Archive for Player Analysis

The Nats signed reliever Casey Janssen, formerly of the Blue Jays, to a one-year, $5-million contract a few weeks ago (feel free to stop reading now to avoid the existential dread associated with over-analyzing Casey Janssen). Overall, it’s hard not to like this pick-up. One year and five million dollars is basically nothing (except when it comes to signing a second baseman), and the Clippard trade certainly left a hole in the bullpen. There was also a recent stretch of time when Janssen was quite good. From 2011-2013, Janssen averaged 57.1 IP, 8/9 strikeouts per 9 innings, and a sub 3 FIP. WAR isn’t the best way to measure relievers, but he averaged 1.2 WAR a season over those three years, which put him squarely in the pretty damn good category of relief pitchers.

So why did a recently good closer sign for a seemingly below market sum? Because 2014 was mostly terrible. Strikeouts were way down (5.5 Ks/9 in 2014 compared to 8.5 in 2013), Homers were way up (1.2 HR/9 in 2014 compared to 0.5 in 2013), and his groundball percentage dropped from 48% to 34%. These are all fairly alarming trends for a relief pitcher that is 33 and doesn’t throw very hard (2014 average fastball velocity: 89.3 miles per hour). Every analysis for relief pitchers should contain small sample size warnings in all capital letters, but important indicators trending that strongly generally indicate something wrong happening.

In July of last season, Janssen came down with a particularly awful bout of food poisoning, and he probably came back too quickly. And looking at the mid-season splits, there’s a case to be made that it was the (negative) turning point for the rest of Janssen’s season. Let’s compare:

1st half: 22 IP, 1.23 ERA, 0 HR, 14 Ks, 1 BB, .218 wOBA against
2nd half: 23.2 IP, 6.46 ERA, 6 HR, 14 Ks, 6 BBs, .378 wOBA against

In the first half, Janssen made opposing hitters look like Austin Kearns. In the second half, they all looked like Yasiel Puig. His numbers did take a nosedive in July when he was sick, but got worse in August when one would have expected him to be feeling better (or put on the DL to recuperate). It’s impossible for anyone to really know how he was feeling, and if food poisoning actually was the main cause of Janssen’s second-half struggles. But, his velocity didn’t change from the first half to the second half, and his strikeout rate remained about the same. The uptick in walks and home runs in the second half are troubling, but maybe first-half Janssen was a fluke based on a year over year decrease in velocity (lost about .8 MPH on his fastball from 2013 to 2014)  and a decrease in strikeouts. For comparisons sake, here is an unnamed reliever’s 1st and 2nd half splits in 2014:

1st half: 37 IP, .97 ERA, 1 HR, 36 Ks, 11 BBs, .208 wOBA against
2nd half: 25 IP, 6.48 ERA, 3 HR, 23 Ks, 8 BBs, .375 wOBA against

This reliever? Rafael Soriano. There wasn’t an injury narrative to fault for his falling off a cliff bad second half, but he stunk nonetheless. Screwy things can happen in small samples, which is why we try to avoid over-analyzing them. Janssen may have just had impeccable timing, and his new true talent level as a command relief pitcher is that of a 4.00 ERA. But unlike with Soriano, there is a realistic narrative for Janssen that fits the timeline of his struggles. Here’s another 1st half/2nd half comparison

1st half:

2nd half:

While his K rate was basically the same from the first half the second half, these charts show that his whiff rates weren’t. Janssen had much more success both down and up in the zone earlier in the season in terms of swings and misses, so while his velocity was the same between the first half and second half, it appears that his stuff wasn’t.

Again, in such small samples, it’s impossible to draw any definitive conclusions. It’s true that first-half Janssen looked pretty similar to 2011-2013 Casey Janssen, while second-half Janssen looked more like Brian Bruney. It’s reasonable to look at the splits and say that Janssen’s bout with food poisoning ruined what looked to be a promising season. It’s also reasonable to look at his decrease in velocity and strikeout rate and think this was money not well spent. But for a paltry (in the context of the MLB) five million dollars, it’s not that much money anyways, so why the hell not?

The “Home Run Derby Curse” has become a popular concept discussed among the media and fans alike. In fact, at the time of writing, a simple Google search of the term “Home Run Derby Curse” turns up more than 180,000 hits, with reports concerning the “Curse” ranging from mainstream media sources such as NBC Sports and Sports Illustrated, to widely read blogs including Bleacher Report and FanGraphs, to renowned baseball analysis organizations like Baseball Prospectus and even SABR.

This article seeks to shed greater light on the question of whether the “Home Run Derby Curse” exists, and if so, what is its substantive impact. Specifically, I ask, do those who participate in the “Home Run Derby” experience a greater decline in offensive production in comparison to those players who did not partake in the Derby?

Answering this question is of utmost importance to general managers, field managers, and fans alike. If players who partake in the Derby do experience a decline in offensive production between the first and second halves of the season, those in MLB front offices and dugouts can use this information to mitigate potential slumps. Further, if Derby participation leads to a second half slump, fantasy baseball owners can use this knowledge to better manage their teams. Simply put, knowing the effect of Derby participation on offensive production provides us with a deeper understanding of player production.

The next section of this study will address previous literature concerning the “Home Run Derby Curse,” and will discuss how this project builds upon these studies.

Previous Research

Although a good deal of research has been conducted concerning the “Curse,” the veracity of much of this work is difficult to assess. Many of the previous studies on this issue have used subjective analysis of first and second half production of Derby participants in order to assess the effects of the “Curse” (see Carty 2009; Breen 2012; Catania 2013). Although these works have certainly highlighted the need for further research, they are simply not objective enough to definitively address the question of the “Home Run Derby Curse’s” existence.

To date, the most rigorous statistical analysis of the “Curse” is an article by McCollum and Jaiclin (2010), which appeared in Baseball Research Journal. In examining OPS and HR %, McCollum and Jaiclin found a statistically significant relationship between participation in the Derby and a decline in second half production. At the same time, they examined the relationship between first and second half production in years in which players who had previously participated in the Home Run Derby did not participate, and found no statistically significant drop off in production in those years.

At first glance, this appears to be fairly definitive evidence that the “Curse” is real, however, they also found that players’ production in the first half of the season in years in which they participated in the Derby was substantially higher than in those years in which they did not participate in the Derby. This suggests that players who partake in the Derby are chosen because of extraordinary performances. Based on these finding, McCollum and Jaiclin conjectured that the decline in performance after the Derby for those who participated is likely due to the fact that their performance was elevated in the first half of the season, and the second half decline is simply regression to the mean.

Despite the strong statistical basis of McCollum and Jaiclin’s work, there are a number of points in this work that need to be addressed. First, McCollum and Jaiclin only examine those players who have participated in the Derby and have at least 502 plate appearances in a season, thus prohibiting direct comparison with those who did not participate in the Derby. At the heart of the “Home Run Derby Curse,” however, is the idea that participants in the Derby experience a second half slump greater than is to be expected of any player.

The question that derives directly from this conception is, do Derby participants experience a slump greater than is to be expected from players who did not participate in the Derby? To sufficiently answer this question, players who participated in the Derby must be compared to those who did not. Due to a methodology that relies upon data of only Derby participants, Jaiclin and McCollum were unable to sufficiently answer this question.

Second, McCollum and Jaiclin use t-tests to test their hypotheses. This method is a strong objective statistical approach, however, it is not ideal, as it does not allow for the inclusion of control variables. Thus, there may be additional factors affecting both the relationship between Derby participation and second half production simultaneously, creating a spurious finding. This problem can only be addressed through multivariate regression.

The final issue with McCollum and Jaiclin’s work centers on their theoretical expectations and their measures of offensive production. Theoretical extrapolation is absolutely necessary in statistical work as it informs analysis. Without theoretical expectation, researchers are simply guessing at how best to measure their independent and dependent variables. Very little theoretical explanation of the “Curse” has put forth in previous work on the “Curse,” including McCollum and Jaiclin’s piece, and therefore, their measurement of offensive output is not necessarily best.

This article is an attempt to build upon previous work concerning the “Home Run Derby Curse,” and to address the above issues. In the next section, I will develop a short theoretical framework concerning the “Curse.” Four hypotheses are then derived from this theory, which are tested using expanded data, and a different methodological approach. The results suggest that a “Curse” does exist.

Theoretical Basis of the Home Run Derby Curse

Two main theories have been posited to explain the “Home Run Derby Curse.” First, it has been suggested that participation in the Derby saps players of energy that is necessary to continue to perform well in the second half of the season (Marchman 2010). This theory is summarized well by Marchman who focused on the particular experience of Paul Konerko who went deep into the 2002 Home Run Derby in Milwaukee. He wrote:

The strange experience of taking batting practice without a cage under the lights in front of tens of thousands of people left him sore in places he usually isn’t sore, like his obliques and lower back, the core from which a hitter draws his power. Over the second half of the year, he hit just seven home runs, and his slugging average dropped from .571 to .402.

In essence, this theory argues that players who participate in the Derby experience muscle fatigue in those muscles from which power hitting is drawn. Because these fatigued muscles are imperative for power hitting, players who participated in the Derby experience reduced power, and see a drop in power numbers in the second half of the season. Thus, one can hypothesize:

H1.1 Players who participate in the Derby will see a greater decline in their power numbers than players who do not participate in the Derby.

Furthermore, one might expect that a player will experience greater decrease in energy the more swings he takes during the Derby. The logic underpinning this assertion is that using the power hitting muscles for a longer period of time should fatigue them to a greater extent. Thus, a player who takes 10 swings in the Derby should experience less muscle fatigue than a player who takes 50 swings in the Derby. Following this line of reasoning, one should expect that the Derby has a greater effect on players’ second half power hitting performance when they take more swings during the Derby. Since those players who hit more home runs during the Derby take more swings, it can also be hypothesized:

H1.2: Players who hit more home runs in the Derby will see a greater decline in power numbers in the second half of the season than players who hit fewer home runs in the Derby (including those who do not participate).

The second theory of the “Curse” proposes that participation in the Derby leads to players altering their swings (Breen 2011; Catania 2013). It is thought that this altered swing carries over into the second half of the season, affecting players’ offensive output.

Although most studies of the “Curse” rarely delve into how players tweak their swings, it is likely safe to assume that they are changing their approach in the hope of belting as many homers as possible for that one night – developing an even greater power stroke. It is a commonly accepted conjecture that power and strikeouts are positively correlated (see Kendall 2014), meaning that greater power is associated with more strikeouts. This conjecture may not be as true for exceptionally talented players (i.e. Hank Aaron, Ted Williams, Mickey Mantle, Willie Mays, etc.) However, if we accept this assumption to be correct for the majority of players, it can be stated that if players change their swing to hit more home runs, they should see a corresponding increase in strikeouts in the second half of the season.[i] Thus, it can be hypothesized:

H2.1: Players who participate in the Home Run Derby will experience greater strikeouts per plate appearance in the second half of the season than those who did not participate in the Derby.

As with hypotheses 1.1 and 1.2, it can also be assumed that the effect of participation in the Derby will be greater the more swings an individual takes during the Derby. That is to say, if a player hits more home runs during the Derby, the altered swing he uses during the Derby will be more likely to carry through to the second half of the season. This leads to the hypothesis:

H2.2: Derby participants who hit more home runs in the Derby will experience greater strikeouts per plate appearance in the second half of the season than those who hit fewer home runs in the Derby (including those who do not participate).

In the next section of this study I will discuss the analytical approach, variable operationalization, and the data sources used to address the above four hypothesis.

Data and Analytical Method

Below I will begin with a discussion of the data used in this study. I will then discuss the independent and dependent variables for each hypothesis as well as the control variables used in this study. Finally, I will discuss the methodological approach used in this study.

Data Sources and Structure

Above, it is hypothesized that those players who either participated in the Derby, or performed well in the Derby will see greater offensive decline between season halves than those who either did not participate in the Derby, or struggled in the Derby. In order to properly test these hypotheses one must use data that includes those who participated in the Home Run Derby, and those who did not participate in the Derby.

This paper performs a meta-analysis of all players with at least 100 plate appearances in both the first and second halves of the season from 1985 (the first year in which the Home Run Derby was held) through 2013. This makes the unit of analysis of this paper the player-year. This data excludes observations from 1988 as the Derby was cancelled due to rain. Further, 1994 is also excluded as the second half of the season was cut short due to the players’ strike.

Independent Variables

The main independent variable for hypotheses 1.1, and 2.1 is a dichotomous measure of participation in the Home Run Derby. A player was coded as a 1 if they participated in the Derby and a 0 if they did not participate in the Derby. Between 1985 and 2013 a total of 229 player-years were coded as participating in the Derby.

The independent variable for hypotheses 1.2, and 2.2 is a measure of each player’s success in the Home Run Derby. This is an additive variable denoting the number of home runs each player hit in each year in the Derby. This variable ranges from 0 to 41 (Bobby Abreu in 2005). Those who did not participate in the Derby were coded as 0.[ii]

Dependent Variables

Hypotheses 1.1 and 1.2 posit that participation in the Derby and greater success in the Derby will lead to decreased power numbers respectively. Power hitting can be measured in numerous ways, the most obvious being home runs per plate appearance (HRPA). However, theoretically, if players are being sapped of energy, this should affect all power numbers, not simply HRPA. Restricting one’s understanding of power to HRPA ignores other forms of power hitting, such as doubles and triples. So as not to remove data variance unnecessarily, one can measure change in power hitting by using the difference between first and second half extra base hits per plate appearance (XBPA) rather than HRPA.

Thus, the dependent variable for hypotheses 1.1 and 1.2 is understood as the difference between XBPA in the first and second halves of the season for each player-year. XBPA is calculated by dividing the number of extra base hits (doubles, triples, and home runs) a player has hit by the number of plate appearances, thus providing a standardized measure of extra base hits for each player-year.

The dependent variable for hypotheses 1.1 and 1.2 was created by calculating the XBPA for the first half of the season, and the second half of the season for each player-year. The XBPA for the second half of the season for each player-year was then subtracted from the XBPA for the first half of the season for each player-year. Theoretically, this variable can from -1.000 to 1.000. In reality this variable ranges from -.116308 to .1098476, with a mean of -.0012872, and a standard deviation of .025814.

The dependent variable for hypotheses 2.1 and 2.2 is the difference between first and second half strikeouts per plate appearance (SOPA) for each player-year. SOPA is calculated by dividing the number of strikeouts a player has by his plate appearances, thus providing a standardized measure of strikeouts for each player-year.

The dependent variable for these hypotheses was created by calculating the SOPA for the first half of the season, as well as the second half of the season for each player-year. The SOPA for the second half of the season for each player-year was then subtracted from the SOPA for the first half of the season for each player-year. Theoretically, this variable can from -1.000 to 1.000. In reality this variable ranges from -.1857143 to .1580312, with a mean of -.003198, and a standard deviation of .0378807.

Control Variables

A number of control variables are included in this study. A dummy variable denoting whether a player was traded during the season is included.[iii] To control for the possible effects of injury in the second half of the season, a dummy variable denoting if a player had a low number of plate appearances in the second half of the season is included.[iv] Further, I include a dummy variable measuring whether a player had a particularly high number of first half plate appearances.[v] Finally, controls denoting observations in which the player played the entire season in the National League,[vi] observations that fall during the “Steroid Era,”[vii] observations that fall in a period in which “greenies” were tolerated,[viii] and observations that fall during the era of interleague play are included.[ix]

Analytical Approach

The main dependent variables used to test the above hypotheses are the difference between first and second half XBPA, and the difference between first and second half SOPA. For each of these variables, the data fits, almost perfectly, a normal curve.[x] For each of these variables, the theoretical range runs from 1 to -1, with an infinite number of possible values between. Although these variables cannot range from infinity to negative infinity, the most appropriate methodological approach for this study is OLS regression.

In the next section of this piece, I will report the findings of the tests of hypotheses 1.1 through 2.2. I will then discuss the implications of these findings.

Analysis

This section will begin with the presentation and the discussion of the findings concerning hypotheses 1.1 and 1.2. I will then present and discuss the findings of tests of hypothesis 2.1 and 2.2.

Analysis of Hypotheses 1.1 and 1.2

Column 1 of table 1 shows the results of the test of hypothesis 1.1. The intercept for this test is   -.0007, but is statistically insignificant. This suggests that, with all variables included in the test held at 0, players will see no change in their XBPA between the first and second halves of the season. The coefficient for the “Derby participation” variable shows a statistically significant coefficient of .008. This means, if a player participates in the Derby, he can expect to see his second half XBPA drop by .008.

Of course, there is a possibility that those who participate in the Derby will see a greater drop in their XBPA than the average player because, in order to be chosen for the Derby, a player will have a higher XBPA.[xi] This would then make it more likely that players who participate in the Derby see a greater drop in XBPA than players who do not participate as they regress to the mean. To account for this, the sample can be restricted to players with a high first half XBPA.

The mean first half XBPA for all players (those who do and do not participate in the Derby) between 1985 and 2013 is .0766589. The sample is restricted to only those players above this mean. This is done in for the tests displayed in column 2 of table 1. As can be seen, the intercept is statistically significant, with a coefficient of .01. Those who have average or above average XBPA in the first half of the season can expect to see their XBPA drop by .01 after the All-Star Break when all other variables in the model are held equal.

Table 1: The Effect of Home Run Derby Participation on the Difference in XBPA.

Note: Values above represent unstandardized coefficients, with standard errors in parentheses. *p<.05, **p<.01, ***p<.001

Turning to the Derby participation variable, one notices that it is now statistically insignificant with a coefficient of .02. When restricting the sample to only those who showed average or above average power in the first half of the season, the results show that those who participate in the Derby will see no statistically discernible difference in their power hitting when compared to those who did not participate in the Derby.

The variables denoting if a player had a low number of plate appearances in the second half of the season, or a high number of at-bats in the first half of the season, are statistically significant and both present with negative coefficients. Meaning if a player has a high number of at-bats in the first half of the season or if he has a low number of plate appearances in the second half of the season, he will actually see an increase in XBPA.

Although the results in column 2 of table 1 are telling, it may be useful to restrict the sample even further. Those who are selected for the Derby are, for all intents and purposes, the best power hitters in baseball. Therefore, one can restrict the sample to only the best power hitters and compare only those players with a first half XBPA equal to, or above the average for those who participated in the Derby, while, of course, keeping all Derby participants in the sample.

The mean first half XBPA for Derby participants between 1985 and 2013 is .1138781. Tests restricting the sample to only those with a first half XBPA of .1138781 are displayed in column 3 of table 1. The intercept for these tests is statistically significant and shows a coefficient of .04. Meaning those with a first half XBPA of .1138781 can expect to see a drop of .04 in their XBPA after the All-Star Break. The coefficient for the variable measuring participation in the Derby is -.004, but is statistically insignificant. This suggests that those who participate in the Derby do not see a marked decrease in their power hitting after the Derby when compared to those of similar power hitting prowess.

The only variable that shows a statistically significant effect in these tests is that which denotes whether a player had a high number of first half at-bats. As with previous tests, the coefficient for this variable is negative. This suggests that players who have a high number of first half at-bats see an increase in the XBPA between the first and second halves of the season in comparison to those without a high number of first half at-bats.

Columns 1 through 4 in table 2 show the tests of success in the Derby (the number of home runs hit) on the difference between first and second half XBPA. The test with a full sample is displayed in column 1 of table 2. The intercept for this test is statistically insignificant, suggesting that on average, players do not experience a marked change in their XBPA between the first and second halves of the season.

The variable denoting the number of home runs a player hit during the Derby is statistically significant and has a coefficient of .0003. This means that for every home run a participant in the Derby hit he can expect his XBPA after the All-Star Break to decline by .0003 points.

Of course, the relationship between Derby success and the difference in first half and second half XBPA is not likely to be linear, but rather curvilinear. Thus, a measure of home runs hit during the Derby squared should be included. The test including this variable is displayed in column 2 of table 2. The intercept is again statistically insignificant suggesting that when all variables in the model are held at 0, players should not see a marked change in their XBPA between the first and second half of the season.

Table 2: The Effect of the # of Home Runs Hit in the Derby on the Difference in XBPA.

Note: Values above represent unstandardized coefficients, with standard errors in parentheses. *p<.05, **p<.01, ***p<.001

The effect of success in the Derby remains statistically significant with a coefficient of .001. This means that with each home run a player hits in the Derby his XBPA in the second half of the season will decline by .001. Further, the variable “home runs squared” is statistically significant, and has a coefficient of -.00004. This indicates that the effect of the number of home runs a player hits in the Derby on second half production decreases with more home runs. In essence, hitting 40 home runs during the Derby does not have the same effect on second half offensive production as hitting 30 home runs during the Derby, and so on.

In terms of control variables, the variable denoting a high number of first half at-bats is statistically significant with a negative coefficient in the tests reported in column 1 of table 2. Further, in the tests reported in column 2 the variable denoting a diminished number of plate appearances in the second half of the season is statistically significant and negative.

As with the tests reported in table 1, restricting the sample to only those players with average and above average first half XBPA may be useful. Column 3 of table 2 shows the results of the test of the effect of success in the Derby on the difference in XBPA between the two halves of the season when the sample is restricted to those with a first half XBPA at or above the leagues’ average (.0766589).

The intercept in this test is statistically significant with a coefficient of .012. This means that, when all variables included in this test are held at 0, players with an average or above average first half XBPA notice a decline in the second half XBPA. Importantly, the effect of the number of home runs hit during the Derby is statistically insignificant, meaning that hitting more home runs during the Derby has no statistical effect on the difference between first half and second half XBPA when the sample is restricted to those with average or above average first half XBPA.

Both the variable denoting whether a player had diminished second half plate appearances, and the variable denoting whether a player had a high number of first half at-bats, are statistically significant with negative coefficients. This implies that those who experience a diminished number of second half plate appearances, and those players with a high number of first half at-bats see an increase in their XBPA between the first and second halves of the season.

Column 4 of table 2 restricts the sample based on the mean first half XBPA of those who participated in the Derby. The mean first half XBPA of these players is .1138781. The intercept for this test is statistically significant with a coefficient of .036. The variable measuring success in the Derby is statistically insignificant, meaning that the number of home runs a player hits in the Derby has no statistical effect on the difference between first and second half XBPA when comparing Derby participants to similar power hitters who did not participate in the Derby.

In terms of controls, the variable denoting whether a player had a high number of first half at-bats is again statistically significant with a negative coefficient. This, as with previous tests, suggests that those who have a high number of first half at-bats will experience an increase in XBPA between the first and second halves of the season.

Analysis of Hypotheses 2.1 and 2.2

The results of the tests of hypothesis 2.1 (participation in the Derby will lead to more strikeouts per plate appearance) are displayed in column 1 of table 3. This column shows the relationship between participation in the Derby and the change in SOPA between the first and second halves of the season. As can be seen, the intercept is -.006 and is statistically significant, meaning, all other things equal, players strikeout more often in the second half of the season.

The coefficient for “Derby participation” is -.005 and is statistically significant, meaning that those who participate in the Home Run Derby will see their second half SOPA increase by .005 between halves of the season in comparison to players who do not participate in the Derby. When one takes into account that SOPA should increase by .006 when all other variables are held at 0, this finding suggests that Derby participants should see an increase of .011 in their SOPA between the first and second halves of the season.

Unlike XBPA, there is very little chance that SOPA is associated with selection for the Home Run Derby. Moreover, the average first half SOPA for the entire sample used in this study is .1610312, whereas the mean first half SOPA for those who participated in the Home Run Derby is .1669383. Those who participated in the Derby were actually more likely to strikeout in any given plate appearance than those who did not participate in the Derby. Essentially, assuming that one should see a regression to the mean, it is more likely that those who participate in the Derby would see a decrease in SOPA between the first and second halves of the season. These results, however, tell the opposite story, and cannot be explained by a mere statistical anomaly. Therefore, it is unnecessary to restrict the sample, and one can state that hypothesis 2.1 is supported.

Turning to column 2 of table 3, one sees a test of hypothesis 2.2 (the more home runs a player hits in the Derby the smaller the difference between his first and second half SOPA will be). Much like the results in column 1 of table 3, the intercept is -.006 and is statistically significant. Thus, when holding all other variables at 0, one can expect the difference between a player’s first and second half SOPA to increase by .006.

The coefficient for the variable denoting the total number of home runs a player hit during the Derby shows a statistically significant coefficient of -.0005. For every home run a player hit during the Derby, the difference between his first half SOPA and second half SOPA will decrease by .0005.

This relationship, however, is likely curvilinear. In order to account for this likelihood I include a variable in which the total number of home runs a player hit during the Derby is squared. Column 3 of table 3 reports the results of a test including a measure of the total number of home runs squared. When this variable is included the coefficient of the total number of home runs becomes statistically insignificant. This suggests that the total number of home runs a player hit during the Derby is not related to the difference in that player’s first half SOPA and second half SOPA. It must be noted, however, that this finding does not negate the result in column 1 of table 3.

Interestingly, there are a number of control variables that show statistical significance in all tests included in table 3. If a player was traded midseason, he can expect to see the difference between his first half SOPA and second half SOPA to shrink, meaning he will see an increase in SOPA in the second half of the season. Further, having a below average number of plate appearances in the second half of the season leads to a decrease in second half SOPA.
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Table 3: The Effect of Home Run Derby Participation and the # of Home Runs Hit in the Derby on the Difference in SOPA.

Note: Values above represent unstandardized coefficients, with standard errors in parentheses. *p<.05, **p<.01, ***p<.001

The next section of this paper will first place the main findings of this paper in a broader context of the “Home Run Derby Curse.” It will then discuss possible avenues for further research.

Implications

The results above were mixed. In some instances, participation in the Derby, or success in the Derby, was statistically related to second half offensive decline, whereas in other tests, there was no relation between participation in the Home Run Derby and changes in offensive production between season halves. When using the full sample (N=7,330), the results showed that Derby participants can expect to see a greater drop in their XBPA between halves of the season than those who did not participate in the Derby. Moreover, those who have greater success in the Derby will see a greater drop in their XBPA between the first and second halves of the season in comparison to those who have not had as much success in the Derby. Further, the results showed, when using the full sample of players, those who participated in the Derby, as well as those who had greater success in the Derby, will, on average, expect to see their second half SOPA increase more than Derby non-participants.

These findings, however, must be discussed in closer detail. As McCollum and Jaiclin (2010) pointed out in their piece, some of these results may be due to the often extraordinary performances of Derby participants in the first half of the season, and any decline is simply a regression to the mean.

In order to address this issue, in testing the effect of Derby participation and success on change in XBPA, I restricted the sample to those who showed above-average and extraordinary performances in the first half of the season. The effect of Derby participation and success on change in XBPA disappeared when the sample was restricted to those who showed average or above-average first halves. This suggests that hypotheses 1.1 and 1.2 are not confirmed, and lends support to McCollum and Jaiclin’s regression to the mean conjecture.

Turning towards the relationship between Derby success and change in SOPA between halves, an effect was initially found. This suggests that those who hit more home runs during the Derby tend to see an increase in their second half SOPA in comparison to their first half SOPA. This relationship, however, evaporates when a measure of home runs squared in included. This suggests a lack of robustness to this finding, and thus hypothesis 2.2 cannot be confirmed.

Based upon these findings, it appears that the “Home Run Derby Curse” is more of a Home Run Derby myth. The results concerning Derby participation and SOPA, however, appear to tell a different story. The test of hypothesis 2.1 shows that those who participate in the Derby see a larger increase in their SOPA between halves of the season compared to players who do not participate in the Derby. As stated above, it is unnecessary to restrict the sample based upon first half SOPA because those who participate in the Derby have, on average, a higher first half SOPA than the full sample mean. Thus, the argument that Derby participants have had an exceptionally strong first half does not apply in the case of SOPA.

Simply put, derby participants do see a statistical increase in their SOPA in comparison to non-participants, suggesting that there is some credence to the “Home Run Derby Curse,” and, it is caused by players changing their swings. The question that remains, however, is what is the substantive impact of participation in the Derby on SOPA?

The Substantive Effect of Derby Participation on SOPA

Essentially, Derby participants can expect to see their second half SOPA increase by .005 more points over their first half SOPA than those players who do not participate in the Derby. The average first half SOPA for those who did not participate in the Derby is .1608855. The mean number of first half plate appearances for the sample used in this study, excluding those who participated in the Derby, is 249.5692. This means that the average Derby non-participant will strikeout about 41 times in the first half of the season.

With all variables in the model held equal, the average second half SOPA will be .006 points higher than the average first half SOPA, about .1668855. The mean number of second half plate appearances for the sample used in this study, excluding those who participated in the Home Run Derby, is 219.5873. Therefore, an average Derby non-participant will strikeout about 37 times in the second half of the season. When the first half and second half are combined, an average player who did not participate in the Home Run Derby can expect to strikeout about 78 times.

The mean first half SOPA for a Derby participant is .1669383. The average number of first half plate appearances for Derby participants is 356.9345. Thus, the average Derby participant can expect to strikeout about 60 times in the first half of the season.

The mean second half SOPA for a Derby participant can be understood as:

2^nd Half SOPA = .1669383+(-α)+(-β)

Where α is the intercept (-.006) and β (-.005) is the coefficient for participation in the Derby. All other variables held constant at 0, Derby participants can expect a second half SOPA of .1779383. The average number of second half plate appearances for Derby participants is 291.7209. With all variables other than “Derby participation” held equal at 0, those who participate in the Derby can expect about 52 strikeouts in the second half of the season. This suggests that a Derby participant can expect to strike out 112 times during the season.

This is a substantial difference in strikeouts, however, in order to accurately assess the true substantive effect of Derby participation, one must utilize a common number of plate appearances across both Derby participants and non-participants alike. For the purposes of this paper, I make the reasonable assumption that players will have about 300 plate appearances in the second half of the season.

Using 300 plate appearances, those who did not participate in the Derby can expect 50 strikeouts in the second half of the season, whereas those who did participate in the Derby can expect 53 strikeouts in the second half of the season.[xiv] This difference of three strikeouts does not seem substantively large.

Further, it must be noted that the coefficient for Derby participation of -.005 is only an estimate with a 95% confidence interval ranging from -.0002 to -.01. If the true coefficient is -.01, this would amount to about 5 more strikeouts over 300 plate appearances in the second half of the season. If the true coefficient is -.0002, a player who participates in the Derby could expect, all other things held equal, to strikeout 2 more times over 300 plate appearances in the second half of the season than a player who did not participate. In essence, the difference in SOPA between the halves of the season due to participation in the Derby is statistically significant, but substantively negligible.

Broader Implications and Future Research

Although the effects of Derby participation on SOPA are substantively minimal, the take away point of this study is that a “Home Run Derby Curse” does exist. Further, the confirmation of H2.1 suggests that Derby participants are altering their swings to develop more power during the Derby, and this is affecting their swing in the second half of the season.

Regardless of the substantive effects, this is an important finding. If a Derby participant’s swing is altered so greatly that they begin striking out at an even faster rate than non-participants in the second half of the season, the question that we must ask is, what other effects does this altered swing have? Does it increase a Derby participant’s flyball ratio? Are Derby participants more likely to see a drop in batting average and walks?

Beyond these questions, future research into the “Curse” should also focus on how the Derby alters a player’s swing. One possible avenue for future research lies in measuring changes in a hitter’s stance (i.e. distance between their feet, angle of their back elbow, etc.) after the Derby relative to a player’s stance prior to the Derby.

Works Cited:

 J.P. Breen, “The Home Run Derby Curse,” FanGraphs, July 11, 2012, accessible via http://www.fangraphs.com/blogs/the-home-run-derby-curse/.

Jason Catania, “Is there Really a 2^nd-Half MLB Home Run Derby Curse?,” Bleacher Report, July 15, 2013, accessible via http://bleacherreport.com/articles/1702620-is-there-really-a-second-half-mlb-home-run-derby-curse.

Derek Carty, “Do Hitters Decline After the Home Run Derby?,” Hardball Times, July 13, 2009, accessible via http://www.hardballtimes.com/do-hitters-decline-after-the-home-run-derby/.

Evan Kendall, “Does more power really mean more strikeouts?,” Beyond the Box Score, January 12, 2014, accessible via http://www.beyondtheboxscore.com/2014/1/12/5299086/home-run-strikeout-rate-correlation.

Tim Marchman, “Exploring the impact of the Home Run Derby on its participants,” Sports Illustrated, July 12, 2010, accessible via http://sportsillustrated.cnn.com/2010/writers/tim_marchman/07/12/hr.derby/.

Joseph McCollum, and Marcus Jaiclin. 2010. “Home Run Derby Curse: Fact or Fiction?,” Baseball Research Journal 39(2).

[i] It could be argued that those players who participate in the Derby are also exceptional players, and therefore, this conjecture will not be correct for the majority of those who participate in the Derby. At first glance, this would appear to create a problem for the analysis in this piece, however, this is not so. This would only present a problem if being exceptional led to a greater positive correlation between a power swing and strikeouts, that is if a power stroke for exceptional players leads to more strikeouts than a power stroke for average players. If exceptional hitters are less likely to have a positive correlation between power and strikeouts, and Derby participants are exceptional players, we would expect to see a lower strikeout rate among these players when they begin attempting to hit for greater power. Essentially, a violation of this assumption leads to a more conservative measurement.

[ii] Some players who participated in the Derby were coded as “0,” as they did not hit any home runs.

[iii] A player is coded as a 1 if he was traded during the season and a 0 if he was not traded.

[iv] A player is coded as a 1 if the difference in his plate appearances between the first and second halves of the season (Pre All-Star Break PAs – Post All-Star Break PAs) is greater than the observed average in the data (39.8). A player is coded as a 0 if the difference in his plate appearances between the first and second halves of the season is less than the observed average in the data.

[v] A player is coded as a 1 if the number of plate appearances he had during the first half of the season is greater than the observed average in the data (342).

[vi] This variable is a dummy variable, with a player being coded as a 1 if he spent the entire season in the National League, and a 0 if he did not spend the entire season in the National League.

[vii] Although the “Steroid Era” is somewhat difficult to nail down, for the purposes of this paper, it is assumed to run from 1990 through 2005. Therefore, if an observation is in or between 1990 and 2005 it is coded as a 1. If an observation falls outside of this time period it is coded as a 0.

[viii] For the purposes of this paper, the era of “greenies” is deemed to run from 1985 through 2005. Therefore, if an observation is in or between 1985 and 2005 it is coded as a 1. If an observation falls outside of this time period it is coded as a 0.

[ix] Interleague play began in 1995 and continues through present. Therefore, if an observation is in or between 1995 and 2013 it is coded as a 1. If an observation falls outside of this time period it is coded as a 0.

[x] The difference in XBPA variable maintains the same basic distribution when the sample is restricted to those with an XBPA equal to or greater than the league average (.0766589), as well as equal to or greater than the Derby participant average (.1138781).

[xi] The mean first half XBPA for those who participate in the Derby is .114, whereas the mean first half XBPA for those who do not participate in the Derby is .077.

[xii] When this model is run including a variable for “home runs squared” the results remain similar.

[xiii] When this model is run including a variable for “home runs squared” the results remain similar.

[xiv] One could quibble with the estimate of 300 second half plate appearances, however, it is important to note that a Derby participant’s second half strikeout total increases over a non-participants strikeout total by .5 for every 100 plate appearances. Thus, if one were to use 200 plate appearances, the difference in average strikeout totals between Derby participants and non-participants for the second half of the season would be about 2.5. Additionally, if one were to use 400 plate appearances, the difference in average strikeout totals between Derby participants and non-participants for the second half of the season would be about 3.5.

For every Dontrelle Willis–who continues to get looks from Major League teams despite over eight years of complete ineptitude–there exists a handful of other players who fade into relative obscurity only a year or two removed from a dominant season. All it generally takes is a down year resulting from–or paired with–an injury to send a guy spiraling below the radar. These are often the players that can return the most value during fantasy drafts if you can make the distinction between a year that’s an aberration, and one that is a bellwether for a significant, irreversible decline in skills.

While I can’t say with complete confidence that Curtis Granderson’s 2014 doesn’t fall into the latter category, there were a couple of encouraging things going on below the subpar surface stats that make me think he can return some solid value this year, especially considering where he’s going in most drafts.

Granderson was 33 last year and coming off an injury-shortened season. He was also trading a left-handed pull hitter’s haven in Yankee Stadium for the cavernous confines of Citi Field. All things considered, it was natural to expect some significant regression. And when he hit .136 through his first 100 at-bats of the season, it seemed like the Mets might have had a disaster of Jason Bay-like proportions on their hands.

Fortunately for them, Granderson managed to right the ship to an extent, putting together a couple of excellent months. His final line of .227/.326/.388–dragged further down by a nightmarish .037 ISO, 16-for-109 August–wasn’t spectacular by any stretch. But there were some nice takeaways buried in there.

For one, his bat speed doesn’t seem to have slowed enough to justify the statistical hits he took across the board. Despite seeing 56.3% fastballs–the most he’s seen since 2010 by a wide margin–his Z-Contact % of 85% was in line with his 85.8% career average, and not far removed from the league average of 87%. I suspect the uptick in fastballs resulted from opposing teams banking on an age-slowed swing, but Granderson’s contact rates on high velocity pitches in the zone didn’t suffer for it.

Granderson also set a career high in O-Contact % with a 62.7% rate. This could usually indicate a lack of plate discipline as much as it could a sustained bat speed, except that Granderson’s O-Swing % of 26.2% is roughly the average of what he did in the four years prior. He also managed to post the second-highest walk rate of his career (12.1%) and his lowest strikeout percentage since 2009 (21.6%). These are not particularly impressive rates in their own right, but in the context of Granderson’s career they do help to dispel the notion that last year was the beginning of the end for his hitting ability.

That is not to say, of course, that I foresee a return to the 40 home run, .260+ ISO form that he flashed in his early Yankee years–there’s no way he ever touches the absurd 22 HR/FB% that sustained that run. But with the right field fences at Citi Field moving in–a change that apparently would have resulted in 9 more home runs for Granderson had it been done last season–and some improvement on last year’s uncharacteristically bad .265 BABIP, I would not be at all surprised to see a home run total between 25 and 30 to go along with double-digit steals and a batting average that won’t kill you. And that has value when it is being drafted as low as Granderson currently is.

Rickie Weeks going to the Mariners moved a lot of eyebrows, raising some, furrowing others. Weeks’s deal will be worth $2 million for one year, according to Jim Bowden. To the casual fan, this move might seem a little unnecessary: Seattle already has a pretty good second baseman in Robinson Cano. If you take a closer look, however, there are some hidden metrics that would point to Weeks having a resurgence.

Let’s first look at this acquisition from the position of the casual fan. Weeks is coming off a 2014 season wherein he only had 286 plate appearances, and saw a substantial reduction in power. Long story short, Weeks was a singles hitter last year. In those 286 trips to the plate, Weeks had 41 singles. In 2013, Weeks had 42 singles in 113 more plate appearances. While this helped his overall batting average get back on track, from .209 in 2013 to .274 in 2014, it did nothing to increase his power numbers.

Weeks is also a below-average fielder. Scratch that, Weeks is the worst fielder at the second base position in all of baseball, and he has been for some time now. If we are going by FanGraphs’s UZR, Weeks has a career total UZR of -56.5 for his career. That puts Weeks right at the bottom as far as second baseman who have played more than 5,000 innings since 2005 (Week’s first full season). Below are the bottom five second baseman according to UZR in that same time frame. Recognize anyone?

Notice that current Seattle second baseman Robinson Cano is four from the bottom. This really doesn’t tell us anymore than that Seattle does not put a premium on defense, and we might have suspected this all along if we had first taken a look at team UZR from the last two seasons.

There we have it. A match made in heaven. It is no coincidence that two of the bottom five defensive teams over the last two years contained two of the bottom five defensive second basemen, in Cano and Weeks. So what does this all have to do with Seattle and their recent free-agent acquisition of Mr. Weeks?

Ceteris paribus. All other things being equal, meaning if we take defense out of the evaluation (because Seattle is not focusing on defense at the time), we can better understand what Seattle saw in this now 32-year-old utility man.

Our answers lie within the batted ball statistics. Over his career, Weeks has had a fly ball percentage of 35-36% consistently. Even in 2013 it was 32.7%. Last season that percentage sunk dramatically to a career low of 25%. This may or may not be a bad sign. We will come back to the fly ball percentage shortly. Now let us look at the HR/FB ratio statistic.

Last season Weeks saw a spike in his HR/FB ratio. It reached an all time high of 17.8%.  His career average for that metric is 14%. Knowing that his fly-ball percentage was at an all time low, with his HR/FB ratio at an all time high we can reasonably expect those two metrics to meet somewhere in the middle this upcoming season.

There is one last measurement we should look at in order to fully understand Weeks’s value possibility. Jeff Zimmerman and Bill Petti, of FanGraphs fame, run their own website, baseballheatmaps.com, where one can look at batted-ball distance for any player going back to 2007. When we look at Rickie Weeks, we see that he has a career average fly-ball distance of 292 feet. Last year, his average fly-ball distance was 285 feet. This slight decline is understandable due to the age factor. Weight this how you wish, but it doesn’t seem like Weeks is going through any more of a power decline than other professionals have gone through at his age.

Putting it all together, if Weeks starts to hit more fly balls, and (if nothing else) maintains his career average HR/FB ratio, the Mariners will reap the full value of his services. His defense is subpar at best, but Seattle does not seem too concerned about that. Right-handed power seems to be scarce at the moment, especially at the second base position. Rickie will add depth to Seattle, but the real value might come during the season when teams start looking for power to boost their playoff lineups—that is, if Weeks can deliver.

For most of his baseball career Xander Bogaerts has been an extremely successful player, whether it’s been in the low or high minors. He’s also always been a highly touted prospect, primarily praised for his ability to hit the baseball all the while playing reasonably good defense as a shortstop. It’s not simply Bogaerts’ ability to impact the baseball that made him such a touted prospect, but also his approach. In his brief stint in the majors, in 2013, Bogaerts was lauded for his impeccable plate discipline, especially in the playoffs. As he should have been; in 2013, Bogaerts had a 10% walk rate, and in the postseason it skyrocketed to 17.6%.

This, however, was in a small sample size; Bogaerts only had 50 plate appearances in the majors in 2013, and only 34 in the playoffs. In 2014, Bogaerts, got off to a very strong start. He didn’t hit for much power at the beginning of the year but he walked an awful lot and the power was slowly starting to creep up. Around the end of May, Bogaerts had close to a 400 OBP.

The wheels though fell off after that. Bogaerts simply stopped walking and hitting well. He basically struggled the rest of the year apart from September where he did show signs of improvement. Bogaerts’ failures though went almost side by side with his walk rate apart from the last month of the season where he did have a spike in BABIP. Below is a chart of Bogaerts’ walks per month displayed by Baseball Savant.

As you can see Bogaerts’ walks just took a huge dip after May. So what happened — why did Bogaerts just stop walking? Well there are a number of factors to consider here. First I think it’s important to look at how Bogaerts was pitched — did pitchers make a sudden adjustment? Below is a chart of hard, breaking, and off-speed pitches used against Bogaerts in 2014. Provided by Brooks Baseball.

What is primarily noticeable is that pitchers, as the year went on started throwing fewer hard pitches and more breaking pitches. This, however, only shows us that pitchers made an adjustment to Bogaerts it doesn’t show us the full story; it doesn’t show us how Bogaerts reacted to the adjustments the pitchers were making.

There are many factors that can indicate how a player reacts to pitching adjustments. We can look at his swing rate or his whiff rate but the question, which we are really trying to answer, is: did something change in the player’s approach? Below I think is the most accurate example of how Bogaerts changed his approach at the plate and why he started walking a lot less. It’s a chart provided by Brooks Baseball that examines a player’s aggressiveness and passiveness, essentially his plate approach on hard, breaking, and off-speed pitches.

As you can see Bogaerts early on in the year was a very patient hitter but as the season went on he became increasingly aggressive at the plate. In fact he didn’t just start getting more aggressive on one type of pitch, but rather in general. He went away from what made him successful on the outset and began his prolonged slump throughout the year. What can be rather alarming is that it wasn’t just a one or two-month spike in aggressiveness, but rather trend in increased aggressiveness throughout the year.

Xander Bogaerts is still a very young player — he’s going into his age 22 seasons and this breakdown by no means should be taken as a prediction for future failures. This is really just part of maturing as a young athlete, getting better and making adjustments. Bogaerts went through the highs and lows of a baseball season in 2014. While I don’t expect him to be a superstar next year, I do expect that we will see significant improvement in not just his stats but his approach at the plate. My advice to Bogaerts on this behalf would be to look back at what made him successful in the first two months of the 2014 season and try and replicate that. Don’t be so aggressive at the plate, just wait for your pitch and when you get it, put a good swing on it. This is of course an annoying cliché but I do think it applies in Bogaerts’ case.

This might be a scary thought to consider if you’re a Red Sox fan or if you’re a member of the Red Sox front office. Most of us including myself perceive Buchholz to be a really good pitcher who just happened to have a terrible year in 2014, which he did. Buchholz last year finished with a 5.34 ERA, which was the worst ERA of his career. His peripherals were better — that being said they were not great — his FIP was 4.01 and his xFIP was 4.04. The problem is that I think the Red Sox are banking or hoping that Buchholz has a bounce back year in 2015 and finds the “Cy Young” caliber form he displayed in 2013.

Buchholz in 2013 posted a great ERA at 1.74, and his FIP was really good too at 2.78. The problem was that he just couldn’t stay healthy throughout the year only pitching 108 innings. This has been a chronic problem for Buchholz throughout his career. Buchholz has never pitched 200 innings in a season; the most he’s ever pitched was in 2012, where he threw 189.1 innings. The other problem is that Buchholz performance has always been very volatile, one year he has a good ERA and then the next he has a bad ERA.

So let’s take a look at Buchholz’s underlying numbers to try and get a better understanding of his erratic performance records. First let’s look at 2013, where he conceptually had the best year of his career. He as I’ve already mentioned posted a great ERA though in a limited sample size. His BABIP that year was also very low at .254. What, however, was most alarming was his left on base percentage (LOB%). His LOB% in 2013 was at 83.7%, which is extremely high. A normal or league average LOB% is normally around 72%, Buchholz that year was well above that and quite frankly unsustainably high. Then if we look at his HR/FB ratio, at 4.5%, it’s also at an unsustainable rate. If you combine these factors, the low innings pitched, the BABIP, the HR/FB, and the LOB% for Buchhotlz great 2013 ERA, it’s easy to see why he had such a low ERA and why that probably was just a fluke.

Now let’s look at his 2010 season, which was considered by most to be his breakout year. In 2010 as you might have guessed Buchholz had a great ERA at 2.33, which probably gave the impression that he had a great year. His FIP however was at 3.61, which is above average, but his xFIP was at 4.07. His BABIP and HR/FB were also at unsustainable rates that year. His BABIP was .261 and his HR/FB ratio was at 5.6% creating his low ERA.

Finally let’s take a look at his 2012 season, the year where he pitched the most innings of his career, 189.1. I think this is the year that best describes Buchholz’s true value and not the skewed value of his 2010, 2013, and 2014 seasons (If you’re wondering what about 2011? Well he only pitched 82.2 innings so I pretty much discounted that year from the analysis). In 2012, Buchholz finished with a 4.56 ERA, his FIP was 4.65, and his xFIP was 4.43. These are not good numbers, in fact they’re well below-average numbers. This is a scary scene especially if you’re the Red Sox and hoping that Buchholz will bounce back. The essential problem is that the only year where Buchholz’s ERA lined up perfectly with his peripherals, the numbers weren’t pretty. His LOB% granted was a little bit low at 69.7% but his BABIP was also low at .283.

This is not a welcoming sight. I’m not actually certain that Buchholz was ever that good of a pitcher or rather a top of the rotation starter. It for the most part seems that his good years or good ERAs were merely a product of low BABIPs and good luck. It’s also not like he’s ever been a pitcher with a high strikeout to walk rate. His career K/9 is at 6.88 and his BB/9 is at 3.33. His K/9 and BB/9 have also always remained around his career average, with not much fluctuation, so it’s not like he’s been trending upward in that regard. Who I really think got Buchholz’s value just about right is Steamer. In 2015 Steamer projects him to finish with a 4.19 ERA and a 4.06 FIP, which is right around where I think Buchholz true talent and value lies. For those who think Buchholz has the great potential of a top of the line starter, well I’m sorry but at this point I just don’t see it; I think he’s probably more of a mid to back of the rotation starter. A useful piece in the rotation but definitely not someone who you should count on.

The 15-team LABR Mixed Draft is the most exciting of the expert fantasy drafts each year. Amateur fantasy owners from all over the globe tune into the live spreadsheet broadcast and debate each one furiously on social media.

Most of these amateurs are looking for expert guidance to help them in their own draft. They see a player getting drafted well above their ADP and they often move the player up on their own personal big board.

I do not think this is the best way to approach and absorb the most information out of LABR. When one expert reaches on a pick, we have no idea if there is a consensus. It could have been just one expert making a stand on a player he himself feels strongly about, or there could have been several owners who felt the same way about that player. We just don’t know.

What we do know is that when certain players drop well below their public rankings, there is an agreement of pessimism. That is the information that could be significant for the rest us. Every owner in the league letting a player fall well below their ADP is the expert consensus we should be looking for.

Here’s a quick look at nine players who the experts are cool on.

Read the rest of this entry »

Set forth below is a description of a short-hand stat designed to measure how well a batter hits when he hits a fair ball.  I call it “Contact Quality” or “CQ.”

Obviously, BABIP is the most common measure of hitting balls-in-play.  But BABIP excludes home runs, and doesn’t otherwise distinguish between singles and extra-base hits.  One possible approach would be to compute slugging average on fair balls (“fair balls” refers balls-in-play plus home runs).  However, the weights used in slugging average do not accurately reflect the value of different types of base hits.

CQ uses the weights from wOBA, in a simplified fashion, before the adjustment to normalize the scale to OBP.  Rounding the weights, I assign 0.8 for a single, 1.1 for a double, 1.4 for a triple, and 1.7 for a home run.  Or, in other words, 0.5 for a hit and 0.3 per total base.

So, the formula is:  (0.5*H+0.3*TB)/(AB-K)

Interestingly, the average CQ in 2014 was .312, as compared to an average OBP of .314.

CQ is intended to be a simple stat that, together with K% and BB%, gives a pretty good profile of any batter.

If a batter’s CQ is above .400, he is considered a member of Mensa (contact quality division).  In 2014, among qualified batters, the following were Mensa members:

1. Giancarlo Stanton (.453)
2. Mike Trout (.450)
3. Jose Abreu (.435)
4. Chris Carter (.407)
5. Justin Upton (.405)
6. Andrew McCutchen (.404)
7. Matt Kemp (.403)

Rounding out the top 10:

8. Miguel Cabrera (.388)
9. Anthony Rizzo (.387)
10. Marlon Byrd (.386)

If you told me, at the end of 2013 (5.19 ERA) that Phil Hughes would have a chance to win a Cy Young, I would’ve told you, no way. If you told me in 2012 (4.23 ERA) that Phil Hughes had a chance to win a Cy Young, I would’ve said it was highly improbable. If you told me in 2011 (5.79 ERA) that Phil Hughes had a chance to win a Cy Young, I would’ve told you to get out of my face; the guy would be lucky to be in the starting rotation (the Yankees’ starting rotation). That’s because for a large part of his career Phil Hughes was a terrible starting pitcher. Not a bad starter, a terrible starter. He was actually, over the last three years, before 2014, one of the worst starting pitchers in baseball.

2014, though, was a different story. In the 2013-14 offseason Hughes signed a 3-year, 24-million-dollar contract with the Minnesota Twins. That year Hughes had one of the best seasons in all of baseball, and needless to say the best season of his career. Just how good was Hughes in 2014? Well Hughes pitched a career high 209.2 innings. His ERA was moderately good at 3.52 but he had the sixth-best FIP in all of baseball at 2.65. The only pitchers to have a better FIP in 2014 were Garrett Richards, Chris Sale, Felix Hernandez, Corey Kluber, and Clayton Kershaw. Of those five only Hernandez and Kluber pitched more innings than Hughes.

Hughes finished with an above-average K/9 that year at 7.98 but he led all of baseball with .69 BB/9. Phil Hughes’ BB/9 in 2014 was one of the greatest BB/9 of all time (37^th all time), in fact the last pitcher to have a BB/9 better than Hughes was Carlos Silva, also of the Twins in 2005 at .43. Hughes’ great peripherals allowed him to finish with a 6.1 fWAR, which was tied as the fourth-best fWAR in all of baseball (not including position players). Only Kluber, Kershaw, and Hernandez had a better fWAR than Hughes in 2014.

The 2014 season, however, was a complete anomaly for Hughes. For most of his career he’s been awful. So how can one determine if he’ll be a Cy Young candidate in 2015? First I think it’s important to consider one’s BABIP. This after all could have just been a fluky BABIP year. It, however, was not. Hughes was actually unlucky by BABIP standards at .324. BABIPs though can be inflated when a pitcher gets a lot of groundballs, but Hughes does not, his GB% is at 36.5, which is below average. Hughes has predominantly been a fly-ball pitcher so I do expect his BABIP to normalize somewhat next year.

Then I think it’s important to see if Hughes made some adjustments to his repertoire and his pitching style. In the table below, provided by Brooks Baseball you can see Hughes’ pitch usage since he’s entered the big leagues.

It seems the important element to observe here is Hughes has always thrown a ton of fourseam fastballs and that clearly has not changed. What has changed, however, is his use of sliders, cutters, sinkers, and changeups. Hughes essentially completely rebuilt his repertoire in 2014, apart from his curveball and fourseam fastball. Hughes abandoned the slider and changeup and basically added a sinker. He also re-started using a cutter, a pitch he basically or barely used over the past two years. Now, it’s the second-most-used pitch in his repertoire.

I don’t know who in the Twins organization told Hughes to re-start throwing a cutter, but he probably deserves a raise (unless Hughes decided to throw it all on his own of course). Hughes throws his cutter at an average speed of 89.2 mph. He threw the pitch a total of 509 times and the wRC+ against it was only 71. It also netted an IFFB% of 23.4 and a GB% of 46.4.

This of course was not the only adjustment made by Hughes in 2014. Hughes was never someone who walked a lot of hitters but a .69 BB/9 is extremely low. Below is Phil Hughes’ heat map for 2014.

As you can see, Hughes basically decided to throw the ball down the middle. Is this a good strategy? To be honest I’m not 100% percent sure but it sure did work for Hughes in 2014 and it’s definitely efficient. This strategy obviously isn’t conducive to a lot of walks and it won’t tire a pitcher out. Plus when one considers the low scoring environment, throwing a pitch right down Broadway may not be such a bad idea. It’s also not like Hughes is throwing a ton of off speed pitches down the middle, most of the pitches he throws are fastball and cutters, in fact more than 80% of them are. Which makes his success all that more impressive.

This strategy may be devised to fit Hughes’ new environment and more specifically his new ballpark. Hughes as I’ve already mentioned is a fly ball pitcher, his FB% last year was 40.2%, which was the 15^th highest FB% in the majors. When Hughes was pitching for the Yankees he was pitching in a stadium that gave up a lot of home runs. In 2014 Yankee Stadium yielded the third-most home runs in the majors, after Great American Ball Park (Cincinnati) and Coors Field (Colorado). Giving up a lot of fly balls in a home-run-heavy ballpark is typically not a good mix. In 2012 Hughes’ HR/FB ratio was 12.4%, well above average and in 2013 it was 11.1%. The Twins stadium (Target Field), however, is not conducive to home runs, in 2014 it ranked 23^rd in home runs allowed. Phil Hughes’ HR/FB ratio dropped to 6.2%.

So is this going to translate into another great 2015 season? Well one thing is certain, Hughes is staying put and so he will play most of his games in Target Field, which should keep his numbers down (when I say down that’s a good thing). However, there’s no way of being 100% sure or accurate on this and Steamer does project a 3.89 ERA with a 3.90 FIP. Hughes, though, I think has a very good chance of repeating his success due to his pitching adjustments and new pitching approach. Plus Hughes’ high BABIP of 2014 should normalize somewhat. Maybe next year he’ll have a low BABIP and his numbers will look even better, netting him a Cy Young. Who knows?

I think a lot of people have a hard time believing in one year of success and with good reason, for all we know it could just be a blip on the radar. That being said, pitchers, sometimes, just figure something out; sometimes things just click. Maybe they invent a new pitch or maybe they re-discover an old one, like Hughes. Sometimes they even change their entire approach to pitching and find success in the latter years of their career, like Cliff Lee. Phil Hughes could very well be that guy and he definitely wouldn’t be someone I would write off in 2015.

As most of you already know Alex Cobb is a pitcher for the Tampa Bay Rays. He is projected to be their “ace” in the upcoming season. In fact many people would argue that he’s the best pitcher in the AL East. Considering Masahiro Tanaka’s health issues it doesn’t seem unreasonable to assume that this might be true. Cobb, however, wasn’t always considered a top of the rotation starter. Since arriving to the majors, Cobb has made significant adjustments to his pitching style, which has earned him his current reputation.

The Rays drafted Cobb in the 3rd round in the 2006 Major League draft. After spending a few years in the minors, in 2011, Cobb made his Major League debut. He pitched 52.2 innings that year and finished with a relatively good ERA, 3.42. 2012, was essentially his first real full season in the big leagues and he didn’t do so well. Cobb, that year finished with a 4.03 ERA. His peripheral numbers, however, were relatively good, his FIP being 3.67 and his xFIP being 3.54.

The next two years Cobb became an extremely dominant pitcher. In 2013, he finished with the best ERA of his career at 2.76 and in 2014 his ERA was only slightly worse at 2.87, but still stellar. His FIP and xFIP, however, have remained consistently in the mid to low 3s. Over the past two years mostly in the low 3s. Many of us have a good understanding of FIP and understand that Cobb’s recent ERA production may just be a normalization of FIP. This may very well be true and an important element to consider, however Cobb in the past two years Cobb has made significant pitching adjustments, which may indicate that this recent ERA success is no fluke.

So what type of adjustments has Cobb made? Well thanks to Brooks Baseball PITCHf/x tool we have a sample size of Cobb’s pitch usage leading back to 2010. If you look at the graph below one thing truly stands out in Cobb’s pitch mix.

Cobb as you can see in the graph above has four essential pitches. What is most notable is how Cobb has made use of his fastball and sinker or inverted their use. In 2010 his fastball was one of the pitches he used the most. In 2013, however, he made a significant decision to use the fastball a lot less. It’s actually the pitch he’s started using the least. Cobb now mostly throws his off-speed pitches and his sinker. The sinker now is basically the pitch he’s using most frequently. Both pitches are predominantly thrown at the same speed, around 92 mph according to Brooks Baseball. The biggest difference is that the sinker has more movement or vertical movement while the fourseam fastball does not.

In the chart below is an example of the vertical movement on Cobb’s pitches. Why vertical movement? Because Cobb throws a splitter, a curveball, and a sinker, which are all conducive to vertical movement.

The sinker or movement on a sinker can be more favorable in creating a higher groundball percentage. It, however, has not been the case with Cobb; his groundball percentage has always remained around his career average of 56.5%. One of the more drastic differences in Cobb’s results in correlation with his new pitching technique is his whiffs per swing.

As you can see here, at the same time that Cobb started to decrease his use of fastballs, he started getting more and more swings and misses with the pitch. The splitter and the curveball have also been pitches which have induced more swing and misses due to his new pitch mix. What one can primarily take away from this chart is that hitters now seem to either be sitting on his sinker or sitting on a pitch with movement (the sinker would fall into that category). Cobb’s fourseam fastball is really the only pitch that doesn’t have significant movement and yet it’s getting a ton of swing and misses even though it’s being thrown a lot less. Meaning that hitters are probably expecting a pitch with movement and when the fastball is thrown they are either surprised or not prepared to hit the pitch.

Cobb here has transformed one of his weakest pitches into one of his strongest. Many young pitchers, and veterans for that matter rely a ton on their fastballs, yet it might be a drastic mistake. If you don’t have an overpowering fastball, like Cobb’s, a good pitching strategy could be to stop throwing it very often. There is no one way to pitch, just like there is no one way to get wins or be successful. Most pitchers are taught at an early age that everything derives off their fastball; “you need to establish your fastball early in the count to set up your off-speed pitches”. This is not true; if you want you can throw off-speed pitches early and then throw fastballs or not throw fastballs at all. There are no rules to dictate the way one pitches and Cobb is exploiting that.

Cobb’s success can serve as a template for younger pitchers, in the minors or majors who do not throw 95+ mph and are trying to compete in this hard-throwing era. It actually can also serve as a formula for pitchers who are getting older and are losing their fastball velocity. Cobb is a very good and unique pitcher, and should be someone who pitchers try and emulate.