In Part I, one of the major observations was that a group of smaller-statured players seemed to be using backspin as a distance tool. I was curious how the increase in home runs would look when broken down by physical size. In addition to using Statcast data from Baseball Savant, I downloaded player heights and weights from MLB rosters and created size quintiles. While I expected to see significant contribution from smaller-sized players, the magnitude of what is occurring was quite surprising:

Note: Size based on height * weight. Since pitchers skewed the quintiles due to their above-average size, they were excluded in making the quintile groups; however, their HRs are included in order to tie back to HR totals.

Now that is democratization of power! While interesting, the obvious question is: How are the smaller players hitting all the additional home runs? Is it more distance through exit velocity (EV) and/or launch angle (LA), more pulled balls, more fly balls, or just better-hit fly balls? Let’s take a look:

Distance, EV and LA

Note: Balls hit at Coors Field excluded

Although the data above would support a slight increase in homers overall, there is no smoking gun as to what might be happening within the smaller player groups. If smaller players are not hitting the ball that much harder or further, maybe it could be that they are hitting more homers to the pull side.

Pulled HR and Hits

Although the location mix of homers did not change significantly from the prior year, smaller players in both years hit a much higher percentage of their homers to the pull side than average. The more important metric to consider with respect with the pull factor is what is happening to the mix of well-hit fly balls.

Again, more data supporting a slight overall increase in homers – More well-hit fly balls hit to the pull side and more of those balls going for homers. No real support here for what might be happening with the smaller players. What about well-hit fly balls in general:

Now we’re getting somewhere! Smaller players experienced a significant increase in well-hit fly balls in 2016. What about fly balls in general, not just those of the well-hit variety:

The last two charts kind of sum it up – smaller players are hitting more fly balls in general as well as more well-hit fly balls that are going for homers. Before closing, I’d like to show two other tables which I believe are meaningful for both the home-run question as well as hitting in general. In a certain respect, it appears hitters are making better contact. The following chart shows the volatility (via standard deviation) for EV, LA, and Distance.

Since EV is up in terms of MPH but down in terms of volatility, this would indicate players in general are making better contact. The same is true for distance – higher average distance but lower volatility. However, the increase in volatility of launch angle would seem to indicate quite the opposite – that players are using a lower ball-contact point in order to achieve the higher number of fly balls. Take a look at pop-ups over the past two seasons:

While not up across the board, it is very interesting that there is a significant increase in pop-ups in the group responsible for the largest increase in homers.

After considering the data above, I was curious how the homers looked broken down by age. The increase in homers of the younger players was equally surprising:

I checked for the obvious relationship between size and age; however, the 24-26 age group was reasonably well distributed in terms of size so there is likely something additional going on with the younger players. Whether it is a power focus earlier in their development, a selection bias through the draft or some other factor I’m not sure. Maybe I’ll get into that another time.

Summary

This is a very interesting issue to consider and while I’m sure there will be much more written on the topic, it certainly takes some possibilities such as a juiced ball completely out of consideration. Now that would be a conspiracy! That umpires are throwing out juiced balls for the little guys! Except that the balls would have to be so stealthy that they don’t get hit significantly harder or further – they just hit bats of the smaller players for well-hit fly balls more often.

For me, the really interesting part is the underpinning driver – that advanced metrics have changed the market which values the players. Whether consciously or not, players are changing to align with the market to maximize their value. Even more interesting is what the future holds – what is the cost of the hyper-focus on power and loft and what are the unintended consequences that have yet to come to light?  As far as the home-run issue, at least in terms of player size and age, less certainly has been more.

Any fan that is somewhat invested in the game of baseball understands the importance of putting both a good team on the field at the MLB level while also sustaining an adequate farm system. The Angels have done neither.

This is a hard thing to do when the best player in baseball plays for you, but Arte Moreno and the Angels have managed to do that. Let’s take a look at how they ended up in this dire situation.

MLB Team

One thing the Angels do have (sorta) is a good core of players. They have Mike Trout, Kole Calhoun, Andrelton Simmons and Garrett Richards. Trout and Simmons play at premium positions, Calhoun is a good two-way player and Richards has ace potential (given his arm doesn’t snap). They even have quality players in Yunel Escobar and C.J. Cron who are quietly productive. With the exception of Escobar, each of those players are controllable for the next several years. The team isn’t devoid of talent behind Mike Trout like some believe. They also have a few starting pitchers who have either shown success or are promising in Matt Shoemaker, Tyler Skaggs, and Andrew Heaney. The only problem is that Heaney is done for next year already and Skaggs may be too if his stem-cell therapy doesn’t work.

The holes in the team are at second base, left field, starting pitching spots that aren’t guaranteed and the bullpen. Especially the bullpen. The only true bright spot of their bullpen is Cam Bedrosian while everyone else is expendable at best (unless Huston Street can return to form).

Now that we’ve looked at their roster heading into next season, how can they fix it?

Win-Now Options

Moreno has to let Billy Eppler spend some money this offseason. He isn’t paying Jered Weaver or C.J. Wilson anymore. That leaves them with roughly $30 million to spend before hitting the luxury tax, which Moreno has made clear he won’t go over. One thing this offseason is sure to provide is offense.

Obvious fixes would be to sign Yoenis Cespedes and one of Justin Turner — which would move either him or Escobar to second — or Neil Walker. The Angels can’t go another year with below-replacement-level players at those positions if they truly want to win. The smart route for them would be to avoid what are likely to be excessive bidding wars for Cespedes and Turner. Walker is a great fit at second for them. He offers good offense from the left side to couple with their righty-heavy lineup as well as average defense to a team that has seen paltry turnouts at the position.

As for left field, their are plenty of corner outfielders on the market. However, instead of paying too much for a reunion with Mark Trumbo, the Angels should look at Ian Desmond, Dexter Fowler and Carlos Gomez. Desmond and Fowler failed to garner much interest last offseason, so an offense-heavy free-agent class should keep their price tags down. As for Gomez, he was DFA’d by the Astros before playing somewhat better with the Rangers. Gomez is the higher risk/reward player while Fowler is the closest to a sure bet to perform consistently. Desmond is a wild card since he just converted to the outfield and profiled as below average in center. Shifting him to left with Trout in center could improve his defense, so he is also a viable option. The Angels took huge risks in the past that didn’t turn out well, so Fowler is probably their best option. Plus he adds another lefty bat against righties.

If the Angels can manage to add both Walker and Fowler, their offense would actually fill the basic requirements for a successful team. They would have a leadoff hitter in Fowler, and a number two in Calhoun, with Trout in the three slot. Either Cron or Pujols will bat fourth and fifth with Walker behind them. Then some mix of Escobar, Simmons and their catcher in the 7-9 spots.

As for fixing the rotation, that will be much harder, and they might just have to wait out the storm or go over the luxury tax. Overpaying for Jeremy Hellickson or Ivan Nova would be a bad move and Rich Hill isn’t a good fit for a fairly old roster that already has its risks. Henderson Alvarez could be a good bounce-back candidate after missing 2016 following a shoulder surgery. Andrew Cashner could be an option in a pitcher-friendly park in Anaheim, though the one in San Diego didn’t do him much good. Going into the season with Richards, Shoemaker, Skaggs, Nolasco and one of Alex Meyer, Nate Smith or Daniel Wright isn’t the end of the world. It just has its risks.

What about the farm?

Minor Leagues

According to RosterResource.com, the Angels have 11 home-grown players on their 40-man roster. That’s definitely on the lighter side compared to most teams, but it isn’t quite as extreme as the Padres’ six. They also only have six free-agent signings, which isn’t too large of an amount. The part of their roster that stands out is their eight waiver claims. The fact is that the Angels didn’t have the depth to fill their own roster with players already within their organization.

Going back to the team’s free-agent signings, six isn’t a large amount as I stated before. However, some of their more recent signings have been very costly (both in terms of monetary and baseball value). In the offseason prior to the 2012 and 2013 regular seasons, they signed Albert Pujols, C.J. Wilson and Josh Hamilton. Each of these players cost north of $15 million per season. Each also cost the team draft picks. They lost two first-round picks and a second-round pick.

The teams’ most recent top picks most likely aren’t going to make an impact with the MLB team. For one, Sean Newcomb definitely won’t since he’s on the Braves now. Taylor Ward and Matt Thaiss were both very weak first-round picks. Ward won’t hit and Thaiss is basically limited to first base.

This isn’t to say the Angels have nothing in the minors. Jahmai Jones is promising but very young and a few of their other 2016 picks could develop into good MLB players, including Brandon Marsh and Nonie Williams. Their farm isn’t deep enough to trade for any key pieces though, and they shouldn’t do that even if they did have the pieces. Eppler has a chance to use a top-10 pick this year as well as future picks to try and build the strong farm that the Angels have lacked for so long, so he can’t waste that opportunity on a middling team.

In Summary

I feel that I’ve laid out the best-case scenario for the Angels next year with potential signings of Walker and Fowler, who would fit in very nicely with their current lineup. Any team with Mike Trout has a chance to be successful after all. They will need to sign those two players first, and then the rotation needs to have luck on its side with the injury situations. The bullpen is a clear gap in the roster, so safe signings over the offseason could complement Bedrosian and possibly Street. Their farm system is also a clear work in progress, but it isn’t empty in terms of talent. That talent is just a little ways off at the moment. Overall the future of the Angels seems dreadful, but if things break right they can be a contender next year. Their overall run differential was only 18 apart from the first-place Rangers, so they at least played in close games. Now all that needs to be done is execution by the players and by the front office to bring a winner back to Anaheim.

Despite their World Series Game 5 win, the Cubs came under fire Monday morning for their lack of hustle. On Mike & Mike ESPN Radio, Mike Greenberg commented that the Cubs seem to be lacking hustle, as evidenced by slow home-plate-to-first-base times by Jorge Soler on his Game 3 triple and Anthony Rizzo on his Game 5 double. Soler assumed a fly out or foul ball, and Rizzo assumed a go-ahead home run. Buster Olney added, “It’s interesting you say that, because I had a conversation with one of the veterans in this World Series…and that’s exactly what he said. ‘This is the World Series, how can that happen? It’s a different generation.’”

That’s right, this is the World Series, the biggest baseball stage, the postseason when even average fans tune in to watch and learn from some of the best players in the game. In today’s baseball market worth billions, where players in their late teens and early twenties are paid 10 or more times the average American salary, maybe a lapse in hustle or a hard 90-time is acceptable, if not necessary, during the regular season of 162 games in order to avoid injury or excessive exhaustion. You wouldn’t want your star player pulling a hamstring on a routine infield groundball in August, would you? Of course not. But c’mon, this is the World Series! Most of these players have never played for higher stakes. Is the “lack of hustle” a generational problem? What ever happened to the commitment Joe DiMaggio had to playing hard just in case someone was watching for the first or last time?

Your organization hasn’t won a title in 108 years. Why wouldn’t you approach every play as if it was the last? You’d think they would in the World Series; especially given Game 5 could have been the last. Greenberg and Olney failed to even mention Javier Baez’s Game 5, second-inning strikeout where he refused to run to first base on a dropped third strike, looking increasingly frustrated with his World Series offense (2 for 18 with 7 strikeouts, and 16 runners left on base after that at bat).

Let’s take a look at some numbers by analyzing the Win Expectancy (WE) for the Cubs before and after each of these three plays (Soler’s triple, Baez’s strikeout, and Rizzo’s double) to show the importance of maximizing every opportunity and play. These three players were caught up in the moment and took things for granted on the biggest stage of their sport, a time when small mental errors could make the ultimate difference in winning a game and the championship. We’ll also look at the WE if each play had ended with a different outcome. All WE are obtained from The Book: Playing the Percentages in Baseball, by Tom Tango, Mitchel Lichtman and Andrew Dolphin.

Jorge Soler (Age 24)

Game 3, Series tied 1-1

Bottom 7^th, Cubs trailing 1-0 with 2 outs

Triple to right field

WE before at bat: 29.1%

WE after triple: 35.0%

WE after next batter ends inning 26.5%

For the sake of this article, let’s assume (which is assuming a lot with an inside-the-park home run) Soler runs hard out of the batter’s box and rounds the bases for an inside-the-park home run, tying the game. The WE for the Cubs would have jumped to 52.2%, a major swing (nearly double) from where that inning ended. The Cubs lost that game 1-0 to give the Indians a 2-1 series lead.

Javier Baez (Age 23)

Game 5, Indians leading series 3-1, Cubs one loss from elimination

Bottom 2^nd, Cubs trailing 1-0 with 2 outs and a runner on 1^st

Strikeout, dropped third strike (ball in dirt), Baez does not run to 1^st base

WE before at bat: 41.9%

WE after strikeout: 39.4%

Again, for the sake of this article, let’s assume Baez runs hard to first on the dropped third strike and reaches base, which rarely ever happens in Major League Baseball. However, this is the World Series and Game 5 is an elimination game. You never know what can happen. Though the data for the number of baserunners reaching on a dropped third strike isn’t available, such instance would be scored with either a passed ball or wild pitch on the play (a battery error). Take a look at the number of passed balls and wild pitches in MLB over the past 10 seasons, as provided by Baseball Reference.

A catcher is scored with a passed ball on average every 14-15 games, while a pitcher is scored with a wild pitch on average every third game. If anyone can provide data for the number of times a batter has reached base on a dropped third strike, it would only strengthen the claim that Baez’s chances of reaching base were slim. Regardless, remember it’s an elimination game in the World Series. For the sake of proving a point, let’s look at the scenarios if Baez had reached base.

The ball did skip quite a distance from catcher Robert Perez, so let’s take a look at WE if Baez reached first, leaving the Cubs with runners on first and second with 2 outs: 44.1%.

How about if Roberto Perez threw the ball away into right field, causing a 1^st and 3^rd situation (let’s note that catcher errors are also very rare): 45%.

Even though Baez reaching first base on a dropped third strike (which was far from guaranteed by running) would have only added about 5-6% to the Cubs WE, there is no excuse for Baez to have a lapse of effort and allow Perez an easy, no-pressure throw to first base because there was no runner hustling down the line. At the very least, run hard and make it look good for the millions of people watching. Not to mention the thousands of people who spent a week’s wage on tickets to Wrigley. They, along with your teammates, want to see you running to first base instead of walking back to the dugout.

Instead, the Cubs were left with their two weakest hitters (David Ross and Jon Lester) to lead off the next inning, which resulted in a 1-2-3 inning for Indians starting pitcher Trevor Bauer, who took a 1-0 lead into the fourth inning.

Anthony Rizzo (Age 27)

Game 5, Indians leading series 3-1, Cubs one loss from elimination

Bottom 4^th, game tied 1-1, 0 outs, first batter after Kris Bryant game-tying home run

Double to right field

WE before at bat (after Bryant home run): 56.3%

WE after double: 63.4%

The back-to-back extra-base hits certainly turned the momentum of the game in the Cubs’ favor. Two batters (Bryant and Rizzo) increased the Cubs’ WE from 43.7% to 63.4%, a major increase in a game they eventually won 3-2 to force a trip back to Cleveland for Game 6.

A better throw from right field would have made a very close play at second base, so let’s look at the WE for the Cubs had Rizzo been thrown out at second base on his hit off the right-field ivy: 53.4%. His lack of hustle from home plate to first base, as he admired what he thought was a go-ahead home run, could have cost the Cubs 10% on their WE.

Conclusion

There is certainly no guarantee that any individual exertion of hustle will lead to a different outcome in a baseball game. Running out a groundball will not guarantee an infield hit, but it puts pressure on the fielder to make a clean play. Running hard on a fly ball has no measurable effect on whether a fielder will catch it or not, but it puts the runner in the best possible position to advance an extra base on a rare dropped ball. Running hard to first base after a dropped third strike does not make a difference in the outcome of the play 99% of the time, and it certainly doesn’t change the 0-for-1 with a strikeout in the box score. But it puts pressure on the catcher to retrieve the ball in a clean manner and make an accurate throw to first base. Let’s not forget that hustle is the right thing to do. It’s more about the precedent, not the result. It’s about the example we want MLB players on the biggest stage to set for younger players worldwide. DiMaggio likely wouldn’t recognize some aspects of today’s game. He, like many other players, never took anything for granted. Despite the fact that Soler and Rizzo still ended up with extra-base hits, and Baez most likely would have been thrown out at first base anyway, shouldn’t we hope that on this kind of stage the very best will play the game to its absolute potential?

Among the astounding phenomena abundant throughout calculus, linearization remains one of the least glamorous. It’s incredibly simple, taught in less than a day, and a more precise (and more complicated) method can often be substituted for it. On the other hand, it’s an incredibly powerful tool and one with weighty implications for fantasy baseball. Because of the concept’s relative simplicity, a reader with even the most basic inkling of what calculus actually is should be able to understand the idea of it, so don’t let a fear of mathematics deter you.

First, let’s think about graphs, functions, and derivatives. Put simply, continuous functions, whether they’re linear, quadratic, or exponential, will generally experience some rate of change — slope. Think of it as the change in the y direction per unit change in the x direction between two points. This is considered a secant line, or the average rate of change between two points. More interesting, however, is the concept of the tangent line, or the instantaneous rate of change at a given point. Note that the tangent line only touches the function at one point rather than two, meaning that we can easily evaluate and analyze the rate of change when comparing two points on a curve. Importantly, the magnitude of the slope of the tangent line tells us the rate by which the function is increasing or decreasing. So the greater the slope, the faster it is increasing (perhaps indicating an exponential function), and the lesser the slope, the more it is decreasing (a negative quadratic).

In calculus, the formula for linearization is:

L(x) = f(a) – f'(a)(x – a)

Here, given some value of a, we get a y-value, or f(a). From there, we subtract the product of the derivative of f(a) and the difference between the value we are estimating, x, and the value we already have, a. This gives the linear approximation and we get a pretty good estimate.

When rendered down to its most basic essence, linearization is a glorified form of estimation that gives credence to gut instinct through a formula. Using the tangent line at a certain point, one can make very incremental estimations, but it’s important to note that they must be very small. The farther from the initial point a that one travels to find an approximation of y, the less accurate the result will be.

It seems that this would have little application to baseball, but that’s incorrect. Recently, I started toying with a couple of formulas that could actually have some importance in the realm of amateur fantasy baseball with the usage of a regression line for an entire player’s career in pretty much any statistic.

L(x) = f(k) – f'(a)(x – a)

Here, f(k) is the actual value at the known point (k), f'(a) is the derivative of the predicted point on the regression line, x is the point for which we are predicting the value, and a is the value we start from.

L(x) = f(a) – f'(a)(x – a)

Differing here, f(a) is the predicted value at the regression line, f'(a) is the derivative of the predicted point on the regression line, x is the point for which we are predicting the value, and a is the value we start from.

I don’t know which would work best, but my guess is that first formula would be most accurate due to its mix of actual and predicted values. Neither of them would be terribly precise, but it’s a heck of a lot better than relying on what you feel might be best.

Regardless of which formula you might prefer, the implications of the linearization idea as applied to fantasy baseball are apparent. Probably best used for 10-day predictions, linearization mixes short-term performance with long-term talent to assess how well a player might perform for a short period of time — whether he’s likely to continue streaking, slumping, or somewhere in between. Rather than having to rely on gut instinct or dated and/or biased statistical analysis, a fantasy player could rely on some concrete math to make short-term decisions. This would be especially helpful in leagues that play for only a month, or can only alter their rosters once a week, or even at the end of a highly competitive season (perhaps making the risky move of dropping a slumping MVP for the streaking rookie).

It’s understandable if it’s unclear how to use one of the formulas at this point. To simplify matters, let’s use formula 1 to demonstrate how this might work in regard to something as simple as batting average. So what you might have is a regression line for a player of rolling 10-game predicted batting averages plotted along with actual values. In this case, x-values are 10-game rolling averages by each 0.01 (the intervals are arbitrary). So 1.1 is the x-value at 110 games played, while 1.2 is the x-value at 120 games. Let’s just say for simplicity that the player has played 110 games in his career, had an actual average of .264 during the last 10-game stretch, and the derivative of the regression line at this point is 0.12. We want to guess his average for the next 10 games, up to career game number 120.

L(1.2) = .264 – (0.12)(1.2 – 1.1)

L(1.2) = .254

We’d expect him to hit .254 over the next 10 games. Hopefully that makes some sense. Obviously it’s still in development and I haven’t done a whole lot of research yet, but expect some to come out later along with some clarifying material if necessary. Confusion is to be expected, but with some explanation applied linearization could potentially help a lot of people out next season in fantasy.

I’ve watched many a baseball game over my lifetime (that’s 50+ years), and I’ve cringed every time I see a National League manager send his starting pitcher up to bat any time prior to the seventh inning. Especially with runners on base! Doesn’t he know that pitchers can’t hit? Doesn’t he know that if he would just pinch-hit for the lame-batting starter he’d improve his team’s chances of winning?

So, after years of pondering this problem for five seconds at a time every couple of days, I decided to see if I could build a solid quantitative case for never letting a pitcher come to the plate for a National League team (obviously this is not an issue for the American League with their designated hitters). How would this change the look of the team’s pitching staff? And more importantly, how many more games would a team expect to win in a season if they adopted a “pitchers never bat” strategy?

The answer to the first question is pretty easy. The staff would “look” different. There were would be no more “starting pitchers.” A team’s pitching staff would consist only of “relievers.” Sure, one of the “relievers” would throw the first pitch of the game and could technically be called a “starter,” but given that he’ll be taken out of the game as soon as his spot in the batting line-up comes up, he’s effectively a “reliever,” just like the other 10 or 11 guys on the staff.

Now, the conventional wisdom would say that the current starting pitchers, especially the “aces,” get in a groove, and can give you six or seven solid innings. Why would anyone take them out the game in the second or third inning? Well, let’s do a “cost-benefit” analysis and see if we can make a case for “The Pitchers Never Bat” strategy.

Key Components of the Case:

The two primary components of the analysis are 1) how many more runs would a team expect to score in a season by pinch-hitting for every pitcher, and 2) how many more runs would a team expect to give up in a season because their starting pitchers are no longer going six, seven, or more innings in an outing? Or, maybe the team adopting such a strategy would actually give up FEWER runs per year by giving up on the century-old strategy of planning for the starting pitcher to pitch deep into the game.

A third component of the analysis could include the benefit of being able to choose from any of the team’s entire staff (probably 11 or 12 pitchers) and use only the ones that look like they’ve got their “stuff” while warming up before the game, instead of sticking with the “starter” who is scheduled to pitch today because it’s his turn in the “rotation.”

A fourth component of the analysis could include the benefit a team could achieve because the other team can no longer stack their starting batting order with a lot of lefties (to face a right-handed starter), or with lot of righties (to face a left-handed starter), because the team with no “starters” will pinch-hit for their first pitcher after one, two, or three innings. So, in total, the “handedness battle” tilts slightly more in favor of the team implementing the new strategy.

A fifth component could include the cost (or benefit) of reducing the size of the pitching staff by one or two, and adding one or two more everyday players, who would be needed to pinch-hit in the early innings.

A sixth component could be an added benefit that batters will not be able to get “used to” a pitcher by seeing them multiple times in a single game. Under the new strategy batters will see each pitcher once, or, at most, twice in a game.

I’m going to focus on the two primary components above, and let the lessor components alone for now. Perhaps others can weigh in on how to quantify the potential impacts of these changes.

Component #1: How much more offense will the “Pitchers Never Bat” strategy create?

This is the easiest of the components to quantify. I will use the wOBA (weighted On Base Average) statistic as defined and measured by FanGraphs to evaluate this component. Let’s start with some basic information and rules-of-thumb.

Using data from the National League for the 2015 season I find that pinch-hitters have a wOBA of .275 across the entire league, while pitchers, when batting, had a wOBA of just .148 across the entire league. The difference in wOBA between pinch-hitters and pitchers is .127 (that’s .275 minus .148.) Note that all position players in the NL combined for an average wOBA of .318 in 2015. I’m assuming that our new pinch-hitters won’t get anywhere near that figure, but will be comparable to the 2015 pinch-hitters, who came in way lower, at .275.

Now, let’s assume we can replace every pitcher’s plate appearance (PA) with a pinch-hitter. This improvement of .127 in wOBA needs to be applied 336 times per season, because that was the average number of times that a National League team sent their pitchers up to the plate in 2015. And lastly, we need to know two rules of thumb from FanGraphs that are needed to complete the analysis of the first component: 1) every additional 20 points in wOBA is expected to result in an additional 10 runs per 600 plate appearances, and 2) every 10 additional runs a team expects to score in season translates into one additional win per year. OK – so, let’s do the math:

If 20 additional points of wOBA translates into 10 runs per 600 PA, then our new pinch-hitters who are now batting for pitchers will provide the team with 63.5 incremental runs per 600 PA (which equals 127/20 * 10.) And since these pinch-hitters will be coming to the plate 336 times, not 600 times, we need to reduce the 63.5 incremental runs per season down to 35.6 incremental runs per season (which is 336 / 600 * 63.5).

Finally, the last step is to take our 35.6 incremental runs per season and translate that into incremental wins per year using the rule-of-thumb that ten runs equates to one win. Therefore, our 35.6 extra runs results in an expected 3.6 incremental wins per year. That’s a decent-sized pick-up in expected wins.

OK, so now, what about the pitching staff? Will replacing the conventional pitching staff with a staff consisting of no starters and all relievers cause the runs allowed to increase, and if so, by how much? Enough to offset our 3.6 extra wins that we just picked up on offense?

Component #2: How many more runs will pitchers give up using the “Pitchers Never Bat” strategy?

Imagine, for the moment, that a GM is to build his pitching staff from scratch. (We’ll worry about how to transition from a conventional staff to an all-reliever staff later.) And let’s just assume he’ll pick just 11 pitchers. (Most NL teams use 12-man staffs while some use 13, so that will give the team one or two additional position players.) Currently, starting pitchers typically throw 160-200 innings per season, and relievers tend to throw 50-80 innings per season. But with the new all-reliever strategy, and using only 11 pitchers, each of our new guys will need to average around 130 innings each, with perhaps some pitching as much as 160, and some as low as 100 innings per year. So, the GM is looking for 11 guys who can each contribute 100-160 innings per season. Each outing will be for about one to three innings for each pitcher. How will they fare?

Let’s look at the National League’s pitchers for 2015. Starting pitchers had an aggregate WHIP (Walks Plus Hits per Inning Pitched) of 1.299, while relievers, in total, recorded an identical WHIP of 1.299. So my takeaway from this is that the average starter was equally as good (or bad) as the average reliever. From this, I am going to take a leap of faith, and assume that a staff of 11 new-style relievers could be expected to perform equivalently. (And that doesn’t even factor in some of the lesser elements of the new strategy, as mentioned above, such as Components 3 and 4 of the analysis.)

From this, albeit simplified, evaluation of Component #2, I estimate that a team moving to an all-reliever pitching staff will have an expected change in Runs Allowed of zero, and therefore the change will neither offset, nor supplement, the offensive benefit evaluated in Component #1.

Conclusion and Final Thoughts

In summary, using the two primary components of my analysis, I estimate that adopting a “Pitchers Never Bat” strategy in the National League (a.k.a. an “All Reliever Pitching Staff” strategy) will improve a team’s offense by an expected 36 runs per year, which will increase the team’s expected win total by 3.6 games. I estimate that the impact on runs allowed will be near zero. Some lesser elements, Components #3 through #6, could also add some additional value to the strategy.

Implementing the strategy does not necessarily need to be a complete, 100% adoption of the “pitchers never bat” rule. Modifications can be made. Perhaps a pitcher is doing well through two innings and comes to bat with two out and no one on base. In this case the manager could let the pitcher bat, so that he can stay in and pitch another two or three innings. This would change the name of the strategy to something like the “Pitchers Very, Very Rarely Bat” strategy.

As far as transitioning to an all-reliever staff from a conventional staff, it could be done over time, or only in part, such that a team could maintain, say, its two top aces, and complement them with eight or nine relievers. This way, the aces could pitch as they do now, going six-plus innings, every fifth day, while limiting the “Pitchers Never Bat” strategy to the three out of the five days when the two starters are resting.

Finally, let’s try to put a dollar value on this new strategy. The guys at FanGraphs, and other places, have tried to estimate how much teams are willing to pay for each additional win. Without going into all the various estimates and approaches at trying to answer that question, let’s just go with a simple $8 million per win. I’m sure it could be argued to be more or less, but let’s just put $8 million out there as a base case. If that’s true, a 3.6-win strategy, such as the “Pitchers Never Bat” strategy, is worth about $29 million per year. Go ahead and implement the strategy now, and, if it takes, say, three years before any of the other NL teams catch on, you’ve just picked up a cool $87 million (3 * 29 million).

And if the other components of the analysis (#3 through #6) are quantified and it can be determined that they add another 0.5 wins per year, which I think is quite doable, then we can get the total up to 4.1 wins per year, for a value of $33 million per year, or just around a cool $100 million over the first three years. And that’s how you make $100 million without really trying!

With the special attention pitchers receive today, such as pitch counts, innings limits, as well as the host of PITCHf/x data that can notify teams of when a pitcher is fatigued, it seems like they days of 300-game winners have come and passed. And for the most part, some of this is true. We’ve seen pitchers be shut down during their earlier years to prevent injuries, such as the Nationals keeping a close eye on Stephen Strasburg. When we think of 300 wins, the math isn’t that hard. It’s some combination of 15+ seasons of 15+ wins over an entire career. Let’s dive in to what further breaks down these pitchers.

I gathered data on pitchers who finished their careers after 1980 as well as pitchers younger than that; I did this to avoid looking at pitchers such as Cy Young who are a little tough to compare to the modern day, with rule changes and the different run-scoring environments. In my query, I looked at pitchers with at least 250 wins. This gave me more data, and since 250-win pitchers are reasonably close to 300, it will allow me to get at what exactly creates a pitcher of this caliber.

My list included 19 names:

Greg Maddux

Roger Clemens

Steve Carlton

Nolan Ryan

Don Sutton

Phil Niekro

Gaylord Perry

Tom Seaver

Tom Glavine

Randy Johnson

Tommy John

Bert Blyleven

Fergie Jenkins

Jim Kaat

Mike Mussina

Jamie Moyer

Jim Palmer

Andy Pettitte

Some of these guys were absolute iron men, pitching over 5000 innings in their career. Maddux did this, as well as Carlton, Ryan, and Sutton. Most of this group barely reached 12 wins per season, showing that they reached the 300-club with longevity, not necessarily dominance. The other guys on this list, by default, either had higher win totals or pitched forever, but without racking up a ton of innings (Kaat, Moyer). Surprisingly, or perhaps not, only four of the 19 pitchers did not pitch for 20 seasons, so again, dominance might not be the key factor — instead, longevity.

I then looked at where these pitchers were at when they were 30 years old. Thirty years seems to be about a halfway point, but the data indicates otherwise. In fact, only three of these 19 pitchers had at least 150 wins at 30. This again drives home the point that these pitchers do not necessarily have to be untouchable every single year they pitched; it just means they have to be pitchers that stay healthy and can pitch for a long, long time. At the same time, the average pitcher on this list had 115 wins at 30, so they did need to have a productive youth in terms of racking up wins.

Here is a table displaying the careers of our 19 pitchers:

The amazing part, at least in my opinion, is that these pitchers almost seemed to get better with age, at least in terms of wins. I know that wins is not a good stat for tracking the effectiveness of pitchers, but since we are talking the 300-win club, it is what we have in front of us. Anyways, 17 of these 19 pitchers had more wins after 30 than they did before. Again, this hammers home the idea that longevity and durability is more important than complete dominance. Yes, you have to be a good, if not great, pitcher, but you also have to stay healthy.

So when looking at current pitchers that possibly have a chance at 300, I filtered through active pitchers fulfilling a few different qualifications. First, the pitcher must have at least 190 innings pitcher per year, including years of injuries (this helps get at longevity and durability). Also, the pitcher must also average at least 12 wins per year. I came up with a group of pitchers who where close to matching these requirements. From this list of 14 pitchers, I think eight or so have the best chance of eclipsing 300.

Here is a table of possible contenders:

This list includes: Clayton Kershaw, Chris Sale, Justin Verlander, Madison Bumgarner, David Price, Rick Porcello, Jon Lester, and Felix Hernandez. CC Sabathia, although at 223 career wins, does not make this list, since I don’t think he has 5-8 more seasons of decent pitching in front of him. I will go into each pitcher in more detail to describe what each pitcher needs to do to have a chance.

I’m going to start with Lester. Lester is currently at 146 wins, with 2003 regular-season innings pitched. He has been great through his first 11 seasons, in nine of which he was a full-time starter. In those nine seasons, he failed to pitch 200 innings just once, when he posted 191.2 innings pitched. He has been an iron man, and at age 32, the recipe is simple. He just needs to stay healthy and he needs his game to age well. This is going to be a repetitive theme, but to be honest, that’s what we would expect. Things helping Lester? Well, playing for the Cubs is one. Not only do they have a great defense, but they also create great run support, which can help Lester pick up a lot of wins. He was 19-5 this past year, matching his career high in Boston in 2010.

Now on to Justin Verlander. After an injury-riddled 2015, Verlander was great this year, posting a 16-9 record and an ERA of 3.04 (FIP of 3.48). Currently, he sits at 173 wins and is 33 years old. I mentioned his injury struggles in 2015. He only pitched 133 innings. In his 11 years as a full-time starter, that was the only the second time he failed to reach 200 innings pitched. People may worry that Verlander is starting to lose his velocity, which could mitigate his effectiveness, but in 2016, he struck out batters at a career-high rate and also had a career-best strikeout to walk ratio. Verlander is back with the elite, and if he can avoid injury trouble, he deserves to be in the discussion for a possible 300-win flirtation.

I’ll now move on to Clayton Kershaw. Kershaw has been the best pitcher in baseball for the past five years, and has only struggled with injuries for this past year, when he hit the DL with back issues. He still picked up 12 wins, and looked like peak Kershaw when he came back. Kershaw continues to strike out hitters and not allow walks, and in his shortened 2016, he posted a career-best FIP. Kershaw currently sits at 126 wins, and is 28 years old, in the middle of his prime. I think there are two factors that could keep Kershaw from getting close. The first one is his back. The Dodgers shut Kershaw down for half the year, and hopefully it heals, but if it is one of those lingering injuries that can also affect his timing a delivery as well as his overall health, he won’t be able to age his game to the necessary limits needed to hit 300. Also, he should get more wins. I’m not sure this will be a big factor now that the Dodgers have Andrew Friedman at the helm, but if he cannot get the run support he needs, that could lead to two or three fewer wins every year.

Chris Sale is next. Sale sits at 74 wins and is 27. He has some work to do. He has been relatively healthy, however, over his five full years as a starter. I think the best bet for Sale is to get out of Chicago, or at least the White Sox, and get on a team that can give him some good defense and offense. His win totals just aren’t high enough, but he is young enough where if he finds a new team and can age well, he might be able to hit 250.

I’ll do Bumgarner next. He really hasn’t had any injury trouble in his six years as a full-time starter. He is 27 and has 100 wins. He is a little harder to project, but I would say he’s got a better shot than Sale. I mean, he is already at 100 and only 27. Kershaw might have a leg up on him, but MadBum has been able to stay healthy. To be honest, Kershaw had been healthy too before this year, which somewhat shows that pitching 20 full seasons does not happen to often. Anyways, Bumgarner hasn’t quite been as dominant as some of the other names on this list, but he has been very good, and has stayed healthy. He is on a solid team with a good defense. The conditions are correct, he just needs to age well and stay healthy. I still like Kershaw’s odds a little more, but Bumgarner’s are not far behind.

Now I’ll move on to David Price. Price is 31, has 121 wins, and has pitched relatively healthy for seven full seasons. He is on the Red Sox now, which — although their poor defense won’t help some of his pitching metrics, they should give him the run support he needs. He wasn’t terrible this year; I have a feeling people think he fell off the map. He had 17 wins, and a ERA of 3.99 and a FIP of 3.60. His ERA and FIP were at career highs, but the FIP really wasn’t too far off what we’d expect. I’d credit the higher ERA to playing in Fenway with not the best defense behind him. Price may not be as dominant as he once was, but the Red Sox should give him support. He might be a little behind pace, but he could be the next CC Sabathia or Mike Mussina, where upon retirement, we say, “I didn’t realize he had 260 wins!” For the record, I doubt CC gets there, but the point is that if Price can stay healthy and moderately effective on a team that will support him, he may be able to move up in the wins chart. Will he hit 300? I don’t see it, but realistically, I’m not sure any of these guys will.

Now I’ll move on to the other Red Sox pitcher on this list: Rick Porcello. Porcello had a modest beginning in Detroit, but his FIP always seemed to outperform his ERA, so he has that going for him. Porcello is only 27 and somehow has 107 wins already. Although he is on the Red Sox, who can support him, Porcello really hasn’t been able to stay healthy over his career, and only eclipsed 200 innings pitched in a season twice: 2014 in Detroit, and this past season in Boston. Still, he is young, and if he can hang around awhile, he might be able to pick up 100 wins or more if he can stay decent on an offensive team. Again, he doesn’t need to contend for the Cy Young, but he has to stay relatively effective, so he keeps his starting spot and racks up wins.

Finally, I move on to my dark horse, King Felix Hernandez. Felix is only 30, but has been a full-time starter for 11 years. He sits at 154 wins. I feel like as a baseball community, we tend to forget about Felix. He has been very durable, although he hit the DL this past season by injuring his calf when celebrating a win. But hey, forgive the guy; he plays in Seattle, who hadn’t given him much help until recently. He is my dark horse on the list. He now plays on a good Seattle team, so he should be able to pick up wins. He might not be as good as he once was, but if he can stay effective, he has the best chance of anyone on this list. He can age well, he has stayed healthy, and he now plays on a winning team. The conditions are there, and I think he has the best shot of anyone on this list.

Realistically, if I had to choose between none of them winning 300 or one of them winning, that would be a much harder choice than picking one out of the group. Realistically, do I think any of these guys have a shot? Sure, but a shot is a lot different than actually getting there. Who knows, maybe one of these guys will age well and will stay healthy. Your guess may be as good as mine.

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. 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 teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “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

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

Assessment

The 1902 Chicago Orphans 

OWAR: 37.4     OWS: 280     OPW%: .527     (74-66)

AWAR: 29.9      AWS: 203     APW%: .496     (68-69)

WARdiff: 7.5                        WSdiff: 77  

The 1902 “Original” Orphans finished in third place, ten games behind the Reds. Bill Bradley (.340/11/77) thrived against opposing hurlers, notching career-bests in base hits (187), runs scored (104), doubles (39), home runs and batting average. “Bad” Bill Dahlen drilled 25 two-baggers and swiped 20 bags. Danny Green delivered a .302 BA and pilfered 35 bases. Jimmy “Pony” Ryan slashed 32 two-base knocks and produced a .320 BA. Johnny “Noisy” Kling succeeded on 25 stolen base attempts. Jimmy “Rabbit” Slagle executed 41 thefts and supplied a .315 BA for the “Actual” Orphans.

Bill Dahlen rated twenty-first among shortstops in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” Orphans teammates registered in the “NBJHBA” top 100 rankings include Frank Chance (25^th-1B), Johnny Evers (25^th-2B), Jimmy Ryan (26^th-CF), Joe Tinker (33^rd-SS), Bill Bradley (46^th-3B), Johnny Kling (48^th-C) and Tom Daly (55^th-2B). “Actuals” second-sacker Bobby Lowe placed fifty-sixth.

  Original 1902 Orphans                          Actual 1902 Orphans

Jack W. Taylor (23-11, 1.29) paced the National League in ERA, shutouts (8) and WHIP (0.953). Mal “Kid” Eason contributed 10 victories with a 2.76 ERA and Carl Lundgren (9-9, 1.97) completed 17 of 18 starts during his rookie campaign. Jock Menefee (12-10, 2.42) and Pop Williams (11-16, 2.49) rounded out the rotation for the “Actuals”.

  Original 1902 Orphans                         Actual 1902 Orphans

Notable Transactions

Bill Bradley 

Before 1901 Season: Jumped from the Chicago Orphans to the Cleveland Blues. 

Bill Dahlen 

January 25, 1899: Traded by the Chicago Orphans to the Baltimore Orioles for Gene DeMontreville.

March 11, 1899: Assigned to the Brooklyn Superbas by the Baltimore Orioles. 

Danny Green 

Before 1902 Season: Jumped from the Chicago Orphans to the Chicago White Sox. 

Jimmy Ryan

Before 1902 Season: To the Washington Senators in unknown transaction.

Charlie Irwin

July 11, 1901: Released by the Cincinnati Reds.

July 12, 1901: Signed as a Free Agent with the Brooklyn Superbas.

Honorable Mention

The 1966 Chicago Cubs 

OWAR: 43.3     OWS: 235     OPW%: .510     (83-79)

AWAR: 27.1       AWS: 176      APW%: .364    (59-103)

WARdiff: 16.2                        WSdiff: 59

The “Original” 1966 Cubs placed fourth with a record north of .500 yet fifteen games off the pace of the Giants. Ron Santo (.312/30/94) merited Gold Glove honors for the third straight season and paced the circuit with 95 bases on balls and a .412 OBP. Lou Brock aka “The Franchise” tallied 94 runs and topped the National League with 74 stolen bases. “Sweet Swingin’” Billy L. Williams socked 29 long balls and registered 100 runs scored. Al “Red” Worthington (2.46, 16 SV) fashioned a 1.018 WHIP and secured the late-inning leads. Ernie “Mr. Cub” Banks contributed 23 two-baggers and a .272 BA. Ken Holtzman collected 11 victories while furnishing an ERA of 3.79 in his inaugural season.

On Deck

What Might Have Been – The “Original” 1921 Tigers

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

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

The Giants started off their 2016 campaign with a 57-33 record before the All-star break, before finishing 87-75. There were plenty of downfalls in the second half of the season, but ultimately the bullpen led the Giants to their fate.

In the first half of the season the combined ERA of the bullpen was 2.27, with 26 saves and a K/9 of 9.7. This being said, they had 42 save opportunities, which means they blew a save 38% of the time. In the second half of the season they combined for a 2.85 ERA, with 17 saves and a K/9 of 8.4. They blew 13 saves in 30 opportunities during the second half, which means they blew a save 43% of the time.

The bullpen was heavily criticized in the second half of the season due to the team’s inability to replicate the same win rate they saw in the first half. However, the bullpen was only slightly better in the first half then it was in the second half.

To me, the Giants were in dire need of acquiring a threat in the bullpen before the trade deadline approached. They went after Will Smith, who came in to the Giants’ pen with a 2.12 ERA, 7.9 K/9 and three blown save opportunities. With the Giants he had an ERA of 2.94, a 12.8 K/9 and a blown save. He was not able to convert a save all season, and although he proved to be a nice piece in the bullpen in hold situations, he was not a guy who could come into the 9th inning and dominate the game.

In the postseason the Giants were 0/2 in save situations and, in their final game against the Cubs, their bullpen collapse was maybe the worst the league has ever seen in the playoffs. However, their rookie Ty Blach came in for 3.2 innings of relief during the postseason and did not allow an earned run. He looked promising at the end of the regular season and pitched well in high-pressure situations during October baseball. It was surprising to see him and Santiago Casilla sit out their final game, as they watched their bullpen drop four runs in the 9th. Furthermore, we saw Clayton Kershaw close the Dodgers’ final game against the Nationals to move on to the NLCS. It would have been interesting to see what kind of performance Madison Bumgarner could have shown the Cubs’ batters in that final inning.

Finally, with the veteran relievers of Javier Lopez, Sergio Romo and Casilla needing new contracts for the 2017 campaign, and the Giants in need of finding someone who can come into a 9th inning and pose a legitimate threat, it will be interesting to see what the team does in the offseason to improve their bullpen. Here are my top five predictions for the Giants’ next closer.

#1:  Kenley Jansen:

It is unlikely that Aroldis Chapman will be looking for a new home this offseason, as he looks comfortable in Chicago and will have a hard time finding a team with that amount of talent. Jansen, however, may flee from the aging Dodgers, especially if someone is willing to pay. The Giants will have a bit of salary space to work with and would benefit greatly from this signing.

#2: Mark Melancon:

Although Melancon is a few steps below the elite Jansen and Chapman, he showed he can work a 9th inning as well as anyone this season. He may be a bit more team-friendly as far as salary space, and that may be intriguing to the Giants who will be looking to add a heavy-hitting left fielder.

#3: Jonathan Papelbon:

Papelbon was replaced by Melancon for the Nationals’ closing position in the second half of the 2016 season. He had a great first half, and showed he is capable of being a dominant closer in the MLB. However, his fight with Bryce Harper in 2015 and his rough second half of the season may make him a risky candidate. This may lower his cost and if the Giants are unable to sign Jansen or Melancon, they would be smart to see what Papelbon could do for their bullpen.

#4: Derek Law:

Derek Law debuted in 2016 and had a pretty good campaign. With a 2.13 ERA in 55 innings of relief, he may have a shot at being the Giants’ closer. However, it would be unlikely for him to start the 2017 season off as the Giants’ closer, unless they are unable to sign someone to fill that duty this offseason. He is an unlikely candidate, but if he can improve from his 2016 season, there is no reason he would not be able to become a legitimate MLB closer.

#5 Aroldis Chapman:

Chapman will likely return to the Cubs, especially if they make it to the World Series this October. However, he has been on three teams in the past two years, and if the Giants are able to show him more money than the Cubs, they might be able to acquire the hard-throwing lefty. If they do, they might lose the power they need to fill left field but they would come into the 2017 season looking stronger than they did a season ago.

The Chicago Cubs, hinting that this year they may have magick stronger than The Goat, recently brought the San Francisco Giants’ even-year playoff dominance to an end. It was an offensively offensive series; add the two teams’ OPS together and you’re just 100 points better than David Ortiz. The low-velocity Giants staff struck out a batter an inning, and both lineups walked at a lower rate than the unwalkable Royals. My working theory was that this series represented the final demise of the already waning power of the current edition of the Giants, and that the next chart-topping version of Big Head Bruce and the Monsters would have mostly new musicians. Turns out that this theory is only partially correct.

Your 2016 San Francisco Baseball Giants were actually a little better than the world-beating 2014 squad, at least when resort is had to statistics:

Stat                                            2016 (MLB rank)            2014 (MLB rank)

Position Player fWAR                   26.7 (4)                                  23.0 (9)

SP fWAR                                          15.0 (5)                                  10.1 (21)

RP fWAR                                           2.1 (22)                                  1.4 (24)

Position Player wRC+                    98 (t12)                                   99 (9)

SP FIP-                                              96 (t7)                                    104 (19)

RP FIP-                                              97 (20)                                    98 (18)

Run differential/game                    +0.51                                      +0.31

Let’s pause a minute to consider the bullpen numbers, which are the very essence of “meh” both years. The Giants have had the reputation of having a good, cheap bullpen. It’s certainly cheap: Sergio Romo is the plutocrat of the unit at a relatively unimposing $9 million. But “good” is more of a stretch; the Giants relievers have delivered value pretty much consistent with what they’ve been paid.

Some commentators have carpeted Bochy for his bullpen usage during the NLDS, but (perhaps because I’m not actually a Giants fan) I take a longer view. The miscellaneous roadies Big Head Bruce has had to work with will hardly make anyone forget The Nasty Boys, but he has often been able to squeeze value out of them when it’s mattered most. In order to maximize value out of this motley crue (I’m in town all week — try the garlic fries) Bochy has had to be very active in the late innings, and the more decisions any manager has to make, the more that will go wrong.

Giants general manager Brian Sabean has correctly recognized that in Bruce Bochy he employs one of the best tacticians in the game today. Sabean has maximized the value of this skill by handing Bochy a collection of misfit bullpen toys and saying “here, you figure this out.” On most nights Bochy does, but every once in a while he fails, as happened in the star-crossed six-pitcher 9th in Game 4. If you want to see what a bullpen meltdown looks like in graphic form, here it is. (Younger or more sensitive Giants fans are advised not to click on that link.)

My guess is that Bochy has had a few other bad bullpen nights, but most of those have happened when the East Coast was already asleep. When you happen to have a bad night nationwide, people may be a little too inclined to draw definitive conclusions. (I do not cut Buck Showalter this kind of slack. Bochy has a bunch of semi-interchangeable parts that present numerous non-obvious choices. Buck doesn’t.)

But back to our regularly scheduled program: the 2016 Giants were, by most measures, a better squad than the 2014 one. This is a roster that’s peaking, and perhaps fell victim to what will soon be a storied Cubs team, or (more prosaically) to the bad luck inherently possible in a short series. So the Giants can look forward to an extended run of playoff contention!

Or not. The Giants are heading in full sail toward the dragon-pocked part of the map. This an old team — the Giants have the sixth-oldest set of position players in the majors and the oldest pitching staff. They have just two regular players under 27, Madison Bumgarner (still just 26) and Joe Panik (25). To borrow a Casey Stengel line, in 15 years Bumgarner may be in the Hall of Fame. In 15 years, Joe Panik will be 40.

The Giants’ farm will provide little aid. Their system has just two MLB top-100 prospects, with the best being the positionless Christian Arroyo at #79 (though the excellent Bernie Pleskoff is less hostile to his defense than I am). Austin Slater isn’t in the top 100, but he raked at AAA at age 23 with good plate discipline, so he may be able to fill the outfield spot Angel Pagan is likely to vacate.

On the bright side, the contracts of Jake Peavy and Pagan expire this year, taking $26 million off the books. Romo and Santiago Casilla will be departing for broadcasting careers as well, taking $15 million more of liabilities with them. The Giants need one or two outfielders and starting pitching, but especially with respect to the latter, next year’s free-agent class would make a cow laugh. The 2018 list is a better one, but between now and both free-agent classes likely interposes a new collective bargaining agreement, so there’s enough fog to compel Sabean to operate his lights on low beam.

And the competition isn’t sitting still. Regardless of how the hated Los Angeles Dodgers fare in the NLCS, they are poised to compete for a while. The Rockies have an exciting core of young talent, even if casual Rox fans despair of the team at the moment. The Outlaw A.J. Preller merits a blog post all his own (say, there’s an idea!), and while the Padres seem to have a bit of transmission loss between talent and wins, some improvement there is possible as well, especially if Tyson Ross can make a successful return from thoracic outlet surgery. (What? You say there’s another team in the NL West? Hmm … I’ll research that and get back to you.)

So the Giants may be stalling or even slipping backward in a division where at least two of the teams are making progress. The Giants have a good but mostly older core which could use the kind of help that free agency and prospect trades are unlikely to provide in 2017. So 2016 may indeed be the last gasp of this once-in-a-while mighty franchise, at least for the moment. Sabean has pulled a whole warren of rabbits out of his hat during his long tenure, but in 2017 he’s going to have to dig deep.

Perhaps there will be a powerful goat looking for work …

Randomness and circumstances are important driving forces in everything that happens in the world. Although they usually work hand in hand with our own actions and decisions, they have the ability to pick you up when you hit the jackpot at the casino, or throw you down when your car gets crushed by a falling tree (hopefully you’re comfortably sleeping in your bed when that happens).  They can also be the difference between a pitcher having an average season on the mound, and having an outstanding one. Such is the case with the seasons Jon Gray and Kyle Hendricks had this year.

I’m not going to make the argument that these two pitchers performed equally well this season, with the main differences being random chance and circumstances, because they didn’t. Hendricks was the better pitcher; it just wasn’t the 2.48-run difference their ERAs show. The similarities between the two performances can be summarized in basically two stats. If we take a look at xFIP and SIERA (two important ERA estimators available here at FanGraphs), Hendricks’ numbers of 3.59 and 3.70, respectively, are eerily similar to Gray’s 3.61 and 3.72. From there on, however, the numbers separate abruptly.

Much like Dr. Jekyll and Mr. Hyde represent the good and the bad within a person, Hendricks’ and Gray’s seasons represent two sides of the same coin. On the one hand, circumstantial factors and good fortune turned Hendricks’ very good performance into a historical season, while a different set of circumstances and some bad fortune turned Gray’s good performance into merely an average one. In this piece, we’ll take a look at the factors that influenced these diametrically opposed results.

I’ll start by saying that Kyle Hendricks had a remarkable and impressive season. He had an average strikeout rate (8.05 K/9), didn’t walk many batters (2.08 BB/9), and allowed very few longballs (0.71 HR/9), which resulted in a really good 3.20 FIP, which ranked 4^th in the majors. His ERA, however, ended up all the way down to 2.13; a whopping 1.07 runs less than his FIP. Despite being a big difference, it’s not all that uncommon, as nearly 2% of individual seasons by starters in the history of the game have had an E-F (ERA minus FIP) of -1.07 or lower. Nonetheless, that difference is hardly sustainable through multiple seasons. In major-league history, out of 2259 pitchers with at least 500 innings pitched, only two had a career E-F below -1.00, and both of them were full-time relievers (in case you’re curious, they are Alan Mills and Al Levine).

On the other side of the spectrum, Jon Gray also had a very solid season. He had an outstanding 9.91 strikeouts per 9 innings (that ranked him 9^th among qualifying starters), an average walk rate of 3.16 BB/9, and a solid home-run rate (0.94 HR/9), lower than league average despite pitching half of his innings at Coors Field. His performance was good enough for a 3.60 FIP, but his actual ERA rocketed to 4.61. This 1.01 positive difference is just as unusual as Hendricks’ negative one, as about 2% of individual seasons throughout history have resulted in differences of 1.01 or higher. For visualizing purposes, here’s a table summarizing both pitchers’ numbers.

So the question still remains: what were the determining factors in these two pitchers having such a massive difference in results? Let’s dive right into it.

First of all, I decided to look at the correlation factors between E-F and a wide array of pitching stats, using data from every pitcher in MLB history with 500+ innings. As a general rule of thumb, a correlation factor between 0.40 and 0.69 indicates a strong relationship between the two variables. The following table shows the stats that had at least a 0.40 correlation factor with E-F:

Welp, that’s a pretty lame table. Keep in mind, I analyzed correlations for stats as varied as pitch-type percentages, pitch-type vertical and horizontal movements, and Soft, Medium, and Hard-hit rates, as well as K, BB, and HR per 9, or HR/FB%. None of those had even a moderate relationship with E-F. So let’s stick with the stats presented on the table.

The first two stats are really no surprise. FIP basically assumes league-average BABIP and LOB% to estimate what a pitcher’s ERA should look like. So, if a pitcher has a high BABIP, FIP is going to estimate a lower ERA than the actual one, resulting in a higher E-F; thus the positive correlation. On the other hand, if a pitcher has a higher LOB%, he’ll allow fewer runs than his FIP would suggest, resulting in a lower E-F. This explains the negative correlation shown in the table. The last stat, however, came as a real surprise, at least for me. ERA seems to be positively correlated with E-F, which means that pitchers with higher ERA tend to have higher E-F than pitchers with lower ERA.

The next logical step would be to determine which factors, if any, explain BABIP and/or LOB% among pitchers. Using the same pitching stats than in the previous step, I ran correlations with BABIP and LOB% separately. The following table shows the stats that had a strong (0.40 to 0.69) or moderate (0.30 to 0.39) relationship.

As was the case in the first table, both of these stats are correlated strongly with E-F, showing factors of 0.58 and -0.42, respectively. It doesn’t come as a shock either, that they are strongly correlated with each other. The negative correlating factor (-0.42) indicates, as you would expect, that a high BABIP leads to a low LOB%, and vice versa. On the BABIP side, a positive strong relationship with ERA is almost too obvious, as more balls in play falling for hits leads to more runs being scored. Also, since fly balls in play (not counting home runs) turn more often into outs than ground balls do, it makes sense that BABIP holds a negative relationship with the former, and a positive one with the latter. This fact, however, goes against a somewhat popular belief that ground-ball pitchers tend to have lower BABIPs.

The factors that correlate to LOB% are more interesting. The first one is not unexpected: a higher strikeout rate seems to lead to more runners getting stranded, and that’s a pretty easy concept to wrap your head around. The second one, however, is really mind-boggling, and I really can’t say I can find a reasonable explanation for it. It indicates that the higher the home-run rate allowed by a pitcher, the more runners are going to be left on base. It is quite possible that this is just a spurious correlation, having no causality at all. Finally, the last factor listed on the table is very interesting and useful in this particular case. It suggests that high percentages of soft contact lead to higher LOB%. We’ll get to that later on in this article.

So let’s go back to our pitchers and check if any of this makes sense. We know that E-F is mainly affected by BABIP and LOB%. Hendricks and Gray had very different numbers in these two stats. The Cubs’ righty had a .250 BABIP and a LOB% of 81.5, while the Rockies’ fireballer had .308 and 66.4%. Considering that the league averages were .298 and 72.9%, respectively, we can say that Hendricks did considerably better than average, while Gray did just the opposite. So far so good, right? These facts go a long way towards explaining the differing outcomes. However, BABIP and LOB% aren’t exactly pitcher-dependent; in fact, they’re the marquee stats for the generic term “luck.”

Looking at the stats from the second table, few of them help out in figuring this out. High strikeout rates, for example, are supposed to increase LOB%, but Gray still managed a really low 66.4% despite a 9.91 K/9. On the other hand, Hendricks’ 81.5% LOB ranked 5^th among qualified starters, even though his strikeout rate of 8.05 was right around league average. Similarly, groundball percentage is shown to have a positive correlation with BABIP. Nonetheless, Hendricks’ higher-than-average rate of 48.4% (league average was 44.7%) resulted in a ridiculously low BABIP of .250, while Gray’s below-average rate of 43.5% came with a .308 BABIP. Almost the same thing happens when you look at the fly-ball rates.

The only factor from that second table that does make sense in these particular examples is soft-contact rate. Hendricks ranked 1^st in this regard among qualified starters, with an impressive 25.1% (league average was 18.8%), while Gray had a below-average rate of 17.8%, which ranked him 50^th out of 73 qualified starters. This stat is very much pitcher-dependent, and it does help explain some of the differences in LOB%. It has, however, a moderate relationship with LOB%, as evidenced by its factor of -0.37. Is that enough to account for the massive difference in the results? Intuitively, I’ll say no. There is one more factor, however, that we haven’t even discussed yet.

FIP stands for Fielding Independent Pitching, so the very thing that FIP is trying to subtract from the equation might hold the key to answering our question. Defensive performances can heavily influence the outcome of the game, and make up a big chunk of what we generally call “luck” in a pitcher’s final results. In order to have a numerical confirmation of this idea, I looked at the correlations between teams’ yearly defensive component of WAR and its staff’s BABIP, LOB%, and E-F. The data I used for this exercise was every individual team season from 1989 (the first year in which play-by-play data contained information on hits and outs location) to 2016.

We can see here that a team’s defense has a strong correlation with all three of the stats, especially E-F. Higher values of the defensive component of WAR lead to lower BABIP, higher LOB%, and lower E-F, just as you would expect.

Saying that the Cubs had a great defensive performance this year is an understatement. Not only was it the best defense in 2016 by a bunch — it was also the best defense of the last 17 years, according to FanGraphs’ defensive component of WAR. Of the 814 individual team seasons played in MLB since 1989, this year’s Cubs rank 8^th. That’ll put a serious dent on opponents’ BABIP. In fact, the Cubs’ average on balls in play of .255 (yes, that is the whole pitching staff’s BABIP) is the absolute lowest since the ’82 Padres. Oh, and also the Cubs pitching staff’s LOB% of 77.5% is tied for 2^nd highest since 1989. All of this adds up to a team E-F of -0.62. Wow. Just wow.

The Rockies defense, on the other hand, wasn’t bad, but it also wasn’t great. According to FanGraphs, it was 17.9 runs above average, which ranked 12^th in MLB. Again, that’s really not bad at all, just miles away from the 115.5 runs above average the Cubs had. The Rockies’ staff as a whole had a .317 BABIP, and a 68.0% LOB%; not unexpected from a team that plays half their games at altitude. Still, both of these values are worse than league average, resulting in a team E-F of 0.54.

All in all, Kyle Hendricks still had a better season than Jon Gray, and people will remember the 2.13 ERA and not the 4.61. This analysis just puts it a little bit more in perspective, and helps shed some light on the little details that make big differences in the course of a long season.

The old football adage says that “defense wins championships.” That doesn’t really apply to baseball, but in the future, when I think back to the 2016 Cubs, I’ll definitely think about their defense.