Some know him as “The Freak”, while others like myself know him as “Big Time Timmy Jim“. Tim Lincecum is planning on showing if he’s got anything left in the tank sometime next month. This year he had some problems with his hip and ended up getting surgery in mid-September. Here’s a link to a some info about hip labrum surgery for those who are interested. Early in his career he was one of the most dominant starters out there and you could make an argument that for a short period he was the most dominant pitcher in baseball. Over the last four years he’s become a dependable 4th or 5th starter, but the 2015 season was one of the worst of his career.

Age has seemingly caught up with another pitcher. Lincecum is yet another example of a pitcher whose velocity peaked early in his career and has been on a decline ever since. We don’t have PITCHf/x data for his rookie 2007 season, but we have the data for the rest of his career. Besides the 2011 season where he regained some form, he’s shown a pretty consistent decline in velocity over time.

To me, the obvious outlier is the most recent season where he saw his average fastball velocity dip below 88 MPH and about 2 MPH slower than the 2014 season. This is where we can see how his hip issues affected his velocity on the mound. Below is table with his peripheral stats (excluding his rookie season). To give a quick overview, K/9 has been trending downward, possibly relating to his diminished velocity. It doesn’t look like his BB/9 or HR/9 has any significant trend, but FIP has almost always been more generous than ERA.

As I said before, Lincecum recently had hip surgery and I assume he is nearing the end of his rehab since he’s planning a February showcase to try and secure another contract. Given his uncertain injury status, and his performance over the last four years, he’s likely only going to be able to secure a 1-year contract possibly with some performance bonuses. Teams are definitely taking a risk if they decide to sign him, since over the last two years he has been just slightly above replacement level, accumulating o.1 WAR in 2014 and 0.3 WAR in 2015. I’ll also mention that as a starter in 2014 he was worth 0.3 WAR, and he was worth -0.2 WAR as a reliever.

He’s certainly not the most imposing pitcher to ever set foot on the mound, standing 5′ 11″ and weighing in at 170 lbs (maybe with a wet towel wrapped around his waist); he’s one of those pitchers who needs to use his whole body to gain the necessary momentum to get those 90+ MPH fastballs. If you go back and look at the fastball velocity chart above it’s pretty clear that there was a significant drop in velocity this previous season. I think it’s pretty fair to think that his hip issues had something to do with that phenomenon. Here’s a link to an article from MLB Trade Rumors with some info about his surgery. I remember reading a more in-depth article earlier in the off-season saying that his hip issues were screwing with his mechanics, but I’ve been unable to find a link to that story. But the takeaway should be that he wasn’t healthy. He wasn’t able to generate the necessary power due to his hip issues and his velocity suffered as a result.

So the question becomes, if the surgery was a success and his rehab goes well, what can we reasonably expect from him for the upcoming season? Well that is definitely a tricky question since he’s almost 32, he’s two years removed from throwing in the 90s, and there’s the possibility that he won’t be back with the team that drafted him. I think in the best-case scenario we could see him start hitting his 2012-2013 velocity (~90.3 MPH) and if that’s the case we could start to see his K/9 creep up to around the 9.0 mark again. But that’s just my opinion and my opinion means basically nothing, so I’ll include a comparison.

I was only able to find one example of a pitchers who’d undergone the same type of surgery as Lincecum and that was Charlie Morton. In October 2011 he also underwent the hip surgery. You can check out his velocity chart below. He also had Tommy John the following June so if you’ll humour me and ignore the elbow issues you’ll see that his velocity over the 2011 season dropped from 94 to just under 92, only to return to 95+ after recovery from TJ.

Over the last two years Lincecum has amassed 0.4 WAR and made $35 million. There is no doubt that the Giants overpaid for his service over the last couple of years and I can’t see him getting anywhere near that annual salary. If we go by the market rate of ~$8 million/WAR, on a bounceback contract where a team expects a 0.5 WAR season we could see a contract in the ballpark of $4 million. Even that seems high to me; if I were to venture a guess I would put it around the $2-million mark with incentives. I’m definitely not saying he’s going to be the pitcher from five years ago, but a dependable 4th or 5th starter with the potential to strike out almost 200 batters sounds pretty awesome to me. You’ve always got to wonder if he’s got any magic left in him. Baseball is better with The Freak in it and hopefully he gets back on the mound soon.

The game is on the line. It’s the bottom of the 9th inning, runners on first and second with two outs for the Mets. Justin Turner drives a fly ball off the bat at a speed of 88.3 mph. All hope for the Braves looks to be lost. In a blink of an eye or just .02 seconds Jason Heyward reacts and races out of center field traveling 18.5 mph to make an incredible diving catch to save the game.

This data set was one of the earlier Statcast recordings released to the public. It shows how important such information could potentially be to clubs in the future. Statcast can record data such as Acceleration, Route Efficiency, Reaction Time, Max Speed, Distance Covered and more. Although not all of their data is available to the public, I wanted to further explore how a baseball club would benefit by using this technology to research defensive analysis on improving a player’s abilities and a club’s defensive positioning.

First off, a team could compile this data and separate each player’s metrics by direction. Players move differently when heading in different areas of the field. It’s obviously easier to move forward than running backward, so having this data would allow teams to identify key information and make comparisons down the road. This can be done so by separating a fielder’s range into eight different quadrants (see graphic below). Once that is done, averages are created based for each quadrant. For instance, on average, what is Brett Gardner’s route efficiency when moving right? When moving in quadrant 6, what is Charlie Blackmon’s average reaction time?

Quadrants

#1: Forward

#2: Right Forward

#3: Right

#4: Back Right

#5: Backwards

#6: Back Left

#7: Left

#8: Left Forward

All this information, separated into different quadrants, will help in visualizing and breaking down defensive ability. When we have averages of acceleration, max speed and reaction time it can create a visual graphic or “Statcast Range” to witness how much distance a player could potentially cover in a certain amount of time. For example, lets say Jason Heyward’s average reaction time, acceleration and max speed when going left was .02 sec, 15.1 ft/s^2 and 18.5mph respectively. We know using this information Heyward could cover approximately 81 feet in 4 seconds. Time can help us represent a player’s estimated “Statcast range.” Each player’s range will look differently as they may show in which directions they are better at fielding. We can then use this analysis to compare fielders and also adjust defensive positioning.

This information will help guide a team in improving its players’ abilities. Teams can compare players much easier and understand what flaws coaches must look into fixing. For example, if a fielder has below-average route efficiency or reaction time to a certain part of the field, this information can be relayed to the coaching staff to further improve a player’s ability over time. In order to put this in perspective, Eugene Coleman of the University of Houston found that the average major-league ballplayer ran 24 feet per second. Using this number, having 0.04 more seconds means the average major leaguer can cover 11.5 more inches of ground. That’s almost a foot more and within only .04 seconds. If a ballplayer cuts down his reaction time, improves his route efficiency, and more, he would be able save time in covering several more feet of ground and thus improving his defensive ability.

To adjust a player’s defensive positioning, a team would have to combine its knowledge from this analysis with the understanding of a hitter’s batted balls. If they know a certain player is a pull hitter and hits to certain parts of the field, they can track his batted-ball locations, hang time and exit velocities to project areas in the field to which he may hit. Using what we know about a fielder’s Statcast metrics and “Statcast Range “ a player’s positioning could be adjusted. Doing so would lead to more accuracy. Improving the range of a team’s fielders will help save distance and time. The ability to increase production of more outs will provide a club with a better advantage for winning the game.

Being a Rockies fan for most of my life, I’ve had my fair share of discussions about how a ballpark can affect not only the performance of the home team, but also that of the visiting team. At this point, I don’t think anyone has any doubt that Coors Field is a hitter’s park. However, there are a couple of questions regarding this park I’d like to address. First of all, is Coors Field alone in its capacity of enhancing offense, or is it comparable to other parks around the league? And secondly, is this effect stronger among Rockies’ hitters than it is for hitters from other teams?

To answer the first question, let’s compare offensive production at home versus on the road for each team, so we can see where the Rockies stand among the rest of the league in this regard. I selected a time frame from 1995 to 2015, simply because it is the same time frame that Coors Field has been hosting baseball games. For teams that moved to a new park during that time, we’ll consider only the seasons played in the newest stadium. The comparing stat we’ll use is OPS. I chose OPS instead of runs scored (which many park factors out there use) to take sequencing out of the equation. The order in which individual events occur in baseball can depend on things like lineup construction or managerial in-game decisions, but mostly it’s just random chance. I could have chosen a sounder, more sophisticated stat like wOBA, but OPS is more readily available, and a wider array of audiences are familiar with it.

After constructing a table for each team, consisting of year by year home and away OPS, I calculated the percent change of the two means, using the away value as the base. But simply comparing means can be very misleading. Randomness will always create a difference between two means, even if there is no actual effect causing it. In order to have some confidence that the differences we observe are statistically significant, I ran a Student’s t-test to each set of data (i.e. yearly home and away OPS for each team). The threshold of significance was set at 0.10, which means that there would be a 10% chance of seeing these differences if there were no real effect.  Anything above that value was considered not significant.

The following table contains the percent change for every team, along with its p-value. Red values don’t satisfy the significance criterion.

According to these numbers, 19 out of 30 ballparks have a statistically-significant positive effect on the home team’s offense, while 10 of them can be considered “neutral” due to the non-significant nature of the data, and just one (San Diego) has a significant negative effect on the home team’s offense.

At first glance, Coors Field seems to be in a league of its own when it comes to enhancing the home team’s offensive production. A common rule of thumb is that in a normally distributed data set, 99.7% of its values fall within three standard deviations of the mean. Any value outside of that range is considered an outlier. In this case, that range goes from -12.43% to 20.96%. Colorado, with its variation of 27.01%, falls way outside these limits, making it the only outlier of the group. This answers our first question, confirming that there’s no park that increases offense for the home team quite like Coors does. Which takes us to the second question: does it have a similar effect on visiting teams? Let’s crunch some numbers and see what they tell us.

The idea is to repeat the same process we used for answering the first question, only this time we’re going to use opponents OPS or OPS against, instead of the team’s own OPS. Basically, what we’re trying to do is compare how opponents’ offenses as a whole, change when they visit a particular park. In other words, and using Colorado as an example, we want to know how the league’s OPS against the Rockies is affected by playing at Coors Field as opposed to anywhere else.

Using the same methodology, here’s the opponents OPS change by park:

There are a couple of things to digest from of this table. First off, the fact that Colorado has the only park in which visiting hitters significantly increase their offensive production is pretty mind-blowing. It seems to me that we’ve been using the term “hitter’s park” way too lightly. Out of the 30 ballparks actively housing an MLB team, 19 have a statistically-significant negative effect on the visiting team’s offense. Just like in our first analysis, 10 of them can be considered “neutral”, with p-values above 0.10, and just one (of course, Coors Field) has a positive effect with a good degree of significance.

This seems to contradict the numbers showed in our first table. In fact, out of the 19 parks that enhanced offensive performance for the home team, 10 of them also have a negative effect on visiting hitters. How can this apparent contradiction be explained? Well, it probably has a lot to do with the all-encompassing concept that is Home Field Advantage. For whatever combination of reasons (familiarity with the park, sleeping in their own beds, having dinner with their families), playing at home seems to get the best out of most players. If you think of the visiting teams’ OPS as a pitching stat for the home team (which it is), then you can interpret the numbers in the second table as having 19 out of 30 parks with a positive effect on the home-team pitching staff, 10 being neutral, while just one of them having a negative effect. Coincidentally, that’s precisely a mirror image of the results we got when analyzing the first table.

Going back to the second question, does Coors Field have a greater impact on Rockies’ hitters than on the rest of the teams? The short answer is yes. The variation in OPS for Colorado players is 27.01%, while the equivalent for non-Rockies players is “just” 9.00%. So by just comparing these two values, it seems evident that the effect is in fact greater among Rockies’ hitters. The explanation could be again simply Home Field Advantage, but the difference is just too big. If we merge both tables in one, and consider the visiting hitters as a control group, then a simple subtraction should give us a rough estimate of the net effect of Home Field Advantage on home-team hitters.

Here’s that table. Red values were not considered in the subtraction since they were deemed non-significant.

Coors Field sits comfortably at the top, way ahead of Minute Maid, the second park on the list. Applying the same criteria for outliers we used before, Colorado’s Net Effect of 18.01% is not within the range of three standard deviations around the mean (-1.60% , 16.74%), once again being the lone outlier. It doesn’t look like that this is simply a result of Home Field Advantage; it seems there’s something else. This brings up a new question, one for which I’m not sure I have a definite answer: Does Coors Field undermine the Rockies’ ability to have a healthy offense on the road?

Let’s go back for a moment to the 27% increase in OPS for Rockies’ hitters at home. That number could mean a huge spike in offensive production when they play at Coors Field or a massive collapse when they hit the road; it depends on how you see it. Colorado ranks dead last in the majors in OPS away from home in the same time span we’re studying, so either they have been the worse offensive team in two decades (which is certainly an option) or something is causing them to consistently under-perform on the road. Of course, it doesn’t help that almost half of their games away from Denver are played in places like San Diego, Los Angeles, and San Francisco. In fact, according to the numbers in the second table presented in this piece, Colorado’s division rivals have the toughest combination of parks for visiting hitters. The average drop-off in opponents OPS in NL West parks (excluding Coors Field) is -7.15%. The following table shows that value for every team in the majors (for the purpose of this exercise, Houston was considered an NL Central team).

This definitely helps explain, at least partially, the abnormal home/away splits that Rockies’ hitters have had historically. Not only do they play their home games in the biggest, if not the only true hitter’s park in the game, but they also play a big chunk of their road games in three of the toughest pitcher’s parks in MLB.

The last question remains unanswered; the thesis of a Coors Field Hangover effect is largely unproven. Still, there’s a good amount of circumstantial evidence that points to the existence of something like it.

The Los Angeles Dodgers have five prospects in MLB’s Top 100, good for a sixth-place tie in the majors. These prospects include two in the top five (SS Corey Seager and LHP Julio Urias) as well as three RHPs in the top 60 (Jose DeLeon, Frankie Montas, and Grant Holmes). Two other Dodger prospects are among the top 10 at their respective positions (C Austin Barnes and 2B Micah Johnson). All this from a team that’s had just one losing season in the last 10, and currently has an eye-watering payroll of around $230 million, roughly the same as the Yankees. At that price you could field almost four whole Brewers teams.

All the Dodgers’ big dollar contracts (which I”m loosely defining here as $7 million AAV or more) except Clayton Kershaw’s will come off the books after the 2018 season, leaving the Dodgers with … a younger, cheaper roster that still wins a lot of games. Or maybe not — after all, prospects are gambles, and many crap out. But the Dodgers have put themselves in a position to at least have a reasonable expectation of success in the near future with a roster quite different from today’s, without taking up extended residence in the damp, roach-infested divisional cellar.

The five teams with more top-100s than the Dodgers have lost a lot more games while accumulating those prospects. Colorado, Atlanta, Cincinnati, Minnesota and Philadelphia have had a combined 25 losing seasons since 2006. Each has six top-100s except the Rockies, who have eight. (And this doesn’t include MLB’s current tank commanders, the Astros and Cubs, who have four and five top-100s, respectively, and have assembled playoff caliber rosters while each having seven losing seasons in the last 10.)

Most of the teams on the above list will be good again in four or five years. The Twins are already on the upswing, and the Phillies’ brilliant Cole Hamels trade may help to shorten their rebuilding project. But all of these teams have been varying degrees of bad for some time, while Dodger fans have not had to endure anywhere near that kind of punishment. How did the Dodgers do it?

In one sense, they simply used ordinary items found around the typical baseball household: They made their first-round picks count (Seager, 18th overall, and Holmes (22nd)). They effectively scouted internationally (they signed Urias out of the Mexican League when he was just 16, for a relative pittance). They effectively scouted other teams (Montas and Johnson came from the White Sox thin, and now thinner, farm system). They at least arguably scouted their own roster effectively (coughing up Jose Peraza to get Montas and Johnson — a fascinating trade in which the future of all three players is the subject of considerable debate; the Dodgers also surrendered Scott Schebler, a player less subject to debate, or indeed to discussion of any kind). And the Dodgers developed their own talent well: Seager, DeLeon, and Barnes (a 9th rounder acquired from the Marlins for Dee Gordon) would all go higher in a hindsight draft, as perhaps would Holmes. The players deserve most of the credit for their own development, but the team deserves at least some.

Nothing about baseball is inevitable, least of all the prior or continuing development of the Dodgers top prospects. Three years ago no Dodgers official would have been able to name these players and say “we know we’re building around these guys.” Three of the key players weren’t even with the organization at the time.

But the Dodgers may have established an organization that enables them to, perhaps even makes it likely that they will, produce a handful of front-line, cost-controlled players every four or five years, whether the major-league team rains or shines. That hypothetical Dodger official of three years ago could have said “we’re pretty sure we’ll be building around maybe four or five guys from our organization, we just don’t know exactly which handful yet. Once we know that, we’ll fill in the details.” That exciting moment is now fast approaching. The Dodgers of 2019 will likely be a hell of a lot of fun to watch, and the three years in between may not be too bad either, if the rickety overpaid veterans don’t break down too quickly.

And my guess is the Dodgers’ secret sauce isn’t brilliant scouting or next generation analytics or superb coaching or excellent training regimens. Well, it may be all of those things, but the only thing that can get all of those things is money.

Money doesn’t help a farm system, at least not like it used to. With the restrictions on draft and international spending, it’s not like the Dodgers can bully the other 29 teams on an amateur level.

Thus Grant Brisbee in 2014, discussing Andrew Friedman’s arrival in LA. The article is still interesting and worth clicking through, but I disagree to some extent with Brisbee’s assertion quoted above. It’s true that restrictions on player salaries and international signings are limiting the ability to buy players. But there is no restriction on the amount that can be spent on locating, evaluating, and developing players. At least some of the rivers of cash that are no longer flowing into players pockets are flowing to these activities instead.

It’s possible that in their obsession to rein in Scott Boras, Jerry Reinsdorf and his allies created a far more dangerous enemy: zombie armies of  number-crunchers, scouts, coaches, and trainers, all laboring, knowingly or not, to produce those next five top-100 prospects. Boras used to facilitate inefficient spending by large-market teams, enhancing smaller-market teams’ ability to compete in the non-payroll space. Those days are waning, Chris Davis notwithstanding.

Maybe the Dodgers have just gotten lucky. Four of their top five prospects are pitchers; the Dodgers are just a couple of snapped UCLs or torn labrums away from disaster. (The recent signings of Scott Kazmir and Kenta Maeda are in part insurance against fire, theft, or loss of the four young arms mentioned above.) Joc Pederson may devolve into a second-division starter. Yasiel Puig may become a reality TV star. Bad things happen even to the best run organizations.

So these Dodgers may signify nothing. But the Dodgers’ owners disagree; that’s why they hired Andrew Friedman. The Dodgers aren’t going to have a $200-million payroll after 2018, maybe never again, and they wanted somebody who understood how to run a team that, like an iceberg, has the vast majority of its substance lying below the surface. Friedman wrested production from the infertile Tampa soil of with the obsessive frugality of a medieval Russian peasant. He’ll apply those same skills in LA, but mostly in ways that won’t be immediately visible on the field, using money that won’t be visible on the payroll.

This is Borg baseball. It could be baseball’s future. And for lower-revenue teams, resistance may well be futile.

Dynasty leagues test the deepest mettle of a fantasy baseball owner. Most good dynasty leagues have a lot of strong owners who have a pretty good view of ballplayers. The key to being a successful dynasty owner is to find players who are undervalued by others. If you can add players to your roster at a relatively cheap cost who have positive net returns for you, you are ahead of everyone else who was unwilling to roster that player.

The American League Central has many up-and-coming, talented players with names like Lindor, Buxton, Kepler, Rodon, Sano, and more. However, one player whose stardust has worn off is Oswaldo Arcia. This stocky Twins outfielder is still only 24 years of age, but with lots of youth coming up in Minnesota he has been surpassed in GM Terry Ryan’s eyes. This does not mean that you, as a dynasty league GM, should be overlooking him. Arcia’s calling card has always been his power, coming from a smooth lefty swing and a strong lower half. 2013 saw him hit 14 homers in a debut effort with the Twins, and 2014 saw 20 more homers at the big-league level. It was not to be in 2015, however, as he struggled mightily with injuries and strikeouts. Strikeouts will be an issue for most power hitters, and Arcia is no exception. However, it is tantalizing power that should draw you as a dynasty owner in. There are two scenarios here: One, Arcia is owned by an owner disgusted by his recent performance and selling low, or two, he is available on the waiver wire. Either way, he is a guy to go get right away, and with a further look, it should be obvious why you need to go out and acquire him.

The Twins have a crowded outfield; they had a crowded outfield last year, and it is not getting any better with Max Kepler coming into the picture. DH is going to be held down with some combination of Miguel Sano and Joe Mauer, and Arcia is going to have to have a monster spring to find playing time. Weird, I’m telling you to go get him, yet I’m telling you that he won’t play?? Think about it: This is an opportunity to buy dirt-cheap low on a player. There were reports out of Rochester, the Twins’ AAA affiliate, of him hitting 450 foot homers. I saw one of them myself. The talent is certainly there.

At this point, I should warn that some wonder about an attitude problem. This can be chalked up to early big-league success followed by struggles. All this kid needs is a change of scenery. He plays an only slightly below-average left field, although he is more comfortable in right, and has an accurate throwing arm from the outfield. His defense isn’t bad enough to keep him out of lineups, and even if it becomes so, he can still DH. A trade to any other team in the American League would give this powerful 24-year-old a chance at reaching his potential. He has been in the Twins organization since he was 16. He was a top-100 prospect prior to 2013 according to BA, BP, and MLB.com. He has been around so long that his younger brother Orlando, a shortstop prospect for the Brewers, has taken the entire spotlight. Don’t let the younger bro overshadow the older — Oswaldo is a power bat who can hit 30 homers in a season given 145 games. He will have to sit against the toughest lefties, the Chris Sales of the world, but what lefty finds guys like that easy? It is a tremendous buy-low opportunity for any dynasty team looking for upside; it is not often you find a guy with 70+ raw power that has shown it in games just lying around on the cheap. Go get him now, and you won’t regret it!

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. Consequently, Giancarlo Stanton is listed on the Marlins roster for the duration of his career while the Mets declare Ken Singleton and the Expos / Nationals claim Tim Raines. 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 1939 New York Yankees          OWAR: 60.8     OWS: 345     OPW%: .607

Based on the revised standings the “Original” 1939 Yankees registered 94 victories and outlasted the Indians to secure the pennant by a 7-game margin. New York paced the American League in OWS and OWAR. GM Ed Barrow acquired all of the ballplayers on the 1939 Yankees roster.

“Joltin’” Joe DiMaggio claimed his first batting title and the 1939 American League MVP Award. “The Yankee Clipper” produced a .381 BA with 30 four-baggers, 126 ribbies and 108 runs scored. Red Rolfe (.329/14/80) topped the leader boards with 213 safeties, 139 aces and 46 two-base knocks. Fellow third-sacker Billy Werber registered 115 runs scored and drilled 35 doubles. Bill Dickey belted 24 round-trippers and tallied 105 RBI along with a .302 BA. George “Twinkletoes” Selkirk (.306/21/101) posted career-bests in homers, runs scored (103) and bases on balls (103). Joe Gordon smashed 28 long balls and drove in 111 baserunners during his sophomore season. Charlie “King Kong” Keller supplied a .334 BA in his inaugural campaign.

Lou Gehrig is listed as the top ballplayer in the All-Time First Baseman rankings according to Bill James in “The New Bill James Historical Baseball Abstract.” Teammates listed in the “NBJHBA” top 100 rankings include DiMaggio (5^th-CF), Dickey (7^th-C), Gordon (16^th-2B), Keller (17^th-LF), Tony Lazzeri (19^th-2B), Dixie Walker (30^th-RF), Rolfe (44^th-3B), Ben Chapman (55^th-CF), Frankie Crosetti (67^th-SS), Lefty Gomez (67^th-P), Werber (78^th-3B) and Lyn Lary (80^th-SS).

Lefty Gomez (12-8, 3.41) earned his seventh All-Star nomination. Atley Donald furnished a 13-3 mark with a 3.71 ERA. Marius Russo contributed an 8-3 record with a 2.41 ERA and a 1.095 WHIP in his freshman year.

The “Original” 1939 New York Yankees roster

Honorable Mention

The “Original” 1932 Yankees            OWAR: 52.6     OWS: 336     OPW%: .588

The Philadelphia Athletics ended the season in a virtual tie with the Bronx Bombers. The A’s edged the Yankees by a few percentage points to take the pennant while New York led the Junior Circuit in OWAR and OWS. Lou Gehrig pummeled opposition hurlers, belting 42 doubles and 34 round-trippers. “The Iron Horse” registered 138 tallies, 208 base knocks and 151 ribbies along with a .349 BA. Lefty O’Doul (.368/21/90) collected his second batting title and topped the 200-hit mark for the third time in four campaigns. Tony “Poosh ‘Em Up” Lazzeri supplied a .300 BA with 15 dingers and 113 RBI. Earle Combs aka “The Kentucky Colonel” scored 143 runs and posted a .321 BA as the Yankees’ primary leadoff hitter. Ben Chapman rapped 41 doubles, swiped a League-leading 38 bases and topped the century mark in runs scored (101) and RBI (107).  Bill Dickey (.310/15/84) and Kiddo Davis (.309/5/57) bolstered the prolific lineup. Lefty Gomez (24-7, 4.21) anchored the starting rotation and finished fifth in the 1932 A.L. MVP balloting in spite of his high ERA and walk totals. Johnny Allen fashioned a 17-4 record with a 3.70 ERA in his rookie year.

On Deck

The “Original” 1906 Cubs

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

Last year, many Indians fans anguished over the so-called “SI Cover” curse. The prediction that the Cleveland Indians would win the 2015 World Series, however, was not their downfall. The downfall of the Indians once again was highlighted by mediocre offense, and the unfortunate decline of their biggest free-agent acquisitions in over a decade, Nick Swisher and Michael Bourn. Swisher and Bourn ate up over 1/4 of the Indians’ total payroll and their sedentary production was an absolute killer in their lineup. Things improved offensively with the promotion of Rookie of the Year runner-up and budding star shortstop Francisco Lindor. Lindor produced way above what was expected of him and figures to be a key piece of the puzzle in 2016. Also the trading of both Bourn and Swisher to the Atlanta Braves was the equivalent of a one-thousand pound anchor being lifted from the Tribe’s lineup. Ultimately, the Tribe finished with a respectable, yet disappointing (based upon previous predictions) 81-80 record. However, there are five key things that will be the difference-makers in 2016 and will lead the Tribe to winning their first AL Central title since 2007.

1. Corey Kluber, Carlos Carrasco and Danny Salazar – The top of the Tribe rotation is arguably the best in all of baseball. According to early projections, the trio all look to post ERAs under 3.35, have more than 200 strikeouts each, and have no more than 50 walks each. Respectively, they project to have WARs of 6.0, 5.8, and 3.5. No top of the rotation in all of baseball projects higher. Carrasco and Kluber each should easily contend for an AL Cy Young Award, and Salazar looks to break through in a big way as well.
2. Terry Francona – Since Francona took over the Tribe they haven’t had a losing season. He’s compiled an overall record of 258-229 since 2013 and, of course, had a very storied career in Boston, winning two World Series in his tenure there. Francona is arguably the best manager in baseball, and would love nothing more than to add another World Series title to his name. He’s worked particularly well with the Tribe’s young roster and was reported to be a key reason behind the front office not moving one of their top-of-the-rotation starters for a bat. With a strong rotation and bullpen to work with (the Tribe had the Majors’ 4th-lowest bullpen ERA at 3.12 last season), Francona’s biggest task lies with getting his lineup in a situation to produce as many runs as possible to support their outstanding pitching. Luckily for him they’re most likely not going to have to score that much.
3. The Middle Infielders – Both Francisco Lindor and Jason Kipnis are among the very best in the league at their positions. They hit 1 and 2 in the Tribe lineup, and they also will be quite possibly the biggest factors to helping the Indians win several games in 2016. Both infielders were top 2 in WAR at their position last season (Kipnis 5.2, 1st in MLB at 2B, Lindor 4.6, 2nd in MLB at SS). Both infielders also were 2nd at their position in both BABIP and wOBA (minimum 400 PA). On top of all this, both players are plus defenders, and remind some fans of the dynamic duo of Omar Vizquel and Carlos Baerga that dominated the middle of the field throughout the 90s for the Tribe.
4. A Healthy Yan Gomes – Okay, so Yan Gomes was pretty bad last year…his lack of production no doubt had a big effect on the Indians lineup, and not in a good way. Gomes had a miserable slash line of .231/.267/.391 and hit only 12 homers. In 2014 we all saw a very different Yan Gomes, as he had a respectable slash line of .278/.313/.472 along with 21 homers. He led all AL catchers in WAR (4.5) and Slugging percentage (.472) (minimum 400 PA). With Gomes returning to full health now in 2016 he should return to form and be a big producer in the middle of the Tribe lineup.
5. Michael Brantley – Losing Brantley for the first month of the season is really going to hamper the Tribe, but if he can return to full health, you’d be hard-pressed to find a more productive player in all of baseball. Brantley is the Tribe’s X-factor; over the last two seasons he has hit 35 homers and 90 doubles, he’s had batting averages of .327 and .310 respectively, and he’s had OPS’s of .890 and .859 respectively. Most impressively has been his ability to hit with runners in scoring position — over the last two seasons combined he’s owned a .351/.437/.507 slash line. When healthy, the hope is that he can return to this form once again. Brantley proved resilient last season, putting up big numbers despite dealing with back issues throughout his 2015 campaign.

So there it is, the keys to the Tribe winning a 2016 division title. Obviously on top of all this, several other things need to go right for the Tribe. But these five factors alone will be among the leading reasons why the Indians win their division.

All stats referenced, or used for statistical analysis for this article are courtesy of mlb.com, baseball-reference.com, and fangraphs.com.

What’s the single most important offensive statistic? I imagine most of us who have bookmarked FanGraphs would not say batting average or RBIs. A lot of us would name wOBA or wRC+. But neither of those are the types of things you can calculate in your head. If I go to a game, and a batter goes 1-for-4 with a double and a walk, I know that he batted .250 with a .400 on-base percentage and a .500 slugging percentage. I can do that in my head.

So of the easily calculated numbers — the ones you might see on a TV broadcast, or on your local Jumbotron — what’s the best? I’d guess that if you polled a bunch of knowledgeable fans, on-base percentage would get a plurality of the votes. There’d be some support for OPS too, I imagine, though OPS is on the brink of can’t-do-it-in-your-head. Slugging percentage would be in the mix, too. Batting average would be pretty far down the list.

I think there are two reasons for on-base percentage’s popularity. First, of course, is Moneyball. Michael Lewis demonstrated how there was a market inefficiency in valuing players with good on-base skills in 2002. The second reason is that it makes intuitive sense. You got on base, you mess with the pitcher’s windup and the fielders’ alignment, and good things can happen, scoring-wise.

To check, I looked at every team from 1914 through 2015 — the entire Retrosheet era, encompassing 2,198 team-seasons. I calculated the correlation coefficient between a team’s on-base percentage and its runs per game. And, it turns out, it’s pretty high — 0.890. That means, roughly, that you can explain nearly 80% of a team’s scoring by looking at its on-base percentage. Slugging percentage is close behind, at 0.867. Batting average, unsurprisingly, is worse (0.812), while OPS, also unsurprisingly, is better (0.944).

But that difference doesn’t mean that OBP>SLG is an iron rule. Take 2015, for example. The correlation coefficient between on-base percentage and runs per game for the 30 teams last year was just 0.644, compared to 0.875 for slugging percentage. Slugging won in 2014 too, 0.857-0.797. And 2013, 0.896-0.894. And 2012, and 2011, and 2010, and 2009, and every single year starting in the Moneyball season of 2002. Slugging percentage, not on-base percentage, is on a 14-year run as the best predictor of offense.

And it turns out that the choice of endpoints matter. On-base percentage has a higher correlation coefficient to scoring than slugging percentage for the period 1914-2015. But slugging percentage explains scoring better in the period 1939-2015 and every subsequent span ending in the present. Slugging percentage, not on-base percentage, is most closely linked to run scoring in modern baseball.

Let me show that graphically. I calculated the correlation coefficient between slugging percentage and scoring, minus the correlation coefficient between on-base percentage and scoring. A positive number means that slugging percentage did a better job of explaining scoring, and a negative number means that on-base percentage did better. I looked at three-year periods (to smooth out the data) from 1914 to 2015, so on the graph below, the label 1916 represents the years 1914-1916.

A few obvious observations:

• The Deadball years were extreme outliers. There were dilution-of-talent issues through 1915, when the Federal League operated. World War I shortened the season in 1918 and 1919. And nobody hit home runs back then. The Giants led the majors with 39 home runs in 1917. Three Blue Jays matched or beat that number last year.
• Since World War II, slugging percentage has been, pretty clearly, the more important driver of offense. Beginning with 1946-1948, there have been 68 three-year spans, and in only 19 of them (28%) did on-base percentage do a better job of explaining run scoring than slugging percentage.
• The one notable exception: the years 1995-1997 through 2000-2002, during which on-base percentage ruled. Ol’ Billy Beane, he knew what he was doing. (You probably already knew that.)

This raises two obvious questions. The first one is: Why? The graph isn’t random; there are somewhat distinct periods during which either on-base percentage or slugging percentage is better correlated to scoring. What’s going on in those periods?

To try to answer that question, I ran another set of correlations, comparing the slugging percentage minus on-base percentage correlations to various per-game measures: runs, hits, home runs, doubles, triples, etc. Nothing really correlates all that well. I tossed out the four clear outliers on the left side of the graph (1914-16, 1915-17, 1916-18, 1917-19), and the best correlations I got were still less than 0.40. Here’s runs per game, with a correlation coefficient of -0.35. The negative number means that the more runs scored per game, the more on-base percentage, rather than slugging percentage, correlates to scoring.

That makes intuitive sense, in a way. When there are a lot runs being scored — the 1930s, the Steroid Era — all you need to do is get guys on base, because the batters behind them stand a good chance of driving them in. When runs are harder to come by — Deadball II, or the current game — it’s harder to bring around a runner to score without the longball. Again, this isn’t a really strong relationship, but you can kind of see it.

The second question is, what does this mean? Well, I suppose we shouldn’t look at on-base percentage in a vacuum, because OBP alone isn’t the best descriptor of scoring. A player with good on-base skills but limited power works at the top or bottom of a lineup, but if you want to score runs in today’s game, you need guys who can slug.

Taking that a step further, if Beane exploited a market inefficiency in on-base percentage at the beginning of the century, might there be a market inefficiency in slugging percentage today? It doesn’t seem that way. First, there’s obviously an overlap between slugging percentage and on-base percentage (i.e., hits), and just hitting the ball hard on contact doesn’t fill the bill if you don’t make enough contact. Recall the correlation coefficient between run-scoring and on-base percentage is 0.89 and between runs and slugging is 0.87. The correlation between run-scoring and pure power, as measured by isolated slugging, is just 0.66. That’s considerably lower than batting average (0.81). ISO alone doesn’t drive scoring.

The second reason there probably isn’t a market inefficiency in slugging percentage is that inefficiencies, by definition, assume that the market as a whole is missing something. In the Moneyball example, other clubs didn’t see the value in Scott Hatteberg and his ilk. It’s harder to believe, fifteen years later, with teams employing directors of baseball systems development and posting for quantitative analysts, that all 30 teams are missing the boat on players who slug but don’t contribute a lot otherwise. Or, put another way, there’s a reason Pedro Alvarez and Chris Carter were non-tendered, and it’s not market inefficiency.

Justin Upton’s recent $132.75M/6-year contract with the Tigers does not seem, on the surface, like an outrageous contract. And right now it isn’t; at age 28, Justin should be hitting his prime. Since breaking in with the Diamondbacks, he has been a consistent power threat in a league where consistent power bats are few and far between. To pay $22 million for an outfielder that the Tigers control for two years, potentially six years (Upton has an opt-out clause after two seasons), does not sound extreme when you consider other contracts signed by young, dynamic outfielders; in fact the contract came in below MLB Trade Rumors’ projection of a 7-year/$147 million deal[1]. So why anyone would be concerned about Justin Upton’s deal? Maybe it’s the fact that it took a while for his market to develop this offseason, or maybe it is because he shares the same bloodline as Melvin (formerly known as B.J.) Upton whose production went in the tank after his age-28 season? I get the feeling that Justin could end up as a bad investment for the Tigers. Here’s why.

Exit Speed and Park Factors

Fortunately for Justin, he is getting out of the notorious pitcher’s kingdom that is Petco Park. Unfortunately for Justin, he is moving to another pitcher’s park, Comerica Park. Poor guy can’t catch a break. One concern that I noticed about Upton’s metrics was his exit speed on home runs. According to the ESPN Home Run Tracker, Upton had an average home-run exit speed of 105.2 mph. The concern here lies when you compare the average exit speed versus his prior years. Take a look at the chart below which compares his FB/HR%, HR totals, and average home-run exit speed.

The numbers here do not look all that out of line, other than his 2012 season where his HR/FB% was off from the average. Upton usually sits around the high 20’s in terms of total home runs, being pretty consistent except for the outlier 2012 season. But the home-run exit speeds have decreased each of the last five seasons — some seasons the decrease was more than others, but still they have decreased nonetheless. Another aspect of Upton’s stats to look at is his 2015 home-run landing spots overlaid with an outline of Comerica’s dimensions.

The graphs show the “True” Landing spots according to the ESPN Home Run tracker for the 2015 season. Notice that roughly eight of Upton’s 2015 home runs would not have made it out of Comerica. Only one would have stayed inside Petco, Upton’s 2015 home field. If we used the Comerica park numbers, Upton would have hit 26-8, so 18 home runs. This creates a reason to be concerned, especially since most of Upton’s value is supplied by his ability to drive the ball out of the park, and not his ability to hit for average.

So a value trap you say?

Yes, a value trap. Considering that Upton is 28, paying $22 million a year seems pretty reasonable. In fact, some baseball commentators saw it as a solid investment (and it may turn out to be such). But the caveat is Upton’s opt–out option after two years, similar to the deal Jason Heyward has. If Upton is able to continue to produce nearly 30 home runs a year, he could easily opt out and test the free-agent market again. But if an underlying metric like home-run exit speeds continues to dip and the power numbers take off downhill with it, there is no rational reason for him to opt out and test the market again when he has a $22 million/year deal locked up for four more years.

Therein lies the trap: In an effort to win now by the Tigers, they will either lose Upton after two seasons or they will get trapped by a contract that could eat $22 million of payroll a year, for four years, for a player whose power numbers have dropped and will struggle to provide value in other areas. Is it a great deal for Upton? Of course. Is it good for the Tigers? Short-term, yes. Long-term, there are very few scenarios where they emerge as a winner in the deal. Either they have to pay for Upton again after the 2017 season, or they get stuck with a player who isn’t as good as he once was. Maybe it’s just a hunch but I think the Tigers may be getting the shaft.

[1] www.mlbtraderumors.com/2015/10/justin-upton-mlb-free-agent.html