Archive for matt moore

James Paxton Is Not the “Next Sonny Gray”

The Yankees kicked off their offseason by acquiring LHP James Paxton from the Seattle Mariners to bolster their starting rotation. You won’t find anybody willing to deny Paxton’s immense talent, but it’s natural for people to scrutinize big acquisitions, especially when the big acquisition is on his way to New York. This scrutiny is best exemplified by a conversation I had with my mother on the day the trade was made. My mom followed the Mets of the mid-to-late 1980s when she lived in Brooklyn, went years without watching baseball, and has watched the Yankees for the past decade by product of my fandom. This leads to the amusing circumstance that she is very familiar with current broadcasters Keith Hernandez and David Cone, all of the recent Yankees players, and almost nobody in between. Our conversation on the day of the Paxton trade went something like this:

Me: The Yankees picked up a hell of a pitcher named James Paxton. I think he’s going to do big things for the Yankees next year!

Mom: Yeah, sure. Isn’t that what you said about Sonny Gray?

Okay — she got me there.

Read the rest of this entry »


Introducing XRA: The New Results-Independent Pitching Stat

There are a multitude of ways that we can judge pitchers. Most people look at earned run average to gauge whether a pitcher has been successful, while many old school announcers will still cite a pitcher’s win-loss record. ERA is a nice, easy way of looking at how a pitcher has performed at limiting runs, but it doesn’t come close to telling the whole story. In the early 2000s, Voros McCracken created the idea of Defense Independent Pitching Stats or DIPS, which credited the pitcher only with what he could actually control. Fielding Independent Pitching was born from this theory and only took into account a pitcher’s strikeouts, walks and home runs allowed. It turns out that a pitcher’s home run rate is not terribly consistent, thus xFIP was created by Dave Studeman to normalize the home run aspect of the FIP equation by using the league home run per fly ball rate and the pitcher’s fly ball rate.

In 2015, a new metric was developed by Jonathan Judge, Harry Pavlidis and Dan Turkenkopf called Deserved Run Average or DRA. This new stat attempts to take into account every aspect that the pitcher has control over and control for everything that he does not, thus crediting the pitcher only for the runs that he actually deserves. DRA, however, is still dependent on the result of each batted ball. If the batter hits a ball deep in the gap and it rolls to the wall, the pitcher is charged with a double, but if the center fielder lays out and makes a remarkable catch, the pitcher is credited with an out. When evaluating pitchers, why should it matter whether they have a Gold Glove caliber defender behind them or not? It shouldn’t, and that’s where Expected Run Average comes in.

Expected Run Average or XRA gives pitchers credit for what they actually can control. FIP attempts to do this as well but assumes that pitchers have no control over batted balls. While the pitcher does not control how the fielders interact with the live ball, he does have an impact on the type of contact that he allows. XRA is based on a modified DIPS theory that the pitcher controls three things: whether he strikes the batter out, whether he walks the batter and the exit velocity, launch angle combination off the bat. After the ball leaves the batter’s bat, the play is out of the pitcher’s hands and should no longer have any effect on his statistics. The goal is to figure out a way to measure, independently of the defense and park, how each pitcher performs on balls in play. Since 2015, StatCast has tracked the exit velocity and launch angle of every batted ball in the majors. Each batted ball has a hit probability based on the velocity off of the bat and its trajectory. The probability for extra bases can also be determined. These batted ball probabilities have been linearly weighted for each event including strikeouts and walks to give each player’s xwOBA, which can be found on Baseball Savant. This is the perfect way to look specifically at how well a pitcher has performed on a per plate appearance basis.

Once xwOBA is found, then XRA can be calculated. The first objective is to find the pitcher’s weighted runs below average. To do this, I used the weighted runs above average formula from FanGraphs except I made it negative since fewer runs are better for pitchers.

wRBA = – ((xwOBA – League wOBA) / wOBA Scale) * TBF

For example, Max Scherzer has had a .228 xwOBA so far this season and has faced 487 batters. After finding the league wOBA and wOBA scale numbers at FanGraphs I can plug these numbers into the formula.

– ((.228 – .321) / 1.185) * 487 = 38.22

Max Scherzer has been 38.22 runs better than average so far this season, but now I need to figure out what the average pitcher would do while facing the same number of batters. To find this I need the league runs per plate appearance rate and multiply that number by the number of batters that Scherzer has faced.

League R/PA * TBF = Average Pitcher Runs
.122 * 487 = 59.41

So a league average pitcher would have been expected to surrender 59.41 runs facing the number of batters that Scherzer has so far this season. Now that we know how the average pitcher should have performed we can find the expected number of runs that Scherzer should have surrendered so far this season by subtracting his wRBA of 38.22 from the average pitcher’s runs.

Average Pitcher Runs – Weighted Runs Below Average = Expected Runs
59.41 – 38.22 = 21.19

Based on Scherzer’s xwOBA, he should have only given up 21.19 to this point in the season. If this sounds incredible it’s because this is the lowest mark of any starting pitcher though the first half of the season. Finally, XRA is found by using the RA/9 formula by multiplying the expected number of runs allowed by 9 and then dividing by innings pitched.

(9 * Expected Runs) / Innings Pitched = XRA
(9 * 21.19) / 128.33 = 1.49

Max Scherzer’s XRA of 1.49 is easily the lowest of any starter through the first half. The second best starter has been Chris Sale who has a 2.15 XRA. Of course these names are not surprising as they each started the All Star Game and are both currently the front runners for their leagues’ respective cy young award.

Here is a list of the top ten qualified pitchers:

Pitcher XRA
Max Scherzer 1.49
Chris Sale 2.15
Zack Greinke 2.26
Corey Kluber 2.33
Clayton Kershaw 2.34
Dan Straily 2.87
Lance McCullers 2.89
Chase Anderson 3.11
Luis Severino 3.17
Jeff Samardzija 3.23

And the bottom ten:

Pitcher XRA
Matt Moore 6.58
Kevin Gausman 6.47
Derek Holland 6.32
Matt Cain 6.26
Ricky Nolasco 6.26
Wade Miley 6.17
Johnny Cueto 6.10
Martin Perez 5.97
Jason Hammel 5.95
Jesse Chavez 5.84

Full First Half XRA List

It is interesting to see that three members of the Giants rotation rank in the bottom seven in all of baseball. In fact, AT&T Park is such a pitcher-friendly park that once you park adjust these numbers, Moore, Cain and Cueto become the three worst pitchers in baseball. It’s not surprising then why the Giants are having such a disappointing season.

One measure of a good stat is whether or not it matches your perception. Therefore, while it is interesting to see Dan Straily as one of the best pitchers in baseball and Johnny Cueto as one of the worst, it is much more assuring to see Max Scherzer, Chris Sale and Clayton Kershaw as some of the very best in the sport. The numbers for relievers also reveal how dominant Kenley Jansen and Craig Kimbrel have been. This is all good evidence that XRA is doing what it is supposed to do, accurately displaying how good pitchers have actually been, independent of all other factors.

Another important characteristic of a good stat is how well it correlates from year to year. While ERA is the most simple and popular way to look at pitchers, it is not very consistent. XRA is much more consistent than ERA and FIP and also compares favorably with xFIP. However, it is not as consistent as DRA. DRA controls for so many aspects of the game that it should be expected to be the most consistent. However, being the most predictive or most consistent stat is not necessarily the goal of XRA. The real goal is to show how well the pitcher actually did, and XRA seems to do this remarkably. While not being as consistent as a stat like DRA, the level of consistency is extremely encouraging and puts it right in line with the other run estimators.

XRA is a stat that takes luck, defense, and ballpark dimensions out of the equation. When evaluating a pitcher, he shouldn’t be penalized for giving up a 350-foot pop fly for a home run in Cincinnati while being rewarded for that same pop fly being caught for an easy out in Miami. With XRA, no longer will people have to quibble about BABIP, since it is results-independent and removes all luck from consideration. A ground ball with eyes will now be treated the same whether it squirts through for a single or is tracked down for an out. Pitching ability will no longer need to be measured with an eye on the level of the defense. It takes a good offense, a good pitching staff and a good defense to make a great team, and with XRA we can finally separate all of these important factions.


Performance After Tommy John Surgery

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

Name Season GS IP K/9 ERA FIP xFIP
Matt Harvey 2013 26 178.1 9.64 2.27 2.00 2.63
2015 24 160.0 8.38 2.48 3.34 3.38
Matt Moore 2013 27 150.1 8.56 3.29 3.95 4.32
2014 2 10.0 5.40 2.70 4.73 4.54
2015 6 26.2 5.74 8.78 5.61 5.77
Jose Fernandez 2013 28 172.2 9.75 2.19 2.73 3.08
2014 8 51.2 12.19 2.44 2.18 2.18
2015 7 43.0 11.09 2.30 1.74 2.48
Patrick Corbin 2013  32 208.1 7.69 3.41 3.43 3.48
2015  11 56.1 6.06 3.67 4.02 3.18

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

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

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

Pitch Speeds By Year (MPH)
Matt Moore Patrick Corbin Jose Fernandez Matt Harvey
FF SL CH FF SL CH FF CU CH FF SL CH CU
2011 95.2 82.7 85.8
2012  94.2  82.1 85.8 90.7 78.8 80.2
2013  92.4 81.1  84.5  91.8  80.0  81.0  94.7 80.9 86.3  95.0 89.0 86.7 82.3
2014 91.3  79.7  84.2 94.9 82.3  87.7
2015  91.0  79.0 83.3  92.4  81.2  82.2 95.8 83.2 88.5 95.9 89.3 87.9  83.2
FF = 4-Seam Fastball, SL = Slider, CH = Change-up, CU = Curveball

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

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

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

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

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

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

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


Your One-Stop Shop for Postseason Narrative Debunking

I, like you, have been hearing and reading a lot about the postseason and which teams are best positioned to go deep into October. The rationales aren’t always based on more tangible factors — like, say, which teams are good — but rather on “hidden” or “insider” clues (use of scare quotes completely intentional) drawn from other qualities. I decided to test each of the factors I’ve heard or read about.

Full disclosure: This isn’t exactly original research. Well, it is, in that I made a list of various hypotheses to test, decided how to test them, and then spent hours pulling down data from Baseball-Reference and FanGraphs in order to create an unwieldy 275×23 spreadsheet full of logical operators. But it isn’t original in that some of the questions I’m addressing have been addressed elsewhere. For example, I’m going to consider whether postseason experience matters. David Gassko at the Hardball Times addressed the impact of players’ postseason experience on postseason outcomes in 2008, and Russell Carleton at Baseball Prospectus provided an analysis recently. I’m not claiming to be the first person to have thought of these items or of putting them to the test. What I’ve got here, though, is an attempt to combine a lot of narratives in one place, and to bring the research up to date through the 2013 postseason.

I’m going to look at seven questions:

  • Does prior postseason experience matter?
  • Do veteran players have an edge?
  • How important is momentum leading into the postseason?
  • Does good pitching stop good hitting?
  • Are teams reliant on home runs at a disadvantage?
  • Is having one or more ace starters an advantage?

For each question, I’ll present my research methodology and my results. Then, once I’ve presented all the conclusions, I’ll follow it up with a deeper discussion of my research methodology for those of you who care. (I imagine a lot of you do. This is, after all, FanGraphs.) In all cases, I’ve looked at every postseason series since the advent of the current Divisional Series-League Championship Series-World Series format in 1995. (I’m ignoring the wild card play-in/coin flip game.) That’s 19 years, seven series per year (four DS, 2 LCS, 1 WS), 133 series in total.

DOES POSTSEASON EXPERIENCE MATTER?

The Narrative: Teams that have been through the crucible of baseball’s postseason know what to expect and are better equipped to handle the pressures–national TV every game, zillions of reporters in the clubhouse, distant relations asking for tickets–than teams that haven’t been there before.

The Methodology: For each team, I checked the number of postseason series they played over the prior three years. The team with the most series was deemed the most experienced. If there was a tie, no team was more experienced. I also excluded series in which the more experienced team had just one postseason series under its belt, i.e., a Divisional Series elimination. I figured a team had to do more than one one-and-done in the past three years to qualify as experienced. In last year’s NLCS, for example, the Cardinals had played in five series over the past three years (three in 2011, two in 2012), while the Dodgers had played in none. So St. Louis got the nod. In the Dodgers’ prior Divisional Series, LA played an Atlanta team that lost a Divisional Series in 2010, its only postseason appearance in the prior three years, so neither team got credit for experience.

The Result: Narrative debunked. There have been 101 series in which one team was more experienced than the other, per my definition. The more experienced team won 50 of those series, or 49.5%. There is, at least since 1995, no relationship between postseason experience and success in the postseason.

DOES VETERAN PLAYERS HAVE AN EDGE?

The Narrative: The pressure on players grows exponentially in October. A veteran presence helps keep the clubhouse relaxed and helps players perform up to their capabilities, yet stay within themselves. Teams lacking that presence can play tight, trying to throw every pitch past the opposing batters and trying to hit a three-run homer with the bases empty on every at bat. (Sorry, I know, I’m laying it on thick, but that’s what you hear.)

The Methodology: For each team, I took the average of the batters’ weighted (by at bats + gamed played) age and the pitchers’ weighted (by 3 x games started + games + saves) age. I considered one team older than the other if its average age was 1.5 years older than that of its opponent. For example, in the 2012 ALCS, the Yankees’s average age was 31.5, and the Tigers’ was 28.1, so the Yankees had a veteran edge. When the Tigers advanced to the World Series against San Francisco, the Giants’ average age was 28.9, so neither team had an advantage.

The Result: Narrative in doubt. There have been 51 series in which one team’s average age was 1.5 or more years greater than the other. The older team won 27 of those series, or 53%. That’s not enough to make a definite call. And if you take away just one year–2009, when the aging Yankees took their most recent World Series–the percentage drops to 50%–no impact at all.

HOW IMPORTANT IS MOMENTUM LEADING INTO THE POSTSEASON?

The Narrative: Teams that end the year on a hot streak can carry that momentum right into the postseason. By contrast, a team that plays mediocre ball leading up to October develops bad habits, or forgets how to win, or something. (Sorry, but I have a really hard time with this one. We’re hearing it a lot this year–think of the hot Pirates or the cold A’s–but there are other teams, like the Orioles, who have the luxury of resting their players and lining up their starting rotation. I have a hard time believing that the O’s 3-3 record since Sept. 17 means anything.)

The Methodology: I looked up each team’s won-lost percentage over the last 30 days of the season and deemed a team as having more momentum if its winning percentage was 100 or more percentage points higher than that of its opponent. For example, in one of last year’s ALDS, the A’s were 19-8 (.704 winning percentage) over their last 30 days and the Tigers were 13-13 (.500), so the A’s had momentum. The Red Sox entered the other series on a 16-9 run (.640) and the Rays were 17-12 (.586), so neither team had an edge.

The Result: Narrative in doubt, and then, only for the Divisional Series. There have been 64 series in which one team’s winning percentage over its past 30 days was 100 percentage points higher than that of its opponent. In those series, the team with the better record won 33, or 51.5% of the time. That’s not much of an edge. And when you consider that a lot of those were in the Divisional Series, where the rules are slanted in favor of the better team (the team with the better record generally gets home field advantage), it goes away completely. Looking just at the ALCS, NLCS, and World Series, the team with the better record over the last 30 days of the season won 13 of 27 series, or 48%, debunking the narrative. In the Divisional Series, the hotter team over the last 30 days won 20 of 37 series, or 53%. That’s an edge, but not much of one.

DOES GOOD PITCHING STOP GOOD HITTING?

The Narrative: Pitching and defense win in October. Teams that hit a lot get shut down in the postseason.

The Methodology: I struggled with a methodology for this one. I came up with this: When a team whose hitting (measured by park-adjusted OPS) was 5% better than average faced a team whose pitching (by park-adjusted ERA) was 5% better than average, I deemed it as a good-hitting team meeting a good-pitching team. For example, the 2012 ALCS featured a good-hitting Yankees team (112 OPS+) against a good-pitching Tigers team (113 ERA+). The Yankees were also good-pitching (110 ERA+), but the Tigers weren’t good-hitting (103 OPS+).

The Result: Narrative in doubt. There have been 65 series in which a good-hitting team faced a good-pitching team, as defined above. (There were four in which both teams qualified as good-hitting and good-pitching; in those cases, I went with the better-hitting team for compiling my results.) In those series, the better-hitting team won 32 times, or 49%. That is, good hitting beat good pitching about half the time. That pretty much says it.

ARE TEAMS RELIANT AT HOME RUNS AT A DISADVANTAGE?

The Narrative: Teams that sit back and wait for home runs are at a disadvantage in the postseason, when better pitching makes run manufacture more important. Scrappy teams advance, sluggers go home.

The Methodology: I calculated each team’s percentage of runs derived from home runs. In every series, if one team derived 5% more of its runs from homers than another, I deemed that team as reliant on home runs. For example, in last year’s NLCS, the Cardinals scored 204 of their 783 runs on homers (26%). The Dodgers scored 207 of their 649 via the long ball (32%). So the Dodgers were more reliant on home runs. In the ALCS, the Red Sox scored 36% of their runs (305/853) on homers compared to 38% for the Tigers (301/796), so neither team had an edge.

The Result: Narrative in doubt. There have been 60 series in which one team derived a 5% or greater proportion of its runs from homers than its opponent. In those series, the more homer-happy team won 27 series, or 45% of the time. So the less homer-reliant team won 55%, which is OK, but certainly not a strong majority. And if you remove just one year–2012, when the less homer-reliant team won six series (three of those victories were by the Giants)–the percentage drops to 50%.

IS HAVING ONE OR MORE ACE STARTERS AN ADVANTAGE?

The Narrative: An ace starter can get two starts in a postseason series (three if he goes on short rest in the seventh game of a Championship or World Series.) Assuming he wins, that means his team needs win only one of three remaining games in a Divisional Series and only two of five or one of four in a Championship or World Series. A team lacking such a lights-out starter is at a disadvantage.

The Methodology: This is another one I struggled with. Defining an “ace” isn’t easy. I arrived at this: I totaled the Cy Young Award points for each team’s starters. If one team’s total exceeded the other’s by 60 or more points — the difference between the total number of first- and second-place votes since 2010 — I determined that team had an edge in aces. (The difference was half that prior to 2010, because the voting system changed in 2010, when the voting went from three deep to five deep and the difference between a first and second place vote rose from one point to two.) For example, in last year’s Boston-Tampa Bay Divisional Series, the only starter to receive Cy Young consideration was Tampa Bay’s Matt Moore, who got four points for two fourth-place votes. That’s not enough to give the Rays an edge. But in the other series, Tigers Max Scherzer (203 points) and Anibal Sanchez (46) combined for 249 points, while the A’s got 25 points for Bartolo Colon. That gives an edge to the Tigers.

The Result: Narrative in doubt. There have been 82 series in which one team’s starters got significantly more Cy Young Award vote points than its opponents’. The team with higher total won 44 series, or just under 54%. That’s not much better than a coin flip. And again, one year — in this case, 2001, when the team with the significantly higher Cy Young tally won six series — tipped the balance. Without the contributions of Randy Johnson, Curt Schilling, Roger Clemens, Freddy Garcia, Jamie Moyer, and Mike Mussina to that year’s postseason, the team with the apparent aces has won just 38 of 76 series, exactly half.

Conclusion: None of the narratives I examined stand up to scrutiny. Maybe the team that wins in the postseason, you know, just plays better.

 

Now, About the Methodology: I know there are limitations and valid criticisms of how I analyzed these data. Let me explain myself.

For postseason experience, I feel pretty good about counting the number of series each team played over the prior three years. One could argue that I should’ve looked at the postseason experience of the players rather than the franchise, but I’ll defend my method. There isn’t so much roster and coaching staff turnover from year to year to render franchise comparisons meaningless.

For defining veteran players, there are two issues. First, my choice of an age difference of 1.5 years is admittedly arbitrary. My thinking was pretty simple: one year doesn’t amount to much, and there were only 35 series in which the age difference was greater than two years. So 1.5 was a good compromise. Second, I know, age isn’t the same as years of experience. But it’s an OK proxy, it’s readily available, and it’s the kind of thing that the narrative’s built on. Bryce Harper has more plate appearances than J.D. Martinez, but he’s also over five years younger–whom do you think the announcers will describe as the veteran?

For momentum, I think the 30-day split’s appropriate. I could’ve chosen 14 days instead of 30 — FanGraphs’ splits on its Leaders boards totally rock — but I thought that’d include too many less meaningful late-season games when teams, as I mentioned, might be resting players and setting up their rotations. As for the difference of 100 points for winning percentage, that’s also a case of an admittedly arbitrary number that yields a reasonable sample size. A difference of 150 points, for example, would yield similar results but a sample size of only 39 compared to the 64 I got with 100 points.

For good hitting and good pitching, I realize that there are better measures of “good” than OPS+ and ERA+: wRC+ and FIP-, of course, among others. But I wanted to pick statistics that were consistent with the narrative. When a sportswriter or TV announcer says “good pitching beats good hitting,” I’ll bet you that at least 99 times out of a hundred that isn’t shorthand for “low FIP- beats high wRC+.” If you and I were asked to test whether good pitching beats good hitting, that’s probably how we’d do it. But that’s not what we’re looking at here OPS and ERA are more consistent with the narrative.

For reliance on home runs, it seems pretty clear to me that the right measure is percentage of runs scored via the long ball. Again, my choice of a difference of five percentage points is arbitrary, but it’s a nice round number that yields a reasonable sample size.

Finally, my use of Cy Young voting to determine a team’s ace or aces: Go ahead, open fire. I didn’t like it, either. But once again, we’re looking at a narrative, which may not be the objective truth. Look, Roger Clemens won the AL Cy Young Award in 2001 because he went 20-3. He was fourth in the league in WAR. He was ninth in ERA. He was third in FIP. He was, pretty clearly to me, not only not the best pitcher in the league, but also only the third best pitcher on his own team (I’d take Mussina and Pettite first). But I’ll bet you that when the Yankees played the Mariners for the ALCS that year (too far ago for me to remember clearly), part of the storyline was how the Yankees got stretched to five games in the Divisional Series and therefore wouldn’t have their ace, Roger Clemens, available until the fourth game against the Mariners. Never mind that Pettite was the MVP of the ALCS. The ace narrative is based on who’s perceived as the ace, not who actually is. (And a technical note: Until the Astros moved from the NL to the AL, the difference between first- and second-place votes in the two leagues were different, since there were 28 voters in the AL and 32 in the NL. The results I listed aren’t affected by that small difference. I checked.)