One of the more subtle stories of the dawn of the statistical revolution in baseball was the case of Frank Thomas. Frank was a big slugger, but not exactly graceful in the field. Since he played in the American League for a long while, he often was slotted as the DH. On paper, this would make sense. A team could theoretically use his bat in the lineup while playing someone else in the field, to avoid his lack of fielding. Well, despite what current trends seem to say, baseball is not played on paper, and some factors that cannot exactly be explained can derail logical thinking, like the Thomas at DH theory.

What I mean by this is that for some reason, Thomas’ best seasons as a hitter came when he played in the field. In his two MVP seasons, he played only 17 games (about 5%) as the DH. In all five of his All-Star seasons, he was voted in as a first baseman. This idea is further explained in Tom Tango’s The Book. For some reason, he was just not as good as a hitter as a DH. Maybe he was not as engaged, maybe he was “cold,” or maybe it was some other weird reason. I tried to dive into the splits to see the exact numbers, but could not get the exact data. Yet, the idea still remains: hitters may perform differently when placed in different spots in the field, or when they are not in the field at all.

I decided to apply this logic to the Minnesota Twins’ All-Star slugger, Miguel Sano. Like Thomas, Sano hits the cover off the ball, but isn’t as skilled in the field. Some would think that this means the Twins should play him as the designated hitter. Again, from a superficial level, this would seem to make sense, as his defensive liabilities would not come into play.

I looked into his splits for the 2017 season. With about 250 plate appearances as a third baseman and about 60 as a designated hitter or a pinch-hitter, there were enough observations to perform a hypothesis test (specifically a Two Sample t-Test) on the subject.

My test criteria is as follows:

• Null Hypothesis: Batting Average/OBP/wOBA as 3B = BA/OBP/wOBA as PH/DH
• Alternative Hypothesis: Batting Average/OBP/wOBA as 3B ≠ BA/OBP/wOBA as PH/DH

My level of significance is 95% confidence, or a = 0.05.

For those who are not familiar with the two sample t-test for equal population means, or if you may have forgotten formulas for test statistics, degrees of freedom, point estimates, or anything else, you can find it all here.

Here are Sano’s splits. One can note the obvious difference in his batting statistics, but is this difference statistically significant?

Let’s dive in. Well, here is my test:

It appears that the difference in means for batting average and wOBA are significant, meaning we can reject the null hypothesis that Sano bats the same when in the field and when off of the field. OBP was not significant, so we fail to reject the null for that metric, but the p-value was still relatively weak.

From this analysis, one can see that Sano does in fact perform worse when he doesn’t play 3B. Perhaps the Twins have already keyed onto this, as Sano has four times as many ABs as a third baseman than as a designated or pinch-hitter. The Twins do have a few decent utility infielders, so maybe they are just squeezing in playing time for those guys when they move Sano to DH. But, in this case, the statistics don’t lie: please play Miguel Sano at third base.

Almost one year ago, I took my initial stab at sabermetrics writing about how the Twins’ fabled philosophy of “pitch to contact” was being stifled by the club’s own inability to field the ball. If you are putting that much faith in your defense, it would make sense that you would have the defensive ability to back up your philosophy. For a while, this was true for the Twins. I am not going to rehash what I already wrote in August of 2015, but if I haven’t summarized myself adequately enough yet, I’ll attempt to do so again: the Twins fostered a philosophy in pitch to contact that relied on their defense, yet from 2010-2015 their defense slowly deteriorated, as did their pitching and overall record. My thought was that if the Twins were able to improve on this sub-par defense, they would be able to bail out their pitching, rather than continue to hamper it. I relied a lot of the idea of fielding-independent pitching, so if you are unaware with that concept, read about it here.

Fast forward to 22 months later, and the Twins have some new captains running the ship. These guys value math, and have started to take a more analytical look at the Twins. The most noticeable difference so far in the Twins’ somewhat surprising season (although as of this posting the team has fallen back to earth somewhat) is their improved defense. To this date, the Twins have the fourth-best defense according to Defensive WAR. Last year, they were the second-worst defense. This idea has already been written about, showing that my prediction nearly two years ago was correct. The whole idea that, on average, a good defense can bail out pitching still holds, and I ran a regression to prove it. On average, a one-unit increase in your FIP-ERA difference increases your defensive rating by 49 points. This is quite the turnaround, showing how valuable a defense can be, and this number, in combination with batting and pitching WAR, can be quantified to show its overall impact on a club’s record. I’ll spare the calculation, but one can see how this improved defense has helped lead the Twins to their surprising start.

Unfortunately, the Twins’ pitching (besides two great starts from Ervin Santana and Jose Berrios) has been awful, so any defensive gains this season have been erased by having the second-worst ERA and FIP in baseball, despite the 13^th-best FIP-ERA metric. To this point in the season, the Twins have the same ERA as they did last year, but their FIP-ERA difference was a horrendous -0.52. They have a positive FIP-ERA difference this year at 0.12, showing that their pitching has actually gotten worse from last year to this current season. In some ways, their defense has kept the team above .500. Turns out my prediction was right: improve the defense, and the team will be noticeably better. If the Twins’ pitching would have stayed at the same point as last season, (4.57 FIP), in combination with their FIP-ERA metric, the Twins would be in the top-20 for pitching this season. Unfortunately, the regression of the pitching staff (independent of the defense) has kept the Twins from fully benefiting from their improved defense.

Before I wrap this up, a quick side-note on the Cubs this year. Last season, the Cubs had far and away the best defense in baseball, the best FIP-ERA in baseball, and the best ERA in baseball. This year, as any baseball fan would recognize, the Cubs have been struggling, especially with their pitching. Coincidentally, the Cubs’ pitching this year has dropped to 14^th by ERA, along with their defense, which is also ranked 14^th. Their FIP-ERA metric is at 13^th in baseball, so their regression in defense may be partly to blame for their pitching struggles.

To sum, from 2010-2015 the Twins’ defense deteriorated, leading their pitching staff to do the same based on their pitch-to-contact philosophy. I wrote a year ago that the Twins needed to improve their defense if they wanted to continue this philosophy. They improved their defense, which has fueled a surprising start for the club, and has kept the team from bottoming out with their horrendous pitching staff.

Appendix

Linear Regression and Plot

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.

One of the most common phrases in all of sports is “defense wins championships.” Defense isn’t flashy; it doesn’t put people in the seats (unless you’re a desperate Twins fan wanting to see Byron Buxton do more of this — or this). People like to see the home runs, the strikeouts. People also like to see the diving plays, but diving plays are a poor indicator of a team’s total defensive quality. So even the plays on defense that do put people in the seats aren’t indicative of a team’s overall level of defense. Other sports are the same way. People don’t realize the ins and outs of NBA defenses; they only see the steals and the lockdown plays — or lack thereof. NFL fans love to see big hits, but sometimes these big hits could be avoided if a team had defended the play better and stopped the ball carrier earlier.

Yes, it is true the nuances of defense can be monotonous, and this is true through all sports. Another factor about defense is the lack of a way to quantify defensive skill. Some metrics, like RPM (shameless plug to my boy Ricky Rubio, clearly a top-5 PG), try to do this for basketball. But in baseball, defense really is quantifiable, using different metrics that track can track how effective a defensive player or team is against league average. For example, read up on UZR, just one of the metrics that can put a number on a defense.

I came to this thinking on the undervaluation of defense through a different path. I had always wondered if an incredible defense could bail out an average pitching staff. I had always been interested in this facet; to reminisce, I once created an outfield of Torii Hunter, Rocco Baldelli, and Carl Crawford on MVP Baseball 2004. These were the best and fastest fielders in the game, and it seemed like they could get any fly ball. As much as I want to credit EA Sports for making an accurate game, I obviously cannot deduce the real-world effectiveness from a video game. Instead, I turned to the numbers.

To quantify how much a defense could “bail out” their pitching staff, I looked at the team’s average ERA compared to its average FIP. The difference between these numbers can somewhat quantify how much a team’s defense (and other factors) influence pitching from what we would expect it to be. For example, if a team had a FIP of 4.00, and an ERA of 3.50, this would indicate that a good defense was able to reach more balls than an average defense, meaning the team’s ERA should be lower, as there were more recorded outs than what we expect. The opposite, a team’s ERA being greater than its FIP, would indicate that a poor defense hurt their pitching staff’s performance, as they should have been able to get more balls that they did. To sum up, my hypothesis was that the teams with the largest FIP-ERA differences had great defenses, while teams that had the lowest FIP-ERA differences (negative values), had poor defenses. Now, I understand that many factors outside of defense can influence ERA, and that FIP does not perfectly match what a pitcher’s ERA would be with an average defense, but these anomalies will be canceled out in a large enough data set.

For the data, I measured playoff-contending teams (at least 85 wins) since 2002 (the furthest back I could get a value for a defensive rating) through 2015. From these teams, I parsed values for ERA, FIP, and defense, as well as the team’s payroll, runs scored, runs allowed, and run differential.

While taking my initial walks through the data, I saw two types of teams on this list. There were teams that scored few runs, but allowed even fewer, and there were teams that scored a host of runs, although they conceded a large, but lesser amount. The teams that scored little and allowed less had a common trend: they had great defenses and ERAs generally lower than FIPs. On the other hand, the teams that blasted the seams off the ball and had no problems putting runs on the scoreboard tended to have poor defenses, and their FIP-ERA difference was negative.

Using this data, I decided to run a regression analysis between a team’s defense and this FIP-ERA difference. There was a solid relationship between these two variables, with an r-squared of 0.48. This indicates that the difference between a team’s FIP-ERA difference tends to increase as the skill level of their defense increases.

Now we know correlation does not imply causation, but this relationship indicates the strength within this relationship. The better a team’s defense is, the more likely their defense will be able to positively influence their pitching staff’s performance. These were teams like the 2002 Atlanta Braves, the 2011 Tampa Bay Rays, or the 2004 and 2005 St. Louis Cardinals. These teams didn’t have great offenses, but they had great defenses, they had good team ERAs, and they prevented teams from scoring runs.

On the other hand, there were teams like the 2003 and 2004 Red Sox as well as the Mid-2000s Yankees. These teams were those with massive payrolls that paid a premium for a punishing lineup. These lineups, however, lacked defensive talent, causing their pitching staffs to underperform their expected performances, as their teams’ ERAs were higher than FIPs.

So how related is this FIP-ERA difference to the amount of runs allowed? Well, pretty strong, with an r-squared of 0.46. Again, a strong relationship, this time negative, indicating that as a team’s FIP-ERA increases, the runs that team allows decrease.

To reinforce this relationship, I looked at defense and runs allowed. Again, this relationship showed a good, not great relationship, with an r-squared at 0.28.

From these relationships, we can deduce that as a team’s defense rises in skill, the runs they allow tend to decrease and their team FIP-ERA difference tends to increase. Similarly, as a team’s FIP-ERA increases, the amount of runs a team allows decreases. From these relationships, we can conclude that these three variables are related.

As a team’s defense increases, they can positively influence the effectiveness of their pitching staff and will decrease their runs allowed. This may seem like common sense, and it probably is.

Now when we look at Bill James’ Pythagorean Win Expectation and other similar formulae, we notice that a team’s expected winning percentage is not dependent on the runs they score, but rather, their run differential. So yes, if you want to, you can construct a team like the Bronx Bombers and spend millions to assemble the some of the best lineups of recent history. If you’ll do that, you’ll hit score a host of runs, and with decent pitching and decent fielding (or below-average defense and good pitching — like those mid-2000s Yankees teams), you’ll be able to outscore your opponents and have a high run differential.

Or, you can assemble a team that will limit the amount of runs you’ll give up, by investing in defense. You will be able to compensate for average hitting and pitching, as you will boost your pitching staff’s effectiveness, and you will reduce the need for your offense to put up great numbers. Again, we have seen teams like this. The 2002 Braves were a combination of good defense, great pitching (aided by that defense), and average or perhaps even below-average offense; yet, this team won 101 games by scoring a mediocre 702 runs on the season (the average for the NL was 720 that season, 747 for all of baseball). Similarly, the 2011 Tampa Bay Rays put up 707 runs, against an American League average of 723, and still put up 91 wins and made the playoffs with good pitching and better defense. In fact, FIP would indicate their pitching was expected to perform right at American League average, a 4.08 ERA, yet they posted a 3.58 ERA.

Moreover, in that same season, the Los Angeles Angels won 86 games on just 667 runs, as they had even better pitching than the Rays. FIP would indicate the Angels’ pitching would be around a 3.94 ERA with league-average defense, but it was at a 3.57 ERA. The impact of good pitching paired with defense clearly is high, and I can’t think of one better, final example than the 2010 World Series-winning San Francisco Giants, who couldn’t have reiterated this structure any better: great pitching, great defense, and below-average offense.

So when one is trying to construct a team, and, unlike with the Yankees or Red Sox, money is a constraint, one might want to consider investing in defense. I say this because I looked directly at the relationship of a team’s payroll and their defensive ability, and it actually produced a negative relationship.

I know this data may be influenced by the fact that salaries have increased essentially every year in the span between 2002-2015, but if this truly did influence the graph, it would show either two things. Teams recently may have lessened their focus on defense and spent on hitting and pitching (explaining why defense-oriented teams had smaller payrolls); or, even with the rising caps, teams have still been able to assemble winning rosters by focusing on defense. Whether it is the first condition or the second, or perhaps a combination of both, perhaps defense is undervalued in today’s MLB. I doubt I’m the first to figure this one out, but the Cubs have far and away the best defense in baseball. Also, the Red Sox and Indians have stellar gloves as well, forming a solid second-tier level of defense that has put them in playoff position. So maybe Jason Heyward’s contract shouldn’t look so bad after all.

You don’t have to score a ton of runs to be a playoff baseball team. You just have to score more than the other team does, which can be done through limiting the amount of runs they score. It may seem like common sense, but common sense eludes us all at times.

There are many ways to construct a baseball team, and this might be just one more. And for stingy owners, it wouldn’t break the bank.

Is pitch velocity an indicator of a good pitcher?

Over this past summer, the Twins struck a deal with the Boston Red Sox to send specialist Fernando Abad to Boston for prospect Pat Light. Light, 25, first pitched in the majors in 2016, where in two innings with the Red Sox, he had allowed 8 runs (7 earned). After the deal, he has spent the rest of the season with the Twinkies. His numbers do not look much better, with an ERA of 10.22 in 12.1 innings pitched. Over his minor-league career, he has posted a 4.35 ERA in five seasons. Why did the Twins want this guy? He was 25, fully established as a reliever, and has only dominated the minors in 2016.

One of my theories is that the Twins saw that Light is a flame-thrower. Recently, he hit 101 miles per hour on a pitch. Are the Twins fixated on his high velocity? Looking at the Twins’ bullpen, another below-average pitcher, Ryan Pressly, is also touted for his high velocity.

I am not saying definitively that the Twins are focusing on pitchers’ velocities to value prospects and players; previously I wrote about how teams have focused on batters’ exit velocities, so perhaps the Twins have tried to apply this mentality toward pitchers.

Either way, I decided to delve into this topic, seeing if a pitcher’s velocity indicates a lower ERA, FIP, and BABIP, or a higher strikeout rate and walk rate. Using MLB’s Statcast, I was able to parse their data to record a pitcher’s average velocity. Using these data, I tried to establish the skill set of a flame-thrower.

To do this, I performed linear regressions between these different factors, seeing if any of these values are highly related to or influenced by faster pitching.

First, I looked at FIP and velocity. Below are the results:

Not a strong relationship, yielding an R-squared of 0.09. This relationship does show that as velocity increases, FIP tends to decrease, but again, not a very convincing relationship.

Next, I looked at ERA and velocity:

It yielded a similar result, a weak negative relationship, if any.

While the results for ERA and FIP were disappointing, I figured BABIP might look better. If a pitcher can throw faster, it would make sense that the batter would have a tougher time making contact, leading to weaker contact and a lower BABIP. Did the results agree? Have a look:

Disappointing. No relationship at all.

On to strikeout rate and walk rate.

I immediately thought of Aroldis Chapman. He has the fastest heater in the league, and his strikeout rate is above 40%, nearing the top of the league. I was much more optimistic for these metrics.

Here is velocity to strikeout rate:

Not a great relationship, yielding an r-squared of .13. It is a little stronger than anything else we have seen, but that is not saying much at all.

Finally, here is velocity and walk rate:

Not much going on here as well.

What does this all mean? Well, for starters, it shows that there are other factors that determine how effective a pitcher is. These data show that these metrics are not the end-all-be-all of a pitcher’s skill. Velocity is not a key indicator of an effective pitcher. Sure, the fastball probably needs to be upward of 85 miles an hour, but speed is not the most important factor. Rather, other skills, such as control, deception, and quality of breaking pitches might be just as important, if not more important, than velocity.

I don’t know if the Twins specifically targeted Light because of his velocity, but in his stint with the Twins, he’s averaged 10.9 walks per 9 innings. What good does his speedy fastball do if he cannot get it over the plate?

After my analysis, I’ll admit I’m a little surprised. I would think a higher velocity would mean a higher strikeout rate. But I am wrong. I guess for every flame-throwing Aroldis Chapman, there is an equally effective Andrew Miller, who does not posses the 105 mile-an-hour heater, but has a higher strikeout rate.

Last season, MLB released Statcast, an innovative tool used to evaluate player movements and athletic skill. Defensively, it can track how efficiently a player’s line to the ball was, how much ground he covered, arm strength, top speed, and many other factors. It also can track baserunning metrics, such as lead distance, grabbing an extra base, max speed, and home-run trot, among other things. Statcast also tracks pitching and hitting metrics. MLB teams can now use iPads in the dugout, meaning they have an endless supply of information at the touch of a finger.

Recently, Albert Chen of Sports Illustrated wrote a piece on various teams’ use of Statcast. The article notes how Pirates hitters would review a pitcher’s spin rate before an at-bat. If the spin rate was high, they would expect something lower in the zone. Even Kris Bryant credits Statcast, saying he improved his launch angle, aiding in his breakout, possibly MVP season. All teams have been using the data, says Chen, and teams have used the data in different ways. Daren Willman, who heads BaseballSavant, describes the use of Statcast as an “arms race,” as teams now have this bank of information at their disposal. Willman analyzes this Statcast data himself, looking at player comparisons and evaluations. The tricky thing, according to Willman, is knowing what information to look at. He claims “It’s so massive, it’s just about asking the right questions . . . the answers are all there.”

The Tampa Bay Rays, a forward-thinking club, tell their players on the first day of spring training that the Rays value their batted-ball velocity, rather than batting average. Similarly, the New York Mets decided to take Lucas Duda over Ike Davis to be their 1^st baseman of the future. Duda soon started to mash the ball, before struggling with injuries. Davis, on the other hand, is still looking for major-league employment.

Some of the highest exit velocities belong to sluggers like David Ortiz, Josh Donaldson, Miguel Cabrera, and Giancarlo Stanton. Perhaps this is not surprising. There are, however, some players who are not in the upper echelon of MLB, such as Chris Carter or Khris Davis. Both of these sluggers have low batting averages, but high exit velocities. At the same time, both of these players have solid slugging percentages, both fluttering around .500. What can this data tell us? Is exit velocity related to batting average? Slugging percentage? wOBA?

My initial thoughts pointed me towards BABIP (batting average on balls in play). My thinking was that if these players hit the ball harder, on average, then their contact will more likely than not will find its into being a hit. If the ball is hit harder, the defense has less time to react and make a play. I was looking at BABIP instead of just batting average, since BABIP will overlook a player’s tendency to strike out. A lot of the guys with high velocities are big swingers, so it would make sense if they tend to swing and miss. So I set out to test these hypotheses, and the results may surprise you.

At first, I looked at the relationship between BABIP and exit velocity by performing a linear regression between the two. Here is the result:

No relationship, at all. R-squared of 0.03. Looks like I’m 0 for 1 so far. My theory that harder-hit balls would result in more hits, on average, looks to be proved incorrect, as there is no relationship between the two in the data. Perhaps this aligns with the idea that a pitcher really has no control of a ball once it is put in play (unless it is a HR), as unless the batter hits a HR, he still has little or zero control over the result (as a reminder, HR is not included in BABIP since the ball is not in play).

So, I will continue to my next ideas. If these players are big swingers, they probably strike out more, right? Well, sort of; a weak correlation exists, if any at all. I’ll take the loss on this one — 0/2. With a correlation of 0.11, it is hard to say a relationship exists. Here is the graph:

I then looked at other hitting metrics to see if a relationship exists. Specifically, I looked at the stats generally associated with exit velocity: Home runs, slugging percentage, and isolated power.

First, I’ll show the relationship between the two. A relationship definitely exists here. It may not be a direct relationship, but players with high exit velocities had more home runs. Now, some of this is tied to other factors, such as how often they could make contact with a pitch, what their fly-ball and ground-ball rates are, and how often they strike out. These various factors will also play a role in the amount of home runs hit, as will exit velocity. Nonetheless, as one might expect, a relationship exists. The R-squared on the regression is 0.37. Here is the graph:

Next, I looked at slugging percentages as well as isolated power. The difference between these two metrics is that isolated power equals batting average subtracted from slugging percentage. It tracks how often a player hits for extra bases, since singles are subtracted out of the equation. Nonetheless, both of these metrics track total bases and include more information about the hitter’s power.

After running my regression between slugging percentage and exit velocity, the graph shows another relationship. Again, it is a weaker relationship, but a relationship exists. The R-squared on the regression again was 0.37, so about the same value as home runs and exit velocities. So again, players with higher exit velocities are more likely to have a higher slugging percentage. Here is the graph:

Isolated power again shows a similar relationship, as the R-squared on the regression was 0.39. Other factors explain isolated power, just as they do with slugging percentage and home runs, which goes to show that other factors are important as well, such as strikeout rate. Nonetheless, isolated power is related to exit velocity in a positive notion.

For those wondering, I left out metrics such as OBP and wOBA because they incorporate how often a player walks, which has nothing to do with how hard a player hits the ball. I did run the regressions, and the R-squared values were around 0.30 for both metrics.

So what does this all mean? Should teams focus on exit velocity? What about launch angle?

For the record, launch angle did seem to have a weak relationship with HR, with an R-squared value of 0.25, so another relationship seems to exist.

Wrapping it all up, it seems that exit velocity is a good way to determine the power of a player. Yes, there are other things, such as launch angle, strikeout rate, fly-ball and ground-ball rate, and other factors. Is it the end-all, be-all of a player? No, of course not, but it may be better able to tell a player’s true power than a recent stretch of hot play. Also, players must also learn to work the count and draw walks, which is separate from exit velocity.

Nonetheless, it is smart to look at exit velocities. There are other important factors, and teams should not neglect these factors, but focusing on exit velocities is a good way to determine the raw power of a player. Also, it can show the potential in an undervalued player, who may have a low batting average, but has an ability to hit for power that is hiding beneath a cold stretch.

Anyways, it looks like major-league baseball teams do know more than me. Oh well, I’m working on it.

For many Minnesota Twins fans, the recently vintage dominance of the AL Central that spanned seemingly the entirety of the first decade of the 2000s had been taken for granted. I, for one, am guilty of this, and like many fans, am starting realize that winning is not easy, although the Twins made it seem as easy as Torii Hunter made robbing home runs look effortless. Nostalgia aside, the Twins, and their fall toward mediocrity, are an interesting topic to look into. To some, they seemed a similar team to the Oakland Athletics (perhaps aiding in the creation of a post-season rivalry). The Twins, who were not quite as much of a small-market team as Oakland, seemed to develop from within. They had a deep minor system, so deep that when Johan Santana or Torii Hunter deemed it time to cash in, the Twins were able to find a quick replacement and continue their success. Santana, and Hunter, as well as Joe Mauer and Justin Morneau (who have both had their careers altered due to more recent concussions) and many other corner pieces, all made their debut in a Twins uniform and became cornerstones, yet they could never win the big playoff series.

They did not have the ability to flex the financial muscle that the Red Sox, Yankees, and even division rivals Detroit Tigers were capable of; however, they still managed to win the AL Central six out of the 10 years in the previous decade, including a loss in a playoff game to decide the division winner in 2008. The success carried into the Target Field era, represented by a beautiful ballpark that fans spent what seems like an eternity waiting for. After another disappointing playoff loss to the hated Yankees, the Twins entered 2011 looking to improve, with a similar roster and the intrigue of Japanese second baseman, Tsuyoshi Nishioka. That year was filled with injuries, and despite a post-All-Star Game push, the Twins ended the year with the worst record in the American League. Since then, the Twins have failed to reach the playoffs, and are currently battling with the Atlanta Braves for the worst record in baseball. Not to mention, long-time general manager Terry Ryan, the one credited with building the farm system leading to the team’s prior success, was fired on July 18^th. Time to find out where the Twins went wrong.

Those successful Twins teams were always credited for their small-ball and defensive skills. With Joe Mauer behind the plate, Torii Hunter (replaced by Carlos Gomez, who could also flash some leather) and many other solid defenders manning the diamond, a lot of the Twins’ success was credited to this defense.

Yet the Twins were far from a one-dimensional team. The Twins had a solid pitching staff, including, most famously, Johan Santana, who was a two-time Cy Young winner with the club, before being sent off to New York. The Twins also produced one of the most exciting pitching prospects at the time in Francisco Liriano. Liriano’s career was marred by injuries, which led to his inconsistency. Despite Johan’s departure and Liriano’s ineffectiveness, the Twins’ pitching was still an effective unit. The Twins raised their pitchers not on the attractive strikeouts, but on “pitching to contact.” The premise behind this was that pitchers would attack the lower half of the strike zone, induce weak contact, and show excellent control to give up few walks. It seemed to work, as pitchers with low to average strikeout rates were able to be effective pitchers, such as Scott Baker, Nick Blackburn, Kevin Slowey, and Brian Duensing.

Before I delve into my research, I should point to Voros McCracken’s ideas about Defense Independent Pitching for those less sabermetrically inclined (if you are sabermetrically inclined, feel free to skip the next few paragraphs). If I were to give a brief summary of his work, I would say McCracken’s main point is that if a pitcher does not give up a home run or strike out or walk a batter, then he has little control of what happens to the batted ball in play. A lot of what happens can be credited to luck, sequencing, and how good his defense is. For those unaware of sequencing, it is the idea that if a pitcher gave up three singles and a home run in an inning, there are many different possibilities of what could happen. The three singles could come in a row, followed by the dinger, for a total of four runs, or, two singles could come early, the pitcher gets a double play or some other way to get out of the jam, then gives up a home run with the bases empty, followed by another single and an out. In that scenario, only one run was surrendered, despite an equal amount of hits. McCracken suggests there is randomness in this effect, which combined with the quality of defense behind the pitcher and a good deal of luck, can make ERA a poor indicator of a pitchers true skill.

McCracken looked at defense-independent pitching stats (HR, BB, K) and defense-dependent stats (ERA), and noticed that the defense-independent stats correlate much better from year to year, and are a better indicator of how a pitcher will perform, since a pitcher does not have control of what happens to balls in play.

While McCracken did not actually create FIP, his work was a building block for modern pitching analysis. FIP (Fielding Independent Pitching) tracks what a pitcher’s stats would look like if he played behind a league-average defense and experienced league-average luck. It is a much better indicator of future performance than ERA. All the data I used was from 2007-2014. Over that span, for pitchers who pitched more than 100 innings in at least a two-year span, a pitcher’s ERA from one year to the next (tracking how consistent the stat is in tracking performance) had a correlation coefficient of 0.338. FIP, conversely, had a correlation coefficient of 0.476. Clearly, FIP performs better when predicting future performance, as McCracken suggested.

To end my digression on McCracken’s importance, if I had to sum up its importance to this article, it is that pitchers have little or no control over what happens to a ball in play.

When I was talking Twins recently with some recent, justifiably uneasy Twins fans, they attributed the Twins’ recent troubles to injuries and inconsistent pitching. This was when I was reminded of the “pitch to contact” philosophy heralded by the Twins. Since the days of recently past successes, the Twins have changed management, and hopefully have let go of this ideology. Anyways, I thought to myself that McCracken’s work and subsequent furthering of the topic do not go along with the pitch-to-contact philosophy. Sure, if a pitcher can prevent walks and home runs, then it does go along with part of McCracken’s ideas. But, if the goal is to induce weak contact, yet the pitcher does not have control of what happens to a ball when it is contacted, then there is a bit of a discrepancy.

So, like any other statistically-oriented college mind looking for how to spend the rainy days of my summer break, I decided to run some regressions to test if “pitch to contact” actually succeeded and the Twins were able to induce weak contact, or if the relative success of the pitching staff is related to luck and a good defense.

To reiterate, the data I looked at came from the seasons of 2007-2014. To sum up the Twins’ pitching through the period, the period starts with solid pitching from guys who lack the ability to post high strikeout rates, excluding the one season Santana pitched in the study. Guys like Scott Baker and Nick Blackburn had solid seasons early on, but Blackburn and many others faded once things went downhill for the team. From the outside looking in, it may seem like a chicken-or-the-egg scenario, whether it was pitching that caused the downfall or some other factor that caused the pitching to fail.

I gathered data for Twins pitching over this span, and compared it to the rest of the league. The pitch-to-contact philosophy was easily visible, as over this eight-year span, only five Twins pitchers had higher strikeouts per nine innings than league average (Johan Santana, Phil Hughes, Scott Baker, Francsico Liriano, Kevin Slowey). At the same time, only four pitchers had a walks per nine innings above league average (Nick Blackburn, Boof Bonser, Sam Deduno, and Liriano), and most of those seasons came in that pitcher’s last season with the team. The data shows that despite few strikeouts, Twins pitchers found some success in limiting numbers of walks. However, for those pitchers who struggled with control, their combined ERA in those seasons was 4.82, with a FIP of 4.60. Clearly, if a pitcher struggled with control, their success was hindered by the high walk rate.

Much of the Twins’ pitching was inconsistent over this time as well, as pitchers such like Blackburn or Brian Duensing seemingly went from quality starters to below-average pitchers. For the most part, I found this to be a team-wide theme. For pitchers with multiple years with the club, I correlated year-by-year ERA and FIP, to see if any consistent trends arose. Amazingly, there was no correlation from ERA from one year to the next, as the R-squared value was 0.002, stressing no relationship at all (graph). FIP, on the other hand, showed an R-squared value of 0.15; so while not a concrete relationship, a weak relationship exists (graph).

Why this lack of consistent ERA and FIP? This is where I think BABIP comes into play. Since FIP does not take into account BABIP, it did produce more reliable data. A few outliers threw off the data, and since it is not a large sample size, those outliers did affect correlation. By the nature of the relationship, this probably did more to affect the FIP correlation than the ERA, but nonetheless, the small sample size of pitchers from this period did affect the relationship. Interestingly, but perhaps not surprisingly, I performed a regression graphing FIP to ERA, and a solid relationship exists, with an R-squared of 0.36 (graph). This would be even better of a correlation if I took out seasons by Phil Hughes and Liriano, as in those two seasons their FIP was almost a full point lower than their ERA, respectively. This shows the validity of FIP as a metric, as it accurately predicts how a pitcher likely will perform based on independent factors.

Nonetheless, there is a clear difference here in the two pitching metrics. FIP implies a relationship, while ERA does not. How can this be? My theory is that it has to do with the pitch-to-contact philosophy. If pitchers are constantly relying on luck and defense to produce outs, rather than getting batters out themselves, then random variation will play much greater of a role in a pitcher’s effectiveness. Additionally, a team’s defense will play much greater of a role in pitching.

How much can a defense affect pitching? Well, I graphed the total WAR produced by the various Twins defenses against the team ERA from the 2007-2014 seasons. I additionally graphed BABIP against team defense. Amazingly, an ERA to defense regression produces an R-squared of 0.47 (graph), while a Defense to BABIP regression produces a 0.37 R-squared value (graph). Team defense clearly has a relationship with team ERA and team BABIP, as when the Twins defense was in its prime (2007, 2010), pitching performed well. Similarly, in the defense’s worst two seasons, the team also had its highest BABIP (2013, 2014). For those wondering, FIP to team defense produces no correlation (as we expect, since it does not account for a team’s defense) with an R-squared of 0.003.

What does this all mean?

Putting it all together, we notice a few trends. After 2010, the defense took significant steps back, along with pitching (ERA). As we expect, the team’s BABIP was affected by the defense’s regression. FIP, on the other hand, remained fairly constant through the span, showing how the defense must play a role in team ERA. For example, we will look at 2014. This was the defense’s worst year in the span, with a defensive WAR of -46.5. Team ERA was second-worst in this year, at 4.58. FIP, conversely, showed the team had its second-best year in pitching, with a value of 3.97. This shows that if the Twins would have had an average defense, their ERA would have been much lower.

As team ERA ballooned, the quality of the Twins’ defense fell. Since Twins pitchers were taught to rely on their defense through the pitch-to-contact ideology, this relationship was amplified. Pitching to contact, although relying on luck and defense, may have had some merit when the Twins’ defense was in its prime. If the team could get to more balls, produce a few more outs, then as long as the pitchers kept batters from getting on for free via the walk, the team would succeed. The pitcher would not need to strike out as many batters since the defense would make more outs than the normal team. This sounds nice on paper, but as the team defense decayed, the pitching regressed. This is most evident in 2014, as a solid pitching staff was marred by the defense behind them.

If the Twins were to truly focus on pitching to contact, then they should have looked at the defense, not the pitcher. At the same time, pitching to contact is flawed in a way. Why should a pitcher rely on a defense if he can just get the batter out himself? Teaching a pitcher not to use his natural talent to strike out a batter is counter-productive. I am not saying the Twins’ coaching staff directly did this, but when only four pitchers in an eight-year span have above-average strikeout rates, it raises the question. Perhaps the Twins looked for pitchers who were undervalued because of their low strikeout rates, and used these undervalued pitchers in their pitch-to-contact system. Yet, this does not seem to be the case, as the Twins pitchers with the lowest ERAs and FIPs were the pitchers with the highest strikeout rate, excluding Brian Duensing, whose downfall could have been predicted by his 3.82 FIP (to a degree), as it showed is 2.62 ERA would be much closer to 4.00 with an average defense. Even in a pitch-to-contact system, the pitchers with the best ability to get the batter out without putting the ball in play were the best pitchers.

If pitching to contact were to have a textbook year, it would be 2007, where a team with a 4.37 FIP had an ERA of 4.18. Yet, soon after, the defense plummeted, bringing the team pitching down with it. Clearly, through the team’s porous defense, the Twins gave up on pitching to contact, too. They just hadn’t realized it yet.

Hopefully, with the new management in place, pitching to contact is forgotten. While it is also important to keep a viable defense behind the pitcher, I still can’t trust the pitch-to-contact ideology. It had a good run, but seriously, when was the last time the Twins were able to produce a consistent pitcher out of a highly-praised prospect? Liriano wasn’t consistent, Kyle Gibson has yet to dominate, and Jose Berrios has looked shaky is his brief appearances. I think Scott Baker might be the answer to my question, but if not him, then maybe Johan Santana?

Clearly, the Twins need a new philosophy for grooming pitching. It’s a team riddled with questions, and this is not the lone answer, but it can be one step in the right direction for the team currently pegged at the bottom of the AL barrel.