I can’t remember if it was in a podcast or over the radio but when the trade deadline was approaching, there was talk about the effects of how a team trading away their stars would affect the playoff picture. Not in the way where a team has a hole in their rotation so they trade for a solid starter. No, this piece was talking about how trading a great player would make it easier for teams in that division to get ahead and how the newly-acquired player would make his new division harder to play in.

My first thought was there’s no way a player’s performance can impact a division so heavily, right? Baseball is a team sport and while affecting their own roster is one thing, affecting the outcome of four other teams in the process seems like a stretch. So I did a little bit of digging and here’s what I found.

For this study, I’ve included players that had a WAR of 2 or greater before being traded from 2007-2016. Additionally, I gathered data from the day they were traded of their old team’s winning percentage, new team’s winning percentage, old division’s winning percentage, and new division’s winning percentage. I also took the difference of their WAR per games played before and after the trade as a percentage.

First things first, let’s see if a great player can really impact a divisional outcome. Out of the 42 players in this study, only six (14.3%) had a positive WAR/G difference, a positive difference in winning percentage of their old division, and a negative difference in winning percentage of their new division:

Victor Martinez – 2009

Doug Fister – 2011

Hunter Pence – 2011

Mark Teixeira – 2008

Roy Oswalt – 2010

CC Sabathia – 2008

For Fister, Oswalt, and Sabathia, their new teams’ win percentage improved. For Martinez, Pence, and Teixeira, the win percentage decreased. All teams made the playoffs, however, with Fister and Oswalt making in to their respective league championship games. It’s interesting to see that the three players whose teams’ win percentage also improved are all pitchers, while the other three were all hitters.

The split between hitters and pitchers in the study was right down the middle, with 21 pitchers and 21 hitters. After their respective trades, 16 out of the 42 players had a positive WAR/G differential. Again, the results were right down the middle, with eight pitchers and eight hitters posting the positive WAR/G difference. Looking at the 26 players that had a negative WAR/G differential after the trade, you could’ve guessed it; half (13) were pitchers and the other half were hitters. I’m not 100% sure what that could mean, but I found it as a fascinating observation.

Out of the 42 teams that made trades in this study, three were under .500 when they made the trade; Reds for Scott Rolen (missed the playoffs), Red Sox for Cespedes (missed the playoffs), and Rangers for Hamels (ALDS). Let’s see how the rest of the teams that were .500 or better fared with their new trade pieces:

No Playoffs – 9 (23%)

Wild Card – 2 (5.1%)

DS – 14 (35.9%)

CS – 7 (17.9%)

WS – 5 (12.8%)

Won – 2 (5.1%)

It should be noted that the WAR/G differential doesn’t include playoff statistics. This is important to note while looking at players in this study that went to or won the World Series. For example, in 2015 the Royals acquired Johnny Cueto from the Reds. Looking at the data alone, Cueto had a -46.4% WAR/G differential and the Royals’ winning percentage dropped by 3.62% after the trade. Looks like a bad trade so far. Fast-forward to the ALCS where Cueto gives up eight earned runs in two innings against the Blue Jays. This trade looks like a disaster. Until Cueto takes the mound against the Mets in Game 2, allowing one run on two hits for the complete-game victory, edging the Royals closer to a World Series title. If given the opportunity again, do the Royals make the trade? Absolutely.

On the other side of the spectrum is Edwin Jackson, the only other player in this study to win the World Series. He as well sported a -63.5% WAR/G differential after the trade. The next question would be, would the Cardinals make the trade again? With a 5.76 ERA that postseason, my guess would be no.

The main question in this study is, “Does an impact player have so much influence in the game around them that they can shift the outcomes of a division?” The quick answer, and one that I’m sure everyone already knew, is not really. There is no correlation between the new division winning percentage change and the old division winning percentage change. A lot of the outcomes of divisional win percent changes seem to be circumstantial. Just because the new team’s division has gotten worse and the old division has gotten better doesn’t always mean that it’s the result of the player. It does seem apparent that a pitcher may have more of an influence than a hitter in these terms however (see Sabathia, Oswalt, and Fister above).

The biggest takeaway for me is that teams seem to be reluctant to overpay and make the smaller, longer-term deals as opposed to big-name rentals as seen at the deadline this year. It’s become apparent that just because you make the trade for the big-name player doesn’t guarantee a World Series victory, trip, or even a spot in the playoffs. Speaking of those big pieces, it will also be interesting to see how Quintana and Darvish affect the data after the season is over. Additionally, I would love to see the implications of a Harper or Trout trade to see if a hitter can ever truly be able to affect a divisional outcome. We can only dream.

Don’t you just love how talking about one topic in baseball can bring you to a completely separate topic than the one you were discussing? For instance, my friend and I were discussing possible landing spots for Mark Trumbo (before he decided to head back to Baltimore). One team that came up was the Colorado Rockies and how they shouldn’t have signed Ian Desmond and should’ve gone with Trumbo instead. This led to talking about the Rockies’ rotation and the fact that it wouldn’t matter what sluggers they had if the rotation was — for lack of better words — “trash.” This led me to think what I’m sure many of you are wondering: How is the Rockies’ starting rotation?

Now, we can look at ERA, FIP, and whatever advanced metric you prefer until we’re blue in the face. But what I wanted to focus on is what type of pitchers they bring into Coors Field, mainly in regard to batted-ball statistics. I want to see if the front office prefers to bring in ground-ball pitchers to combat the altitude and ballpark factors of the stadium. I also want to take a look at the pitch mix of their starting five to see if that has a hand in how their rotation is selected.

One would imagine that a pitcher with a good mix of ground balls and fly balls would be preferred in a starting rotation. Too many ground balls and you have a better chance of giving up more hits. Too many fly balls and you risk the opportunity for more home runs. Like the library on FanGraphs says, “If you allow 10 ground balls, you can’t control if zero, three, or nine go for hits, but you did control the fact that none are leaving the park.” Considering a park with the altitude and home-run factor of Coors Field, you would expect a rotation of primarily ground-ball pitchers to lessen the chance of a home run.

Let’s look at Tyler Chatwood and Chad Bettis first. Chatwood and Bettis have very similar stats across the board in addition to being the only two that are above-average ground-ball pitchers. While their HR/FB% are close and below league-average, where they both differ are the home and away splits. While Chatwood seems to get lit up at home, Bettis goes the opposite direction and actually has more fly balls go for home runs when he isn’t starting in Colorado.

Now let’s look at Jorge de la Rosa. Jorge has the worst HR/FB% of any starter on the team, by far. In fact, he was ranked 20th overall in 2016 for HR/FB%. Another stat that Jorge is last in for the starting rotation? Fastball usage, and by a considerable margin. For all MLB starting pitchers with a minimum of 60 IP, he ranks fifth-last in fastball usage in 2016. Maybe this is why the Rockies prefer to stick with fastball-type pitchers. Since 2011, the Rockies have used 21 different starting pitchers. Of those 21, 13 (62%) have been above the league average in fastball usage. In the four years that Jorge has been used as a starter, he’s sat at the bottom of the list three times (he was ranked eighth-last in 2013).

Something else I found noteworthy in the chart is that all five starters have higher fly-ball rates when pitching away as opposed to at home. While the difference for Tyler Anderson is very minuscule (0.2%), the fact that all five fall under this criteria makes it seem more than coincidental. Could they be pitching differently at home than they are when they’re away? Let’s take a historical look.

According to Baseball-Reference, this is the list of the most common Colorado Rockies starting pitchers from 2011 – 2016. The list gives us 30 total pitcher-seasons and 21 unique pitchers. Out of the 30 pitchers listed, 21 (70%) have a lower fly-ball rate at home than they do when pitching away. Additionally, 23 (76%) have a higher ground-ball rate at Coors as opposed to any other stadium. This leads me to believe that Rockies pitchers are conditioned to pitch differently when they are at home versus when they are away. This would make sense, since Coors has the highest park factor in all of baseball and anyone from a fair-weather fan to a front-office executive understands that keeping the ball on the ground in that park is best.

The last question we have to ask is, “Is this change effective?” The short answer is, not really. As seen, 14 out of the 30 (46%) pitchers have a higher HR/FB% when pitching away, while 15 out of the 30 (50%) pitchers have a higher HR/FB% when pitching at home (Eddie Butler in 2015 is the odd man out at an even 0.00%). The good news is that four out of the five latest seasons have the Rockies’ starting rotation having a lower HR/FB% than the league average for starting pitchers. The bad news is that all five seasons were losing seasons.

Like many of you, I spend hours on FanGraphs trying to take in as much information as possible. One of the more fascinating statistics to me is the category of plate discipline. This includes how often a batter will swing on a pitch inside or outside of the zone, how often a batter swings and misses, and many other variables that affect a player’s approach at the plate. While these numbers alone are a good indication on how a player acts at bat, I wanted to know how these numbers affected performance. For instance, it would make sense that a higher O-Swing% could lead to less-than-average hitting. The 2015 season had Adam Jones second in O-Swing%, swinging at 47% of pitches outside of the strike zone. Pablo Sandoval led the league in O-Swing% with a 48% rate. Jones recorded a 109 wRC+ while the Kung Fu Panda had a 75 wRC+.

By breaking down these Plate Discipline statistics for the 2015 season, I believe that we can get a good answer on which statistic leads to the best performance. For my methodology, I used the wRC+ and Plate Discipline leaderboard for the 2015 season. After breaking down each statistic, I compiled a top 10 and bottom 10 wRC+. Additionally, I grouped percentages to get the number of batters and average wRC+ for certain percentages.

O-Swing %

O-Swing% = Swings at pitches outside the zone / pitches outside the zone

Top 10 wRC+ Average: 97

Bottom 10 wRC+ Average: 132

O-Swing% gives us a pretty good indication of a player’s overall performance. It’s no surprise that patience and a good eye are part of a skill set that leads to a higher wRC+. For each 10-percent decrease of O-Swing percentage, batters see an increase of over 10 points for their wRC+. The top 10 wRC+ compared to the bottom 10 also tells a compelling story of what O-Swing tells us. In the top 10, we see a couple of above-average hitters like Starling Marte and Nolan Arenado. However, we also see five of the top 10 with a wRC+ under 100 and one hitter (Marlon Byrd) at 100. On the other side of the spectrum, there isn’t a hitter under 100 wRC+ in the bottom 10. The difference in wRC+ between the top and bottom 10 is 35, the biggest difference between all the statistics.

Let’s look at two very different extremes: Joey Votto and Pablo Sandoval. Sandoval had an O-Swing% of 48 percent while Votto had a 19 percent rate, which means that while Sandoval is swinging at almost half of the balls he faces, Votto is taking a little more than 80% of pitches out of the zone. Sandoval faced 1848 pitches (1287 strikes to 561 balls) while Votto faced 3020 pitches (1644 strikes to 1376 balls). Sandoval’s more than double strike-to-ball ratio and Votto leading the league in walks can both be explained by their O-Swing percentage.

Z-Swing %

Z-Swing%  = Swings at pitches inside the zone / pitches inside the zone

Top 10 wRC+ Average: 111

Bottom 10 wRC+ Average: 115

The first thing that I noticed when looking at the Z-Swing charts is the duplication of names from the O-Swing charts. Adam Jones, Avisail Garcia, Marlon Byrd, and Starling Marte showed up on both the O and Z Swing percentage top-10 while Ben Zobrist, Carlos Santana, and Brett Gardner appeared on both bottom-10 lists. This is a very mixed bag of players for both the top and bottom. Both have a 100 wRC+ hitter, the epitome of average. Both have seven hitters batting above 100 wRC+ meaning that both lists also have two hitters batting below 100. The top and bottom 10 averages are almost even. The one outlier that separates them is Mike Trout in the bottom 10 with a 172 wRC+. Seeing the same name on multiple lists can tell us a lot about a player. Someone like Marlon Byrd will swing at most of the pitches you send his way while Ben Zobrist will take a pitch outside of the zone about 77% of the time but will also take a strike 45% of the time as well.

O-Contact %

O-Contact% = Number of pitches on which contact was made on pitches outside the zone / Swings on pitches outside the zone

Top 10 wRC+ Average: 104

Bottom 10 wRC+ Average: 104

Similar to Z-Swing%, O-Contact doesn’t show much disparity between the top and bottom 10. In fact, they’re identical at 104 wRC+. A higher O-Contact gives a batter more balls in play, but doesn’t always lead to success. My initial thought was that swinging at a pitch way out of the zone can lead to weak contact, and usually an out. The fact the top and bottom are identical shows that this isn’t always the case.  It also makes sense why the middle of the pack (60%-69%) has the greatest wRC+ (besides the small sample size of < 50%). These batters are still able to make contact with pitches outside of the zone more than half of the time, but also miss the pitch enough of the time where they don’t make bad contact.

Z-Contact %

Z-Contact%  = Number of pitches on which contact was made on pitches inside the zone / Swings on pitches inside the zone

Top 10 wRC+ Average: 110

Bottom 10 wRC+ Average: 125

Z-Contact was the most surprising statistic in terms on its effect on wRC+, until you look at the names in the bottom 10. One would expect that hitters that hit more pitches in the zone would be the better performers. However, the bottom 10 is filled with power hitters, leading to the main difference in wRC+. Davis and Cruz were number one and two in terms of home-run leaders in 2015. In fact, besides Michael Taylor, the bottom 10 is all in the top 50 for home runs in the MLB. The list makes sense as players like Chris Davis are trying to “Crush” the ball out of the park and swing harder than someone in the top 10 like Martin Prado.

SwStrike %

SwStr% = Swings and misses / Total pitches

Top 10 wRC+ Average: 110

Bottom 10 wRC+ Average: 105

Not surprisingly, the top 10 for SwStrike looks a combination of both the O-Contact and Z-Contact bottom 10. Obviously if your contact is low, you’re going to have more swings and misses. The main factor that stood out to me looking at the top and bottom 10 is the deviation of wRC+. The top 10 is all over the place, having players like Kris Bryant with a 136 wRC+, Michael Taylor with 69, and every level of player in between. The bottom 10 has less variation, providing a more consistent group of hitters.

Totals

As evidenced by the chart, the main statistic in regards to plate discipline to show a great change in performance that compares the bottom to the top level is O-Swing percentage. Z-Contact seems to also be relevant when evaluating and predicting a player’s performance.