs at a time in a circle of 9), and elegantly captures some interesting trends, in particular, the distribution of end-game batters 1-9 and thus the most likely batter to end a game. From such a simulation, we can calculate where best to slot a team’s best hitter to maximize his chances of coming to the plate with the game on the line, another stated reason for putting Tulo in the Blue Jays number 1 spot.

Figure 1a shows the distribution of batters faced (BF) over 1,000,000 simulated BOO games, where the most likely end was 40 batters faced followed by 39 and 41 (the 3-5 hitters), as might be expected with a hard-wired OBP = .333 (binomial p = .33). It seems the custom of having your clutch hitters in the 3-5 slots matches the computational results.

Figure 1a: Distribution of # of batters faced   Figure 1b: Distribution of end-game batters

Interestingly, however, the leadoff hitter doesn’t end a game more often than a middle-order batter. Figure 1b shows the distribution of end-game batters (EGB) for a 1-9 lineup, and is perhaps counter-intuitive. In fact, the number 2 and 3 hitters are more likely to end a game than the leadoff hitter, while there is an obvious dip 3-7. Table 1 shows the frequency of end-game batters 1-9 (number and percentage).

Table 1: Number of games ended and percentage versus lineup position (OBP = .333)

Initially, I expected a constant drop-off from 1 to 9, or perhaps following some form of a Benford’s Law distribution, for example, in the wear pattern on a ATM pad or the leading digit in a collection of financial data (1 appears about 30%, 2 about 18%, 3 about 12%, 4 about 10%, . . . , and 9 about 5%). Note, if the data were randomly distributed, each number would appear 11.1% or 1/9. But the modulo aspect of a repeated baseball lineup creates another distribution, one that has a clear maximum after the leadoff spot and a mid-lineup dip at batter number 7.

Of course, the leadoff hitter will always have more plate appearances over an entire season, but somewhat surprisingly does not end a game more often. Table 2 shows the number of at bats 1-9 averaged over a 162-game season (I have assumed 8.5% of plate appearances are walks). As can be seen, the leadoff hitter gets about 130 more ABs than the number 9 hitter, or 21% more per season, reason enough to put your best hitter at the top of the order. From one batter to the next, however, the difference is only about 17 ABs (monotonically decreasing), about an extra AB every 10 games. Not that much difference one spot to the next.

Table 2: Number of ABs and percentage ABs over 162 games (OBP = .333)

Using BOO, we can also analyse how the EGB distribution changes for a good and a bad team, modelled using an OBP of .250 and .400. The results are shown in Figure 2 including our .333 OBP team. Here, it seems that the lineup order matters more on a bad team than a good team (a practically flat EGB). Indeed, it is often said that you can run any lineup out with a good team. Conversely, losing teams are always juggling their lineups to find the right mix.

Figure 2a: Distribution of # of batters faced   Figure 2b: Distribution of end-game batters (OBP = .250, .333. .400)

Of course, baseball is not just statistics over a large number of sample-sizes (or simulations). Baseball is played in bunches and hunches. It would take a little over 400 years to play 1,000,000 games in a 30-team, 162-game schedule. Matchups, streaks, situational hitting, and team chemistry may be more important than any theoretical trends. And, of course, a real, non-flat, batting lineup (which I’ll look at in Part II).

In an actual BF and EGB distribution for the 2014 Toronto Blue Jays and their opponents over a 162-game season, we see the small-sample versions of our super-sized theoretical distributions (Figure 3). The actual BF distribution is comparable to the theoretical binomial/Gaussian BF, though positively skewed, showing the effect of blowouts, not adequately covered in the hit/out simulation. The EGB distribution seems quite random, but late peaks may indicate the use of pinch hitters in the closing parts of a game. It is also interesting to note that BOO “throws” a perfect game about once every 10 seasons, a bit less than the official 23 over the last 135 years.

Figure 3a: Distribution of # of batters faced   Figure 3b: Distribution of end-game batters (2014 Toronto Blue Jays and opposition)

So do the calculations mean anything? According to the numbers, your best hitter should bat 2 or 3, that is, if you want him coming up more often with the game on the line. In “The Batting Order Evolution,” Sam Miller noted that “the anecdotal evidence is strong” to put your best hitter in the number 2 spot. The worst spot for heroics is number 7.

Furthermore, a classic run producer such as Troy Tulowitzki shouldn’t bat leadoff, something the Jays found out after he struck out 4 times, almost a month to the day after acquiring him. Dropping him to the number 5 spot, the manager John Gibbons stated, “Maybe this’ll jump-start him a little bit.” Or maybe, he saw the wisdom of inserting the 2014 NL hit leader and speedster Ben Revere in the leadoff spot and using Tulowitzki’s power in a proven RBI position.

Mind you, with a scorching hot lineup that has scored 100 more runs than the next-best hitting team, it may not matter who bats where. That is, if the game is on the line.

Do The Math! is available in paperback and Kindle versions from the publisher Sage Publications, on-line at Amazon.com, and on order at local book stores. Do The Math! (in 100 seconds) videos are on You Tube.

Before I get started, just a quick note: I have created some graphics to aid in the explanation of my work, but was unable to integrate the graphics into WordPress. To view a pdf of the post with graphics included click here. (Also note that you won’t be able to click on hyperlinks in the pdf but the URLs of each link can be found at the end.) Otherwise, please enjoy the post below without the graphics.

I set out the other day to try to develop an equation that can predict, with reasonable accuracy, the number of runs a team will allow. I intended to use Fielding Independent Pitching Minus (FIP-) and Ultimate Zone Rating (UZR) (see my blog post to come on this research for why I used those two statistics) but noticed one position that had gone unaccounted for thus far: catching. UZRs don’t exist for catchers because UZR is based on Outfield Run Arms (ARM), Double-Play Runs (DPR), Range Runs (RngR), and Error Runs (ErrR)¹, none of which are among the most relevant statistics for catchers. While catchers do play a role in bunts, popups, and plays-at-the-plate, the most important aspects of the position, and where the most variability exists, is in the baserunning game. Blocking pitches and throwing out baserunners are the responsibilities of a catcher that have the greatest impact on the game.

Obviously I’m far from the first one to set out to quantify a catcher’s impact on the game. In fact, incredible progress has been made by the likes of The Fielding Bible who calculate the metric Stolen Base Runs Saved (rSB) to measure a catcher’s effect on stealing and Bojan Koprivica who calculates Passed Pitch Runs (RPP)² to measure the catcher’s ability to block errant pitches³. While both of these are good metrics* to measure a catcher’s value, they will never be adequate predictors in a team-based context because they don’t account for the other half of the equation: the pitcher. Catcher baserunning defense will forever be connected to the pitcher. Stolen bases are dependent upon the lead and jump that the runner gets, both of which depend on the pitcher’s pickoff move, predictability on when he throws over and when he goes to the plate, and the speed of his delivery. Likewise the number of bases taken via wild pitches and passed balls depends on the accuracy of the pitcher.

* I’m skeptical on the validity of the Stolen Base Runs Saved metric because it hinges on the ability to use a pitcher’s past history of allowing stolen bases as a baseline for how easy or hard they make it for runners to steal. The way this would work would be if the stolen base attempts off a pitcher were spread out over a large number of catchers with varying abilities so that the ability of the catcher on a given stolen base attempt would be random. However, many pitchers have pitched mostly to just a few catchers, which would not achieve the necessary randomness. For the time being, I’ll take rSB’s acceptance by the baseball community as sufficient vetting but if nothing else I would point to this as another reason why a new metric is needed.

Where I differ from my predecessors is what I decided to do with this undeniable interconnectedness. They tried to control for the pitcher by measuring the variation catchers have from past averages. This is necessary when searching for a stat to measure a catcher’s independent value. I instead decided to take my catching metric and turn it into a metric that measures both the pitcher and catcher together (hence Battery Allowed Baserunning). In doing so, the metric lost its capability to assess either player’s individual impact, but gained the ability to measure their combined impact on the team. It also became more innately accurate because it is strictly a measure of observable events, rather than an experimental determination. No matter how impeccable the statistical procedure, any attempt to extract additional meaning or relevance from the numbers creates the risk of error.

Enough with the preview, lets get into it. I assembled data from the years 2003 to 2014 (every complete season with UZR data because I intended to go back with these number to my original inquiry). I selected the statistics Stolen Bases (SB), Caught Stealing (CS), Wild Pitches (WP), Passed Balls (PB), Pickoffs (PK), and Balks (BK) as those that resulted from the battery and set about combining them.

I aggregated Wild Pitches and Passed Balls because the only difference between the two is the blame assigned by the official scorer. BAB measures the impact of the battery and being that both WPs and PBs are attributable to the battery, both should be included. Furthermore, a given ball that gets by the catcher and allows runners to advance is completely random as to if it is a PB or WP—that is to say one does not happen more or less often in a given situation (eg. Mostly with 1 runner on base; scarcely with two outs) than the other. As such, they can be equally weighted. By the same logic I added balks to this sum. Oversimplified, all three are accidents by the pitcher or catcher that aren’t influenced by the situation. I call this sum of Wild Pitches, Passed Balls, and Balks non-Stolen Base Advancement (nonSBadv).

I stressed the randomness of the Wild Pitches, Passed Balls, and Balks because Stolen Base Attempts do not occur randomly. Rather, their likelihood depends upon the situation. A wild pitch is equally likely to occur with the bases loaded as when there is just a runner on first. However, a triple steal is nowhere near as likely as a runner on first stealing second with no one else on base. Likewise a balk with a runner on second is just as likely to occur with one out as it is two outs. On the contrary, the tendency of a runner to steal is influenced by the out total. For example, runners are generally less likely to steal third with two outs than with one because with one out reaching third gains the advantage of being able to score on a sacrifice fly or a ground out but that doesn’t work with two outs. The same goes for the score of the game, the inning, and so forth but you get the idea. The point is Stolen Base Attempts and non-Stolen Base Advancement need to be treated separately because the odds of the former is influenced by the situation while the odds of the latter not.

As for combining the last three stats, Stolen Bases, Caught Stealings, and Pickoffs, the easy part was the latter two. I added them because they have the exact same result—increasing the out total by one and removing a baserunner. I called this sum Baserunning Out (BRout).

The only possible issue with this is that catching a runner stealing also keeps the runner from advancing a base while pickoffs don’t always do this. Sometimes, and I would argue most of the time, pickoffs happen because of a bad jump or abnormally large lead due to a runner’s plans to steal on that pitch or soon thereafter. Furthermore, many pickoffs occur when a player doesn’t even try to get back to their former base on a pickoff throw and are thrown out in the ensuing run-down. This situation is almost precisely the same as a stolen base attempt. Other times, however, the pitcher just has a good move and catches the runner off guard, despite the runner having no plans to steal. I didn’t have a good way to account for this, being that pickoffs aren’t classified in any way. Since pickoffs are far less common than caught stealings I decided to just let this one slide.

This leaves me with just one stat unaccounted for: stolen bases. I couldn’t simply subtract baserunning outs from stolen bases because they are not the same. A caught stealing increases the out total, while a stolen base does not decrease it; a stolen base advances a baserunner a base while a caught stealing doesn’t just move a runner back a base, it eliminates them entirely. For example, given a runner on second with less than two outs, if the runner steals third the advantage is that he can now be scored on some groundouts and flyouts and all non-Stolen Base Advancements. However, if the runner is thrown out at third all opportunities for the runner to be scored via a hit and all opportunities that include advancing over multiple at-bats are lost. The latter loss is much greater than the former gain.

To measure that exact difference I turned to run potentials. The stolen base run potential measures the additional runs, on average, that are scored after a stolen base as opposed to the former state. The same goes for caught stealing except for that those numbers are always negative because fewer runs are expected to be scored after a caught stealing than otherwise would have been. Over the period 2003-2014, the SB run potential was always 0.2 while the CS run potential was anywhere between -0.377 and -0.439, depending on the year. (Since I earlier deemed Caught Stealings and Pickoffs to be statistically equivalent I allowed the CS run potential to represent both.) For each year I divided the loss in run potential from a caught stealing by the gain in run potential from a stolen base. In essence, I did this to use the run potentials as a ratio. The ratio I created was the ratio of loss from a caught stealing to the gain from a stolen base.

As I said above, a stolen base results in a one base advancement while a caught stealing results in a hindrance that is much greater, much greater than one base. By multiplying the above ratio/dividend by each Baserunning Out total, those totals become the overall loss in bases, the same unit as stolen bases. Now the new, weighted BRout total can be subtracted from (or added to if you keep the negative sign in CS run potential and your ratio is thus negative) the stolen base total. This quantity is called Net Stealing (NS).

One important question I’m sure you have is how to approach decreased stolen base attempts against well-respected batteries. This is a question I wrestled with quite a bit. The explanation that finally made sense was this: think about the advantage of a well-respected battery as that of a pitcher with an excellent pickoff move. Yes, from time to time runners will be picked-off but that’s not the purpose of the pickoff. The purpose is to keep the runners close so that they rarely attempt to steal bases—that they advance the minimum amount, only the amount allowed by hits, walks, HBPs, etc., and that when they do try to steal they are at such a disadvantage that they get thrown out. Applying this back to the battery as a whole, the best battery is the one that has a negative Net Stealing value (for whom attempting to steal against will have a negative net impact in the long run), but not necessarily a hugely negative Net Stealing value because the original intent of a strong battery is to keep runners from advancing, not to get them out. A Net Stealing value of 0 should be regarded as success because it means no bases were taken. The original purpose was achieved. A well-respected battery’s Net Stealing is bound to be low because NS is a counting stat, measuring the total bases taken against a given battery and you can’t take very many if your number of attempts is low. Therefore, the Net Stealing values of well-respected batteries does not have to be adjusted for low numbers of stolen base attempts because the advantage this entails is already reflected in their Net Stealing consequentially being a low number, be it a low positive one or a low negative one.

The final task is to merge non-Stolen Base Advancement with Net Stealing. This is not a task for a simple sum because Net Stealing’s unit is bases, as I painstakingly ensured above, but non-Stolen Base Advancement’s is not. A wild pitch with the bases loaded is a single wild pitch, as is a wild pitch with just one runner on base. The glaring issue is that the former situation resulted in three bases being taken while the latter in just one base, but both are valued as one unit, one nonSBadv. To solve this I return to a concept I referenced earlier—that nonSBadvs are totally random and no more or less likely based on the number of runners on base (ROB). Therefore, the total number of bases taken on nonSBadvs should mirror the average number of runners on base at a given moment. Although, I must clarify this to the average number of runners on base at a given moment, provided that at least one runner is on base. A wild pitch with the bases empty is not reflected in the box score so the numbers would be skewed if I included those situations as being possible scenarios for a nonSBadv. Because the only data available was from an offensive perspective—tracking the number of runners on base when each hitter was at-bat, I had to settle for creating yearly league averages to be used for every team. I did this by taking the total number of runners that were on base during plate appearances and dividing that by the number of plate appearances that had runners on base.

Once I had the average ROB with ROB I multiplied that number by each nonSBadv value to make the unit bases and therefore able to be combined without unintended weighting to my Net Stealing value.

In a perfect world I would have used team-specific average runners on base values, because teams with better pitching staffs aren’t at quite as much risk on nonSBadvs because there are typically fewer runners on base to advance than there are for teams with worse pitching staffs. At the end of the day I didn’t lose too much sleep over this because it was an approximation either way. It’s conceivable that a team that allowed very few runners on base was miraculously more prone to nonSBadvs with more runners on base or vice-versa so while the approximation would have theoretically been slightly better, it wasn’t a matter of life-or-death.

Once I had weighted non-Stolen Base Advancements by multiplying it by the average number of runners on base, I simply added that value to Net Stealing to create Battery Allowed Baserunning.

So there you have it: Battery Allowed Baserunning (BAB). The last thing I want to talk about is its applications, shortcomings, and potential improvements.

Applications: As I stated at the beginning, this statistic was originally conceived of in the search for a metric that measured the impact that a catcher (and eventually a battery) had on a team defensively. For now, I believe this stat belongs in the team defensive category for the same reason that outfield assists and double plays are measured in a team context, even though they only involve a couple of players: because it measures the skill/weakness the team as a whole has in this discipline. It could, theoretically, be used as an individual stat belonging to both the pitcher and catcher, although it would need to be understood that a tremendous confounding variable exists in the given player’s batterymate.

One way managers could use BAB is to help determine pitcher-catcher assignments. While lots of the time the catcher with the better bat will be behind the plate no matter what, this could show them which assignment would be best from a defensive perspective, and perhaps when that difference does or doesn’t outweigh the offensive difference. This would be one of the better uses of BAB because it depends on BAB’s distinguishing attribute, its measure of each battery’s combined performance. If a catcher is able to read a specific pitcher’s breaking ball especially well, their BAB value would reflect that. As a result, even if one catcher were better overall, BAB would indicate if a different catcher happens to work better with a given pitcher.

Another possible managerial use would be to use Net Stealing to decide when to steal. In a tight game with a lights-out pitcher, a large Net Stealing value, combined with predictive measures that indicate low chance of success for the batter, could mean that trying to steal a base would be a statistically/probabilistically sound decision. Finally, BAB’s best use, in the team category, would be in all-encompassing calculations such as Pythagorean expectation⁴. This is because it doesn’t account for the situation and such calculations measure overall offensive/defensive output regardless of situation. There is an expectation of moderate error for that exact reason.

Shortcomings: As I ended my “Applications” section with, one main issue with the stat is that it doesn’t account for the importance of a given play on the outcome of the game. A balk-off (walk-off balk) and a wild pitch by a position player in the 9^th inning of a 14-0 game are treated the same. Theoretically, a team’s BAB could be skewed by throwaway innings at the end of blowout games. The stealing part of the equation takes care of itself for the most part because most stealing occurs in tight games when a team needs an edge, but the nonSBadv part of the equation would need to be addressed.

Additionally, more reliable information as to how many bases are taken on WPs, PBs, and BKs would greatly improve the accuracy of the statistic. My current strategy of using the average number of runners on base is actually ideal for a value statistic because it doesn’t discriminate against players based on how lucky/unlucky they were—based on how many runners were on base during nonSBadvs. However, BAB as it stands now is not a value statistic. Therefore, it would more effectively do its job of measuring the observed impact the battery has if the number of bases taken on nonSBadvs was more accurate.

I also wasn’t able to account for extra bases taken on overthrows by either half of the battery on pickoffs or when throwing out runners. The difficulty is that while each overthrow that allows a runner to advance is scored an error, I don’t know of a way to differentiate between non-baserunning related errors, or errors that resulted in multiple bases being taken.

Lastly, I haven’t found a good way to account for double steals. When a runner is thrown out on a double steal, the other does not get credit for a stolen base. While the battery certainly is not deserving of blame for this, the base taken is an observable influence that BAB intends to measure. Finally, introducing weighting for the different bases (2^nd, 3^rd, or home) would also allow BAB to more accurately measure the influence that the allowed baserunning has on the game.

Improvements: As it stands the unit for BAB is bases taken. To make it easier to read, this could be pretty easily converted to runs by dividing by four. (I must admit, I’m not positive on this one as I haven’t yet read up on how stats whose unit is runs are calculated. This just made intuitive sense to me. Judging by the run potential of a stolen base being 0.2, perhaps this I should actually divide by 5.) From there the unit could even be converted to wins and become a WAR⁵-like stat if plugged into the Pythagorean Expectation formula. (Again, not 100% positive this would work but it makes intuitive sense. I’ll look into it.)

One possible way this statistic could measure individuals’ performances would be (ironically) to use the same strategy Stolen Base Runs Saved used that made me skeptical. Theoretically, a pitcher’s value could be determined by comparing how he performs with each catcher relative to how that catcher performs on average with all other pitchers. This average (weighted for number of pitches thrown to the given catcher) could determine a pitcher’s true value. That true value would be the average influence they have on their battery’s BAB (per 1000 pitches or something). The catcher’s true value could be calculated by working backwards, by taking the true value of each pitcher they have caught (the influence on their BAB they have endured from other pitchers) and subtracting that value (weighted by the number of pitches they have caught from each pitcher), from their total BAB. What would make this work better than what Stolen Base Runs Saved did is if catchers saw enough different pitchers to rule out the possibility that they looked good because their pitchers, on average, made them look disproportionately good. Just as Stolen Base Runs Saved needs sufficient variability in the catchers that pitchers throw to in order to have statistic validity, for this to work for BAB catchers would need sufficient variability in the pitchers they catch.

Finally, for fun, here are the five best and worst BAB, Net Stealing, and nonSBadv seasons, by a team since 2003 (not including this current season). Do keep in mind that while I included the primary catcher for each team, each of these statistics measures both the pitcher and catcher and thus is not an accurate reflection of the contributions exclusively by the catcher. I just included the catcher because they are catching for a much higher percentage of the season than any pitcher is pitching.

Top 5 Best BAB Seasons Since 2003

Team                                                   BAB                                       Primary Catcher

2008 Oakland Athletics ……………-28.79…………………….…..Kurt Suzuki

2004 Oakland Athletics …………….7.04………………………….Damian Miller

2005 San Francisco Giants ……….10.67…………………………Mike Matheny

2012 Philadelphia Phillies …………12.12……………………..….Carlos Ruiz

2005 Detroit Tigers ………………….16.91……………………..….Ivan Rodriguez

Top 5 Worst BAB Seasons Since 2003

Team                                                   BAB                       Primary Catcher

2007 San Diego Padres …………….214.32……………..Josh Bard

2010 New York Yankees …………..185.96………….….Francisco Cervelli/Jorge Posada

2014 Colorado Rockies …………….177.41………………Wilin Rosario

2008 Baltimore Orioles ……………177.39.…………….Ramon Hernandez

2012 Pittsburgh Pirates …………….175.71……………..Rod Barajas

Top 5 Best Net Stealing Seasons Since 2003

Team                                          Net Stealing                                        Primary Catcher

2008 Oakland Athletics ……….-87.50………………………………..Kurt Suzuki

2004 Oakland Athletics ……….-73.84………………………………..Damian Miller

2005 Detroit Tigers …………….-69.35…………………………………Ivan Rodriguez

2003 Los Angeles Dodgers …..-62.08………………………………..Paul Lo Duca

2007 Seattle Mariners …………-57.95……………………….………..Kenji Johjima

Top 5 Worst Net Stealing Seasons Since 2003

Team                                          Net Stealing                                Primary Catcher

2007 San Diego Padres ……….134.60………………………….Josh Bard

2012 Pittsburgh Pirates ……….97.44……………………………Rod Barajas

2006 San Diego Padres ……….88.93……………………………Mike Piazza

2009 Boston Red Sox ………….87.40……………………………Jason Varitek

2008 San Diego Padres ……….77.70…………………………….Nick Hundley/Josh Bard

Top 5 Best Non-Stolen Base Advancement Seasons Since 2003

Team                                          nonSBadv                                   Primary Catcher

2005 Cleveland Indians …………36 ………………………………Victor Martinez

2010 Philadelphia Phillies ……..37 ………………………………Carlos Ruiz

2004 San Diego Padres …………38……………………………….Ramon Hernandez

2008 Houston Astros ……………39……………………………….Brad Ausmus

2009 Philadelphia Phillies……..40………………………….……Carlos Ruiz

Top 5 Worst Non-Stolen Base Advancement Seasons Since 2003

Team                                          nonSBadv                                     Primary Catcher

2012 Colorado Rockies ……….122………………………………Wilin Rosario

2009 Kansas City Royals …….109………………………………Miguel Olivo

2010 Colorado Rockies ……….104………………………………Miguel Olivo

2006 Kansas City Royals …….104………………………………John Buck

2010 Los Angeles Angels …….102………………………………Jeff Mathis/Mike Napoli

¹http://www.fangraphs.com/library/defense/uzr/

²http://www.hardballtimes.com/another-one-bites-the-dust

³http://www.fangraphs.com/library/defense/catcher-defense/

⁴http://www.baseball-reference.com/bullpen/Pythagorean_Theorem_of_Baseball

⁵http://www.fangraphs.com/library/misc/war

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Therefore, Bobby Grich is listed on the Browns / Orioles roster for the duration of his career while the Phillies declare Dick Allen and the Pirates claim Jose A. Bautista. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

Assessment

The 1992 San Diego Padres          OWAR: 52.6     OWS: 324     OPW%: .595

GM Jack McKeon acquired 84.2% (32/38) of the ballplayers on the 1992 Padres roster. Based on the revised standings the “Original” 1992 Padres won 96 contests but came up two games short of the Atlanta Braves for the division title. San Diego led the National League in OWAR and OWS.

The Padres’ offense featured seven players that registered at least 20 Win Shares. Roberto Alomar (.295/8/76) scored 105 runs, stole 49 bases and topped the Friars with 31 Win Shares. Carlos Baerga (.312/20/105) accrued 205 safeties and earned his first All-Star appearance. Shane Mack posted a .315 BA with 101 tallies and 26 steals. Dave Winfield crushed 33 two-baggers and 26 big-flies while plating 108 baserunners. The corner infield was anchored by John Kruk (.323/10/70) and Dave “Head” Hollins (.270/27/93). Ozzie “The Wizard” Smith batted .295 and continued his dazzling defensive displays to earn his 13^th consecutive Gold Glove Award. Tony Gwynn aka “Mr. Padre” batted .317 in the midst of an 19-year streak in which he hit .300 or better.

Gwynn ranked sixth among right fielders according to Bill James in “The New Bill James Historical Baseball Abstract.” Eight ballplayers from the 1992 Padres roster placed in the “NBJHBA” top 100 rankings including Ozzie Smith (7^th-SS), Roberto Alomar (10^th-2B), Dave Winfield (13^th-RF), Kevin McReynolds (45^th-LF), John Kruk (72^nd-1B), Ozzie Guillen (74^th-SS) and Carlos Baerga (93^rd-2B).

Andy Benes furnished a 3.35 ERA and notched 13 wins for the ’92 squad. Omar Olivares crafted an ERA of 3.84 and managed 9 victories in 30 starts. Bob Patterson saved 9 contests while Jim Austin fashioned a 1.85 ERA in 47 relief appearances.

The “Original” 1992 San Diego Padres roster

Honorable Mention

The “Original” 1989 Padres    OWAR: 46.4     OWS: 303     OPW%: .552

Tony Gwynn collected his fourth batting crown with a .336 BA and topped the circuit with 203 base knocks. Roberto Alomar batted .295 and pilfered 42 bases during his sophomore season. Ozzie Smith contributed 30 doubles and nabbed 29 bags while Kevin McReynolds jacked 22 long balls and knocked in 85 baserunners. Greg W. Harris accrued 8 wins and 6 saves to complement an ERA of 2.60, pitching primarily out of the bullpen. The Friars tied the Giants for second place in the National League West, two games behind the division-leading Reds.

On Deck

The “Original” 1986 Mets

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

Classical baseball line-up construction involves putting your fastest player in the lead-off spot. This is due to the belief that speed generates runs (a la Rickey Henderson). In order to test this theory I went back to 1998 (since the last expansion) and looked at how may runs were scored in each season and then looked at 3 indicators, OBP, wOBA and stolen bases to test which indicator would be most useful in predicting runs. Although OBP and wOBA are very similar stats I decided to include both of them in the analysis because of differences in calculation. To put simply OBP gives a home run the same weight as a single and considers them equal (which they are not) while wOBA gives different types of hits more weight (see the OBP and wOBA pages for more information). I’ll admit that I am a huge fan of stolen bases, there is nothing like watching a player steal second or third to try and get a rally started. But the question is, can you expect to score more runs by being fast or by getting on base?

To get started I only looked at data from 2015 and pulled out the top 25 players from each stat category in order to define the “fast” players and the players who get on base the most. I also standardized runs scored to runs per game (RPG) to account for rest days and injuries which may have kept players out of the lineup for short periods of time. In the plot below it appears that the leaders in stolen bases have been scoring fewer runs per game than players who get on base more often. Based on the 95% confidence intervals of the top 25 players the difference was not significant, but the results are interesting nonetheless.

Now let’s look at some long-term data with how many runs were scored each year since 1998. In the plot below we can see that there was a large spike in runs scored in 1999 and 2000 before scoring evened out. The trend seemed to remain relatively stable from 2001 up until around 2006 or 2007 and then we see a dramatic decrease in runs scored up until last year. MLB started testing for steroids in 2003 and perhaps this is why we’ve begun to see that decrease in runs scored, but that is outside the scope of this article so let’s just focus on runs.

Runs are the most important aspect in baseball, whether that means scoring runs or preventing them. In the end, if your team can’t score any runs then you can’t win any games and unless a team have a titan of an offense you need to prevent runs as well. Here we are going to focus on run generation so we can forget about run prevention from here on out. Let’s look at the seasonal stats for our indicators and see how they look over time. I’m going to note here that OBP and wOBA shown in the plots are the league average, while the stolen bases are the league total for each season. A quick look tells us that OBP and wOBA are very closely related to the trend we saw in the second figure while stolen bases have a lot of variability over time. This seems to give a lot of evidence to getting on base, but let’s go one step further and see if we can develop a linear model to predict how each predictor affects the expected runs scored in a season.

In the final plot below I’ve put runs per game on the y axis and each stat on the x axis. In order to test how changes in league performance affects run scored I predicted the number of runs scored based on the 10%, 50% and 90% quantiles to see how many runs a player would generate over a 162-game season.

I’ve created a summary table for easy comparison of each stat and the thing that really jump out is that stolen bases doesn’t have any effect on runs scored. Based on the model, in a season where players steal almost 700 more bases collectively they generate less than 1 extra run.

In the end, getting on base is the most important (Thanks Moneyball!). For many the results should be unexpected, players who get on base more give their teams more opportunities to score runs. There doesn’t seem to be a significant advantage to using OBP or wOBA to predict runs, but based on advanced analytics people should probably consider wOBA more useful since singles, doubles, triple and home runs are all treated differently in the calculation.

I’ve always been fascinated by the outlier season where a guy puts up numbers well above or below his career pattern (Mark Reynolds’ 2009 steals total is one of my favorite examples). I wanted to take a look at the biggest outlier seasons in baseball history. To do this, I ran the data on every player-season since 1950 and calculated a z-score for each season based on the player’s career mean and standard deviation for that stat (only including qualified seasons). While the results were interesting, in my first pass through I did not control for age and the results were largely what you would expect – lots of guys at the beginning or ends of their careers.

On my second pass, I rather arbitrarily restricted the age to 25-32 to attempt to get guys in the middles of their careers. I think these results ended up being pretty interesting. The full list is here, but I’ll highlight a few below:

I had never heard of Bert Campaneris, but it turns out he was a pretty good player who put up 45 career WAR, mostly as a speedy, light-hitting, great-fielding shortstop. But in 1970, he briefly turned into a power hitter. He hit 22 home runs, his only season in double digits. He hit two in 1969 and five in 1971, playing full seasons both years. So this wasn’t even a mini-plateau. This was a ridiculous peak that he would never come close to again. We don’t have the batted ball data to dig further, but I would love to know just what was going on that year.

Dawson, on the other hand, was a pretty good home run hitter who usually hit 20-30 a season, except in 1987 when he blasted 49. Usually guys hitting crazy amounts of home runs in the late 80s through the 90s wouldn’t be that interesting, but these guys played for a long time after, never coming close to their 1987 totals again.

The guys on the downside are all fantastic home run hitters. With guys playing a full season and falling this short of their numbers, it’s always a possibility that they were playing hurt. Schmidt did indeed play hurt in ’78, but a quick Google for Thomas and Carter brought up nothing, making it all the more inexplicable.

As I mentioned above, in 2009 Mark Reynolds went 44 HR/24 steals. That was Reynolds’ only season stealing more than 11, but it “only” registered a z-score of 2.0. The three guys listed here blow that out of the water. Zeile had his season early in his career so it could have been a case of a guy losing speed or getting caught too many times and then being told to stay put. But Palmeiro and Yaz did it right in the middle of their careers. Palmeiro’s stolen base record consists of usually stealing 3-7, and getting caught 3-5 times. But in 1993, he decided to steal 22 while only getting caught 3 times. The next year he was back to his plodding ways.

On the negative side, Crawford’s struggles have been well documented. Driven by a .289 OBP and possibly declining health, Crawford’s 18 steals in his dismal 2011 season were the lowest amount of his career in a qualified season by far. We knew it was a shocking performance at the time, but I didn’t fully grasp its historical significance.

The last things I will look at are plate discipline numbers. They differ from home runs and steals because they represent hundreds of interactions, thousands if you consider individual pitches, rather than the dozens that the former two represent.

Mantle’s 1957 season deserves some attention (although he put up 11.4 WAR so it probably gets plenty of attention). That year, he put up the second best walk rate and the best strikeout rate of his career, at age 25. After that he went right back to being the great player he was before, albeit with slightly worse plate discipline stats.

Except for Money who was a guy early in his career working his way into better walk rates, this is something I don’t have a great explanation for so I’d love to hear theories. Why did Ripken in 1988, right in the middle of his career, take a bunch of walks and then never do it again to that degree? Likewise, how was Brett Butler able to cut his strikeout rate from 8.7% to 6.3% in 1985 then jump back up to 8-10% for the rest of his career?

Before I corrected for age, I got a bunch of results of guys at the tail end of their careers doing what you would expect. I do want to highlight one of them, however. In 1971 at age 40, Willie Mays had a 3.7z walk rate and a 3.1z strikeout rate. He walked a ton, but also struck out a ton. Added with his 18 home runs, that season he had a robust 47% three true outcome percentage. As the z scores show, it was a radical shift from anything he had done in his career and impressively, he used this new approach to put up a 157 wRC+ and 5.9 WAR. Apparently that guy was pretty good.

This piece identifies the biggest outlier seasons in history, but is crucially missing the why. And unfortunately, for most of these that’s not something I have a great answer for. If you have enough player-seasons, you’re going to expect some 3z outcomes. But historical oddities are one of the joys of baseball and each of the 3z outcomes is the product of a radical departure in underlying performance. I think it would be fascinating to talk to some of these guys and see what they have to say about why things went so differently for one season.

exOUT%

In the early 21st century, Oakland Athletics’ General Manager Billy Beane revolutionized baseball forever. He was the first general manager in baseball to heavily utilize sabermetrics in his baseball operations. This isn’t a history lesson though, I bring him up because of his idea that outs are precious, and as a hitter your goal is to not make out, thus him prioritizing OBP so heavily. In the following years, baseball statistics have seen phenomenal progress on both offense and for pitchers. While I believe FIP and xFIP are both very useful statistics in really measuring a pitcher’s skill, my problem is that they essentially ignore all the batted ball data that we have (GB%, FB%, LD%). SIERA and tERA have solved some of these problems, but are far from perfect, and I believe the more statistics we have, the better.

As I mentioned with Beane, while we largely focus on a hitter’s ability to not make out, we still don’t have a catch-all statistic to realize how effective pitchers are at getting batters out, because if the batter’s goal is to not make out, the pitcher’s goal is to get the batter out. So I present to you expected out percentage, or exOUT% (the name is certainly a work in progress). exOUT% sets out to answer a simple question: For any plate appearance, what is the likelihood that the pitcher will get the batter out? This can easily be found by just looking at a pitcher’s opponent OBP, but that is rather primitive, and we can get a better estimate by focusing more on pitchers’ skills to strike people out, not walk batters, and the type of contact they are giving up, and also trying to negate the effect of the defense by him, by just using league averages. So to calculate a pitcher’s exOUT%, I used K%, BB%, GB%, LD%, FB%, lFFB%, and 2014 league averages on ground balls, line drives, and fly outs. (HBPs are essentially ignored but can certainly be incorporated in a future version, this is pretty much exOUT% v1.0)

I want to give full disclosure, I am not a statistician or close to it. Math and statistics are an area of interest and I am currently pursuing a degree in math-economics, but I am far from a professional, so I recognize there are going to be errors in my data. This is an extremely rough version; there’s even a combination of data from this year and last year so there will be inconsistencies, as I don’t have the resources to gather all the data I need. If after reading this, you are interested in this and would like to take this further, please feel free to contact me if you have the skills necessary to advance this further (or even if you don’t).

I will first post a simple step-by-step breakdown of how to calculate exOUT%, and then get into more detail and take you through it with Clayton Kershaw, because well, he is awesome.

1- Add K% and BB%, subtract this percentage from 100%, this leaves you with a balls in play%, let’s just say BIP%

2- Multiply the pitcher’s GB% (make the percentage a number less than 1, for example 40% is .4) and BIP% (leave it between 1 and 100, ex 40%), this gives you a GB% for all PAs, not just balls in play, we’ll call this overall GB%, or oGB%… now multiply this percentage (in between 1 and 100) times the league average percentage of ground balls that don’t go for hits (league average is .239 on ground balls in 2014, so out percentage on ground balls is 76.1%, but make it .761…. this will give you a percentage you can leave between 1 and 100, if the number is 20%, that means that there’s a 20% chance that pitcher will induce a ground ball out that PA, assuming league average defense, we can assume this because we’re using the league average for batting average on groundballs… we’ll call this exgbOUT%

3- Now follow the same steps but with LD%, exldOUT%, the percentage chance for any given PA that the pitcher will produce a line drive out. (The league average on line drives last season was .685 (!) so that means there is a 31.5% chance a line drive will result in an out)

4- Same thing with FB%, sort of, because we also want to incorporate IFFB%. So multiply a pitcher’s FB% by their IFFB%, this gives you the percentage of balls in play that the pitcher produces an infield fly ball (bipIFFB%). Multiply this percentage by their BIP% to get his overall percentage of PAs that result in an infield fly, and this will also be their exiffbOUT%, because any infield fly ball should be converted to an out, and if not, it’s to no fault of the pitcher, so we won’t punish him. Next subtract a pitcher’s IFFB% from 1 or 100, whatever, and this is their balls in play percentage of fly balls that are normal fly balls, to the outfield. Multiply this number by their BIP%, this gives you the overall normal FB% for a pitcher, not just balls in play. Multiply this number by .793 (the league average on fly balls in is .207, so there’s a 79.3% that a fly ball will result in an out). This number is the percentage chance that for any given PA, the pitcher will produce a fly ball out to the outfield. Add this exnfbOUT% (n for normal) and his exiffbOUT% and you have his exfbOUT%, the percentage that for any given PA, the pitcher will produce a flyball out, to the infield or outfield.

5- Add K% + exgbOUT + exldOUT + exfbOUT

6- You have your exOUT%

The terms are not that technical or scientific so I don’t confuse anyone — I tried to simplify a very complicated procedure as much as possible. To clarify and give you an example, let’s go through Clayton Kershaw.

Kershaw profiles like this (I compiled this data on 8/21): 32.3 K%, 4.9 BB%, 52.8 GB%, 26 FB%, 11.8 IFFB%, 21.2 LD%.

So let’s look at the balls that don’t go in play, strikeouts and walks. Add the two and balls not in play percentage is 37.2, 4.9% are walks and thus won’t be an out, and 32.3% are strikeouts so will be an out. Thus far, Kershaw’s exOUT% is 32.3 (of a possible 37.2 so far)

Now let’s look at the balls in play. People will usually say that a pitcher can’t control what happens when a ball is in play, but I vehemently disagree, the type of contact the pitcher gives up can’t be ignored and largely effects what will happen to the ball in play. I will quote a FanGraphs article here to explain it, “Generally speaking, line drives go for hits most often, ground balls go for hits more often than fly balls, and fly balls are more productive than ground balls when they do go for hits (i.e. extra base hits). Additionally, infield fly balls are essentially strikeouts and almost never result in hits or runner advancement.” And FanGraphs also gives us this data from 2014.

GB: AVG- .239, ISO- .020, wOBA- .220

LD: AVG- .685, ISO- 190, wOBA- .684

FB: AVG- .207, ISO- .378, wOBA- .335

So this means that fly ball pitchers are most likely to get outs, although they may be less effective because when they don’t get outs, it’s more trouble than for ground ball pitchers. But remember, this statistic is just finding the chance that the pitcher will get a hitter out.

All right, so, let’s calculate Kershaw’s exgbOUT%, exldOUT%, and exfbOUT%; you can follow the numbers along with the steps I listed above.

GB%- 52.8

62.8 x .528 = 33.1584

(33.1584 x .761)=  25.23354424 exgbOUT

LD%- 21.2

62.8 x .212 = 13.3136

(13.3136 x .315) = 4.193784 exldOUT

FB%- 26

26 x .118= 3.068 bipIFFB%

26 x .882= 22.932 (bipFB%)

62.8 x .22932= 14.401296 (onFB%)

14.401296 x .791= 11.3914251 exnfbOUT%

62.8 x .03068= 1.926704 oIFFB% and exiffbOUT%

exnfbOUT% + exiffbOUT% = 13.3469317 exfbOUT%, if you followed my math exactly a decimal may be off, like 13.31 something, but this is the number the excel doc chugged out, so I’m trusting that, my iPhone calculator can’t carry all the decimals sometimes.

Now add them all up

32.3 + 25.23354424 + 4.193784 + 1.926704  + 11.3914251 = 75.07%

K% + exgbOUT% +  exldOUT% + exiffbOUT% + exnfbOUT% = exOUT%

The league average exOUT%, using league average statistics from 2014 for the ones involved, is 69.8%. Scherzer leads the majors (well the 89 pitchers I was able to export data from FanGraphs) with a 76.43 exOUT%. If you want to look at it as a more concise and better version of opponent OBP, his is .236, so, you know, good. Here is a picture of the data for the top 37 — the J column is what you are looking at. Betances is in their because I wanted to calculate one reliever. 

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All right, I’ve explained it a bit in the prologue, but now that you’ve seen it, let me explain more why I like this stat. Well first, I created it and calculated, so, well, yeah… but I also like this stat because it answers a very simple question “How good is a pitcher at getting people out?” Pitching in its simplest form, is exactly that, getting people out. The stat recognizes that there’s basically only these outcomes for an at bat: strikeout, walk, ground ball, line drive, and fly out, and looks at the pitcher’s stats in these categories to determine how many people he should be getting out. The stat is more predictive than evaluative in nature, because you can calculate a pitcher’s actual out percentage, but that doesn’t nearly tell the whole story, because a lot of luck is involved with balls in play, and other fluky outcomes.

This operates under the basis that a ground ball will perform the way the average ground ball does, a line drive performs the way an average line drive does, and a fly ball behaves the way a typical fly ball does. There could be guys getting very fortunate with ground balls: having a great infield behind them, balls not squeaking through the holes; with line drives: being hit right at people; and fly balls: staying in the park, having outfielders who cover a lot of ground. And there could be guys who are getting unlucky: the ground balls are getting through the holes, the infielders don’t have range; line drives seem like they are always going for hits, and fly balls are falling in. This says that a pitcher can’t control that, but they can control how much they strike out people, how much they walk people, and how often they give up ground balls, line drives, and fly balls, and if these balls in play behaved the way they should, the pitcher should be getting this percentage of people out.

I will address the flaws I have found with it. As much as getting people out is important, sometimes what happens in the plate appearances that don’t end in outs are almost as important. This only deals in batting average regarding balls in play, but wOBA is very important too. Fly balls are more likely to be outs than ground balls, but the wOBA on fly balls is over 100 points higher. Additionally, I’d prefer instead of ground balls, line drives, fly balls, to use soft contact, medium contact, hard contact, because that is a truer test of pitcher skill, however, I did not have this data at my disposal as far as league averages on what the batting average is for soft contact, medium contact, hard contact (if someone does, please contact me like I said). So what I have for now will do and this batted ball data is still a good measure. I set out to calculate what percentage of batters a pitcher should be getting out, and that is exactly what I found out. So while it’s not perfect, it has its use, and it’s something to build on.

exRP27

And build on I did. While the out percentage is nice, it doesn’t give us a measure like ERA or FIP or xFIP, that tells us how many runs a pitcher should be giving up. So using the data I used to calculate exOUT%, I present to you exRP27 (expected runs per 27 outs, a stupid name for a hopefully not stupid stat).

The basis for this stat is this data from FanGraphs, “Line drives are death to pitchers, while ground balls are the best for a pitcher. In numerical terms, line drives produce 1.26 runs/out, fly balls produce 0.13 R/O, and ground balls produce only 0.05 R/O.” (I don’t know how this was calculated, or when it is accurate for, but this is what I got). We don’t know this for soft contact, medium contact, hard contact, so again I’m sticking with ground balls, line drives, and fly balls. 

All right, so what I am going to do using this stat and the pitcher’s K%, BB%, GB%, LD%, and FB% is see how many runs the pitcher should be allowing over 27 outs, and then adjust it to get it on a scale similar to ERA, FIP, and xFIP.

Keeping Clayton Kershaw as our example, let’s take a look.

Kershaw’s K% is 32.3 — we’re multiplying this by 27 (for outs in a game), and we get 8.721 K’s, so 0 runs so far because a K will never produce a run

Now GB%. His exgbOUT% is 25.23354424, multiply this by 27 and we get 6.8 (ish, final number will be exact via the Excel doc). Multiply this by .05 (the runs per GB out he gets) and we get .34 runs.

LD%- his exldOUT% is 4.193784, multiply by 27 and get 1.13232168, and multiply this by 1.26 for LD runs/out and we get 1.43 runs

His exfbOUT% is 13.3181291, now multiply by 27 get 3.6 and then that by .13 and you get .47 runs

Add up all these exRUNS and Kershaw’s total is 2.24. However, we can’t stop here because the number of outs he’s recorded is only 20.3 (8.7+6.8+1.1+3.6) approximately. 20.3 is the rounded up total. So get this 20.3 (or whatever the pitcher’s exOUTS is) up to 27  by multiplying by whatever it takes, and then multiply his exRUNS by this same number. For Kershaw you end up with 2.97 exRP27. The league average would be 3.78. Last year’s average ERA/FIP/xFIP was 3.74, but when I adjust everything to that, everyone’s exRP27 just goes down slightly (Kershaw’s from 2.97 to 2.94), but I want it to be on a more realistic scale where everyone’s totals are lower and a really good exRP27 is comparable to a really good FIP, like in the low 2s. 

So I don’t know what the statistic’s correct way is, but here is what I did to make it work. I calculated what his “ERA” would be using by multiplying his exRUNS by 9 and then dividing that by his exOUTS. His was .99, the league average was 1.26. I then did .99/1.26 to get .78 or so, I then multiplied that by his exRP27 and got 2.34. I felt like this was more realistic and in line with his ERA/FIP/xFIP. Obviously, can’t be the same because they measure different things, but just got in in the area. And the same is done for all pitchers. Obviously, not everyone gets multiplied by .78 of course. The league average remains 3.78, between last season and this season’s average for ERA/FIP/xFIP.

Here is the leaderboard for that (S column):

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 I really like this stat a lot, and feel like it does what I wanted to accomplish: figure out how many runs a pitcher should allow per 27 outs given his K%, BB%, GB%, LD%, FB%, and the notion that balls in play will behave the way they normally do, as anything else is likely luck and not indicative of the pitcher’s performance.

I look at Sonny Gray as someone this stat is perfect for. His ERA is outstanding at 2.04, but his FIP is 3.00, his xFIP is 3.47 and his SIERA is 3.50. The problem is, at least with FIP and xFIP for sure, is that they ignore what happens when the ball is in play. He doesn’t strike out too many people, he has a good BB% but not spectacular, and he’s given up 10 home runs, a fair amount, so this hurts his FIP and whatnot. However, instead of saying “well he will regress, look at his FIP/xFIP/SIERA” this looks at why he’s having this success, and it has to do with the balls in play, which is getting ignored. Gray’s LD% is just 14.6! That is really good! Second best of the 90 pitchers I did this for. And his GB% is 54%, 9th best, also really good. The pitcher does have control over the type of contact he allows, and the fact that Gray is producing a ton of ground balls, and very few line drives, is why he’s been so successful. His 2.34 exRP27 suggests that he has not been as good as his 2.04 ERA suggests, but he’s not as far off as the other stats suggest. 

Obviously exRP27 is far from perfect, and is in no way supposed to replace FIP/xFIP/SIERA, but it is something to look at with them. I am a big believes in aggregation, so I think that averaging some combination of these 4 stats together or them all, is an even better way to evaluate a pitcher. We’ve got more data than ever, so it makes sense to use it, exRP27 and exOUT% are just more examples of utilizing this data to help better evaluate pitchers.  

I hope you guys enjoyed. Any feedback please comment or contact me. Next I will be looking at exWOBA against for pitchers using similar data, and exWOBA for batters using the data but for hitters.

Remember at the end of last season and before this season when we all foresaw an Indians rotation that could possibly feature somewhere between 2 and 5 really good, and possibly great, starting pitchers?  Don’t get bogged down on the slight exaggeration of that 1^st sentence – To recap what we were looking at coming into this season for the Indians’ rotation:  Corey Kluber won the 2014 AL Cy-Young; Carlos Carrasco had a string of starts to end 2014 in which he seemingly (finally) figured out how to harness all of his powers in a bid to ascend his name to an echelon where only Clayton Kershaw’s name resides; Danny Salazar has always had elite swing and miss stuff and was also excellent in the second half of 2014;  Trevor Bauer and his Costco-sized arsenal of pitches have made some of us incredulously, if not warily optimistic since he was taken 3^rd overall in 2011; and even T.J. House made us pause and take notice with his strong second half of 2014.

Then, like hype men with a special blend of Cleveland Kool-Aid being intravenously administered, Eno Sarris and Daniel Schwartz posted one of my favorite FanGraphs articles ever, Pitch Arsenal Score Part Deux, and the anticipation over the Indians’ rotation pulsated like a vein in the neck of John Rambo in the midst of fleeing from man-hunters.

The supporting cast, the lineup, looked poised to support the staff with plenty of runs.  Returning would be: break out star Michael Brantley; bounce-back candidate Jason Kipnis; now-full-time-first-basemen, Carlos Santana; a supposedly healthy Michael Bourn; an offense-first but totally-respectable-defensively, Yan Gomes; and an actually-not-that-horrible-in-2014, Lonnie Chisenhall.  Slugger Brandon Moss, and contact-happy-supposedly-glove-first Jose Ramirez had secured full-time spots as well in RF and SS respectively.  So even though it wasn’t without flaws, it seemed like they would allow the pitchers to rack up plenty of fantasy-relevant wins.

Note: This post isn’t about the disappointment of the Indians, though they have been disappointing; it’s more about what factors beyond luck have contributed to the numbers of the Indians’ starting rotation at various points throughout the year, and the disparity (big or small) between the pitchers’ rates and predictors at those points.

The Indians’ starting pitchers, or at least the top 4 (Kluber, Carrasco, Salazar, and Bauer) have, for the most part, been putting up good, albeit, inconsistent numbers all year despite posting some elite peripheral rates and ERA indicators.  A number of reasons have caused these numbers to grow apart (bad), come together, and then grow apart again (good).  Luck can work like a bit of a pendulum, swinging from one extreme, through the middle, and to the other extreme before evening out and that is at the core of what the Indians’ starting pitchers have experienced this year — although they have yet to experience the final stabilization phase.

We will examine plenty of numbers (Beginning of season to August 18th) based on this time frame: (Spoiler alert – this article is long and dense, and this timeline serves as a sort of cliff notes as to how the staff’s numbers have improved throughout the year – so if you’re the type of person who feels like looking at a bunch of data is superfluous when the bullet points are in front of your eyes, just read the timeline and be done with it.)

April 6^th – May 23rd/May 24th – June 15th

One week into the season, before it was evident that the team’s defense was very sub-par, Yan Gomes hurt his knee and hit the disabled list for over a month.  Roberto Perez filled in quite nicely, and looking at just a couple numbers, could be considered the more valuable catcher (1.4 WAR compared to 0.5 WAR for Gomes).  Brett Hayes (0.0 WAR) was called up and was the secondary catcher during this period.  Behold, a table from StatCorner:

Perez has had the least amount of pitches in the zone called balls and the most amounts of pitches out of the zone called strikes.  Overall, despite receiving fewer pitches than Gomes, he has saved more runs (4 DRS to Gomes’ 1) and their caught stealing rates are basically identical with a slight edge going to Perez – 38% to Gomes’ 35%.  Gomes was much better in terms of framing in 2014, and it’s possible the knee injury has limited his skills all around this season.  Anyways, from April 6th – May 23rd, the combined stats of Kluber, Salazar, Carrasco, and Bauer look like this:

Gomes returned as the primary catcher on 05/24, and from that point through June 15th, the cumulative numbers aren’t too different, although there is a dip in both K-BB% and GB% that we’ll have to look into.

So despite lower K-BB and ground ball percentages (leading to higher ERA predictors), the group’s ERA in the segment of the season when Gomes was reinstated is essentially exactly the same as from the first block of time with Perez.  Now, I am not a big believer in CERA because there is a high level of variation and too many unknown variables pertaining to how much of the responsibility/credit goes to the catcher, the coaching staff, or the pitcher; but I do think that it’s possible Gomes’ extra service time has enabled him to be more in tune with his staff as well as understand hitter tendencies better than Perez and Hayes.  I realize we’re getting into a gray area of intangibles, so I’ll reel it in with some results based on pitch usage%.

% Difference in Pitch Usage with Yan Gomes compared to Roberto Perez

Using BrooksBaseball Pitch f/x data, let’s painstakingly find out how different each pitcher’s pitch usage was in regards to different counts, or better known as Pitch Sequencing.  We’ll look at first pitches, batter ahead counts, even counts, pitcher ahead counts, and 2 strike count situations.  As good as pitch f/x is, the data still isn’t perfect.  There may be discrepancies if you look at usage at Brooks compared to the usage at FanGraphs, so for each pitcher we’ll split the pitches up into three categories: Fastballs (four-seam, sinkers, cutters), Breaking Balls (sliders, curve balls), and Change Ups (straight change/split finger) – I’m aware that splitters are “split fingered fastballs”, but I liken them to change ups more because of the decreased spin rate and generally lower velocity.

*Having a table for each pitcher in regards to pitch sequencing made this article quite messy, so I’ve included a downloadable Excel file, and briefly touched on each pitcher below.

Pitch Sequencing Excel Doc.

Corey Kluber

Looking at the data, Gomes stays hard with Kluber more than Perez until they get ahead in the count.  Perez swaps some early count fastballs for curve balls, but they both see his curve ball as a put-away pitch.  Gomes tends to trust Kluber’s change-up more than Perez later in counts and Perez likes it more earlier in counts.

Danny Salazar

Much like with KIuber, when Gomes catches Salazar, they have a tendency to stay hard early.  Gomes pulls out Salazar’s wipe out change up after they’re ahead whereas Perez will utilize it in hitter’s counts as well.

Carlos Carrasco

Carrasco has 5 good pitches and he’s pretty adept at throwing them for strikes in various counts which is why there is some pretty even usage across the board, at least in comparison to Kluber and Salazar.  There is quite a bit more usage of Carrasco’s secondary pitches in all counts and there are pretty similar patterns when Gomes and Perez are behind the plate.  With Hayes, it doesn’t look like there is much that changes in sequencing until there are two strikes on a hitter.

Trevor Bauer

Bauer is probably a difficult pitcher to catch because of the number of pitches he has and the constant tinkering in his game.  Side note: Gomes is the only catcher to have caught a game in which Bauer threw cutters, and in their last game together, Bauer threw absolutely no change-ups or splits.  Bauer’s highest level of success has come with Hayes behind the plate and perhaps that’s from their willingness to expand his repertoire in more counts than Gomes and Perez do, but there is no way I can be certain of that.

Pitch sequencing can effect the perceived quality of each pitch and therefore, can produce more favorable counts as well as induce higher O-Swing and SwStrk percentages (or less favorable and lower).  So despite the framing metrics favoring Perez, the group throws more strikes with Gomes and also induces more swings at pitches outside the zone – although, as previously noted, there is some regression with Gomes behind the dish in terms of SwStrk% and K-BB%.

**These graphs represent numbers through the entire season to garner a bigger sample size.

With lower line drive rates and more medium + soft contact, and (in the case of the Indian’s defense), more fly balls, a conclusion could be jumped to that the staff’s BABIP has trended downward since Gomes regained his role.  A look at BABIP throughout the course of the season:

Woah!  It was well above league average in April and then plateaued at just above league average through mid June, but has been plummeting ever since.  Obviously a catcher is not responsible for this dramatic of a swing in BABIP, so the Indians’ defense must have improved.

June 16th – August 18th

The rotations’ traditional stats look even better if you use June 16th as the starting point:

So let’s take a look at the Indians’ defensive alignment by month (Player listed is the player who received the most innings played at the position).

If you’ve paid attention to the Indians at all, you know they’ve made some trades and called up a couple prospects.  But just how different is the new defense?  Well, we only have a small sample with the current configuration, but it appears to be A LOT better. If BABIP wasn’t enough of an indicator, and it’s not, because there has to be some regression to the mean – it can’t stay that low – here are some numbers from the players who were playing the most in May compared to the players who are playing the most in August (again, numbers represent full-season stats):

What’s interesting is that the biggest difference in the infield is Francisco Lindor (Giovanny Urshela has been very solid, but Chisenhall was pretty similar this season at 3B).  I’m sure someone at FanGraphs could churn out a really cool article (if someone hasn’t already) that shows us a quantifiable difference an above average to well above average shortstop makes for a team even if you just keep the rest of the infield the same, as the control.  The 2015 Tigers come to mind – a healthy Jose Iglesias has made a difference for a team that still features Nick Castellanos at 3B and Miguel Cabrera at 1B.  Teams are willing to sacrifice offensive contributions if a SS has elite defensive skills (Pete Kozma, Andrelton Simmons, Zack Cozart to name a couple off the top of my head).  Lindor, to this point, has been an above average offensive player, too, so this could be special.

At this point the Indians are in last place and are out of contention.  Abraham Almonte is their starting center fielder and with Kipnis back from the DL, Jose Ramirez is not playing 2B, but is instead getting reps in left field while Michael Brantley DHs due to his ailing shoulder.  Perhaps all this means is that they don’t have better replacements; OR PERHAPS they’re planning to establish a more defense-oriented squad next year…

Now there’s no doubt that this research has led to some frustrating conclusions.  With Gomes behind the plate, the K rate and GB rate of the staff has trended in the wrong direction in regards to ERA indicators; so is the difference in the batted ball profile plus an improved defense enough to make up for these facts?  This small sample size thinks so, but it could 100% just be noise.  However, there are clubs that are succeeding by using similar tactics right now:

Granted, the Royals and Rays have the 1st and 2nd best defenses in baseball, and their home parks play differently than the Indians, but they also don’t boast the arms the Indians do.

The Indians have their noses deep in advanced metrics and having rid themselves of Swisher, Bourn, and Moss during 2015’s trading period has allowed them to deploy a better defensive unit which has amplified their biggest strength – their starting pitching.  Furthermore, their unwillingness to move any of their top 4 starting pitchers also leads me to believe they see next year as a time for them to compete.  I’m not going to speculate what moves the Indians will make in the offseason, but I hope they stick with this defense-oriented situation they have gone with recently because it’s been working (and because I own a lot of shares of Kluber, Carrasco, and Salazar in fantasy).

INTRODUCTION & EXPLORING THE QUESTION

So there’s been a lot of attention paid to Three True Outcome guys recently. The subject was touched upon in a recent article by Craig Edwards, as well as in this community blog by Brian Reiff. These articles brought attention to guys who are notable for putting 7 of the 9 defensive players to sleep. However, what caught my attention the most was a comment on Craig’s piece by “steex” who proposed a hypothesis about these sluggers:

I think this makes selecting TTO players strictly by the numbers difficult. For me, the spirit of TTO is a player that does enough good (HR+BB) to balance out for a lot of bad (K). Harper and Votto don’t really fit that definition in the intended way, but rather show up on the list because they do SO much good (HR+BB) that their total HR+BB+K makes the cut despite having not as much of the bad (K).

I wonder if a better list of players comparable to one another would be obtained by first sorting by TTO%, then subdividing that by the percentage that Ks represent from the TTO events (i.e., K/[HR+BB+K]). That provides a lot of separation between guys like Harper, Votto, and Goldschmidt who have strikeouts represent less than 50% of their TTO events and guys like Carter, LaRoche, and Belt who have strikeouts as more than 65% of their TTO events.

This was also supported by follow up comments speaking about how they differentiate the players into two groups, those who strike out at a higher clip and those who have BB% and HR% compensate for a reduced K%. My goal was to figure out whether the quality aspect of the Three True Outcomes was different between these high-K% players and the low-K% players, beyond the walks and strikeouts.

THE PROCESS

First, let me define how I picked out my sample, and how I classified the players into two groups, and then I’ll begin to discuss the details of the study. I pulled all the data from 2010-2014 for player seasons who qualified for the batting title (minimum 502 Plate Appearances). This gave me a sample of 723 player-seasons (where a single player may be listed as a qualifier separately for up to five seasons). Of these 723 player-seasons, I set the Three True Outcome bar at 40%. Why 40%? Well the simple average (weighted to PA) was 29% Three True Outcome (I’ll abbreviate to TTO from now on), with a standard deviation of approximately 8%. So that would make 40% TTO somewhere around 1.5 standard deviations above the mean, which seemed like a reasonable line to draw in the sand.

There is now a sample of 52 player-seasons (7.2% of the qualifiers). From here, I had to draw a new line, and I wanted to go by “steex”’s suggestion of using the proportion of strikeouts to TTO% as the barrier. The key was getting a decent number of player-seasons on either side. I started off with 50% (using the formula K/[HR+BB+K]), but that would have left me with only two player-seasons (2011 Bautista, 2013 Votto, for those who are curious). I bumped it up continually until I reached a 60% ratio, which seemed to be reasonable. That placed 11 player-seasons in the low-K TTO group (which will be referred to as TTO-L) and 41 player-seasons in the high-K TTO group (which will be shown as TTO-H).

The whole TTO population is now divided into two groups, TTO-L (with 11 player-seasons) and TTO-H (with 41 player-seasons). Now what? I was truly curious about how these two groups differed in their hitting abilities. It seems fairly obvious that those who have lower K% and higher BB% will have higher (better) wOBAs, wRC+s, and the like (just due to trading strikeouts for walks). As Craig showed in his article, the average TTO player is an above average hitter due to a typically lumbering stature and a penchant for not being great at defense. Those who aren’t above average hitters and are bad at defense usually find themselves riding minor league buses around the country. But I’m not trying to compare TTO hitters to non-TTO hitters, rather comparing the two halves based on TTO quality.

BATTED BALL DISTRIBUTIONS

I decided to compare them using  statistics that might glean differences between good and bad hitters. I looked at batted ball distributions to start. I compiled the GB%, LD%, FB% and IFFB%, as well as the PULL%, CENTER% and OPPO% from the leaderboards (plus HR/FB for good measure), and computed the mean, standard deviation, and p-value based on a two-tailed T-Test. The results are in TABLE ONE:

Interestingly enough, the batted ball distributions are very similar between the two groups. The groups are pretty much interchangeable, with the only thing close to being statistically significant is the percentage of balls hit to center field. However, when looking at that in the bigger picture of pull/center/opposite, the numbers are nearly identical. So far, the two groups are relatively indistinguishable from one another.

BATTED BALL AUTHORITY

At this point, my mind went in another direction: do TTO-L player strike the ball better than their TTO-H counterparts? If you’ve got a good eye and can take a walk more easily, then you’re probably able to see the ball better, and therefore are able to drive the ball harder. So, even though it may not have manifested itself in the GB/LD/FB numbers, perhaps these “elite” players in the low-K group have better pop. To evaluate this, I pulled the HARD%, MED%, and SOFT% of balls by each group, along with BABIP for good measure, summarized in TABLE TWO:

Again, a little surprising to me. There’s no statistically significant difference between these low-K guys and high-K guys in terms of batted ball authority. Each group hits roughly the same, with the low-K guys trading a few medium hit balls for some hard hit ones (albeit not enough to differentiate the groups). BABIP would manifest itself in these guys striking the ball harder, and it comes out roughly even. One note that BABIP would control itself here more than in most hitter studies because the subset of TTO players typically have similar builds and are not artificially increasing BABIP by beating out infield hits (neither group would have a distinct advantage).

BATTING SELECTIVITY & CONTACT RATES

So where do these two groups separate? Something has to cause the disparity between the groups and show a differential in ability. And that something is at the plate in their selectivity – which only makes sense. Players who draw walks are those who lay off bad pitches out of the zone, and those who strike out typically struggle to identify strikes from balls, or lack the ability to contact balls when they swing (usually not both, or else they wouldn’t be in the majors). The data is summarized below in TABLE THREE:

Here’s all that red you’ve been waiting for. Starting with the first three rows, there’s a statistically significant difference (p < 0.05) between the two groups in swinging at balls (O-Swing%), which goes to show the selectivity of the TTO-L group is better than the TTO-H group. In rows four to six, we see that for swings on pitches both in and out of the zone, the TTO-L group makes contact more often, with in-the-zone contact being statistically significant at the p < 0.01 level. To summarize this table, the TTO-L hitters don’t swing as often, but when they do they are better at making contact with the pitch as compared to the TTO-H batters.

GROUP SUMMARY

The final table, TABLE FOUR, summarizes the groups for anybody who was curious. 

Obviously above you see that the K-rates and BB-rates are statistically significant, which only makes sense because that’s how we divided the groups, so that was artificially implanted. And, of course, you’ll always have a better wOBA if you walk more and strike out less, because walks count for approximately 0.7 runs based on linear weights each.

SUMMARIZING THE FINDINGS

Of the 723 player seasons between 2010 and 2014, inclusive, 52 were deemed to be Three True Outcome seasons (with 40% of the plate appearances ending in BB, K or HR). From there, the group was subdivided into two by the relative amount of K’s compared to total TTO% (with [K%/TTO%]>60% as TTO-H, and [K%/TTO%]<=60% as TTO-L.

The groups were compared against one another on Batted Ball Distributions, Batted Ball Authority, and Batting Selectivity & Contact. The vast majority of the statistically significant differences between the groups appeared in the third table, with the TTO-L group displaying a better eye for strikes, while also contacting the ball better when they decided to swing. Perhaps the most interesting finding of the study was that this increased contact did not manage to create better authority when hitting the ball, nor did it change the batted ball distribution significantly. Just because the TTO-L group made contact more often on their swings did not mean they were able to drive the ball better than the TTO-H players.

Just a quick thank you to end this, to the FG community comments that inspire people to write things like this and make my last college summer a little more (less?) exciting.