When I saw that Clayton Kershaw signed a seven-year contract with an average salary of over $30 million per season, my first thought was: that’s definitely going to be an albatross contract. In my mind, anything over $20 million per season has always seemed to be that threshold where a player no longer has a realistic chance of performing to the value of the contract. But, I am smart enough to know that what is in my mind is not always the same as what is in reality, so this brief post will look at every player during the 2013 MLB season that collected a salary of $20 million or more. My goal was to get a rough idea of exactly how many players either outperformed, adequately performed, or underperformed their salary.
I collected data from the website baseballplayersalaries.com. In the table below, I’ve reported the names, teams, and estimated salaries of each $20 million plus player in the 2013 MLB season. I’ve also reported the percent of their team’s payroll each player received and the percent contribution that player made to the team’s on-field performance.
Table 1: Players with $20 million or greater salaries for the 2013 MLB Season
The first thing I noticed was the number of players that underperformed their salary — 13 of 18. That’s just over 72%!
When it comes to players that I consider underperformed, there are too many to list. So, instead I’ll list the players who I consider adequately performed to their salary for the team they were on: Joe Mauer, Miguel Cabrera, Adrian Gonzalez, and Justin Verlander. That’s only 4 of 18, or about 22%. I did not include Cliff Lee in the list because he clearly outperformed his salary based on this measure. He was the only one of 18 players to do so. That translates to only 6% of players with $20 million plus salaries outperforming.
Even more staggering was when I calculated the average percent of team payroll an individual player on my list made, and compared it to the average percent of team on-field performance. The average player on the list made 14% of their team’s payroll, but only contributed to 5% of their team’s performance.
I realize that the criteria I have used is limited in many ways. For example, players on a poor-performing team (ex. Cliff Lee) will have a higher percent of on-field team performance, and vice versa. Or players on a team with a low total payroll will have a higher percentage of team payroll. However, I feel these numbers are so overwhelmingly lopsided that I’m not sure if you would be able to find any objective criteria that would show an opposite trend.
Given that these high-paid players consistently underperform their salary, an entire new set of questions arise. Why are teams still so willing to hand out these contracts? Do underperforming ‘star’ players really generate enough additional team revenue to justify their cost? What would happen if a large-market team properly valued their players?
With the precedent set by the Kershaw contract, maybe in the not-so-distant future $30 million will be the new $20 million, but as of the 2013 season a $20-million salary almost guarantees the player will not be getting the short end of the stick on that deal (in terms of performance at least).
I’d like to share some of my thoughts and research on how we evaluate Major League Baseball pitchers. I think for the most part when we use statistics to discuss a pitcher, we are really looking at the pitcher from one or more of the following three perspectives: 1) ability, 2) performance, and 3) contribution. Before I get into my research, I will take a moment to describe what I mean by each of the three terms.
When I use the word ‘ability’, I am describing the physical and mental skills the pitcher has at his disposal. Some examples of ability are: how hard he can throw, what kind of movement he has on his pitches, how well he can locate, how well he mixes his pitches, etc. With the introduction of PitchFX, we are now capable of measuring ability better than ever before. With that being said, it is still difficult to accurately and meaningfully quantify many aspects of ability. Since a pitcher’s performance is based at least in part upon his ability, performance statistics can sometimes be used as a substitute for direct ability measures.
Performance literally describes how well a pitcher performed. In other words, it refers to the outcome or outcomes resulting from that pitcher throwing pitches. Nearly all baseball statistics describe performance. Some statistics measure a pitcher’s individual performance fairly well, whereas others combine the pitchers performance with the performance of his team and other factors. For example, ERA is generally not considered a great measure of a pitcher’s individual performance; however, FIP is considered a better measure of individual performance.
I have not found much reference to the word ‘contribution’ in the baseball literature, but I do think it is an important concept to consider. Contribution is a word I use to describe a pitcher’s contribution in helping his team win baseball games. By this general definition, I suppose ERA (and other performance measures) could also be considered a contribution measure in some respects, since wins are related to runs allowed. Therefore, I also propose that the relationship between ability, performance, and contribution is not divided by solid lines but is instead a spectrum where each statistic can be considered somewhat a part of each category. However, in an attempt to clear up this somewhat murky discussion, I will offer stats such as W-L, WAR and WPA as the most obvious contribution stats*.
*Note: Contribution stats can be measured directly (ie. W-L) or derived from performance stats (ie. fangraphs WAR is derived from FIP).
Now on to my research… The hypothesis that drove this work was: pitcher ability measures are more consistent between seasons than performance or contribution. This hypothesis is based on my belief that unlike performance and contribution, which are affected by countless outside factors, a pitcher’s ability is within himself and therefore less likely to dramatically change between seasons.
To test this, I took each pitcher that pitched a minimum of 120 innings in each season from 2008-2011. This gave me a pool of 63 pitchers.
For my ability measure, I took the statistic whiff/swing. I like this measure of ability because to me it is the simplest measure of an isolated part of a pitcher’s ability. Since the batter has already decided he will swing, we are only looking at the pitcher’s ability to throw a ball that will evade a hitter’s bat. I know ability to hit the ball is also heavily dependent on the hitter’s ability, but I think that using pitchers that pitched 120 innings in each season will let me take the individual batter out of the equation and use this as a measure of pitcher ability.
For my performance measures I used ERA and FIP from FanGraphs. I agree ERA is not the best performance measure, and may be considered more of a contribution; however, I have included it nonetheless. Finally, for my contribution measure I decided to use FanGraphs WAR.
I calculated the average whiff/swing, ERA, FIP, and WAR for each pitcher of the four-year period. I also calculated the standard deviation within each pitcher for each stat and the within pitcher coefficient of variation (stdev/avg). Coefficient of variation is the best way to report the variability of each statistic over the four seasons because it effectively normalizes each stat by the units they are reported in.
Globally, over the four-season period the 63 pitchers in my group had an average:
whiff/swing = 0.205
ERA = 4.03
FIP = 3.97
WAR = 3.08.
The average within pitcher coefficient of variation was:
9.6% for whiff/swing
18.5% for ERA
12.0% for FIP
and 47.7% for WAR.
So what does this mean? Well, I know this is just a start, but based on this I believe my hypothesis was correct. A pitcher’s ability is much more consistent between seasons than their performance and/or contribution. Furthermore, performance is more consistent than contribution. It appears as though the further you get from pure ability measures the more difficult it will be to accurately/reliably predict a pitcher’s future performance and contribution. I’d like to do some further research on performance prediction to confirm this but, my guess is that trying to predict future WAR from past WAR will be extremely difficult. Perhaps predicting future WAR from past ability measures may prove to be more effective.
Stated in as simplest terms as possible, the goal of pitching is to get batters out without allowing runs to score. There are three ways any given pitch can get a batter out. A pitch can either be swung on and missed, taken for a called strike, or batted in such a way that the batted ball does not result in the runner reaching base. Batted balls involve the defence and are therefore less directly related to the pitch’s effectiveness at getting outs. That leaves us with swinging strikes and called strikes as the two best ways to measure a pitch’s effectiveness.
In Part I of my research on curveballs, I looked at what makes a curveball effective from a swinging strike perspective. I used an outcome variable that I like to call: ratio of effectiveness. Ratio of effectiveness is simply a ratio between swinging strikes and home runs hit. In Part II of my research, I will look at the effectiveness of curveballs from a called strike perspective. This work will aim to answer two basic questions: 1) are curveballs taken for strikes more often than fastballs? And 2) what are the characteristics of curveballs most often taken for strikes?
Are curveballs taken for strikes more often than fastballs?
Read the rest of this entry »
The curveball is often used as an ‘out’ pitch. This implies either it is difficult to hit or is often taken for a called strike. I was interested in exploring both of those possibilities, and as such, I have decided to present research addressing both. Part I, presented below, addresses the questions of how difficult the curveball is to hit and what makes it difficult to hit.
Earlier this week, I shared some research about the relative importance of velocity, location, and movement with respects to major league fastballs. The approaches I used to answer the curveball problem were very similar to the approaches I described previously. Again, I used the 2011 MLB season as my dataset, and included only pitches to right handed batters. Since curveballs are thrown far less frequently than fastballs, this time I included both right and left handed pitchers to increase my sample size. Another reason I wanted to include lefties is I wanted to know if the direction of the horizontal break mattered.
Is a curveball more difficult to hit than a fastball?
Velocity, location, and movement are all unquestionably important when we try and compare ‘good’ pitches to ‘bad’ pitches. My particular interest lies in how important each are. I’ve often wondered if a 98 mph cutting fastball can be thrown right down the middle and still have little chance of being hit for a home run? Or conversely, is an 88 mph fastball that’s straight as an arrow still likely to get a swinging strike if it paints the bottom outside corner of the zone?