The Best and Worst Four-Seam Fastballs of 2013
Introduction
What is the best pitch of all-time? Is it Mariano Rivera’s cutter? Is it Randy Johnson’s slider? Is it Walter Johnson’s fastball? I do not know. What I do know is that this question is nearly impossible to answer, so let’s simplify things a little. What was the best pitch thrown during the 2013 regular season? On a rate basis, PITCHf/x would lead us to believe that the best pitch thrown by a qualifying pitcher was Yovani Gallardo’s cutter with a wFC/c of 4.95. In other words, for every 100 cutters thrown by Gallardo, he saved 4.95 runs above a pitcher who throws an “average” cutter. What does this really mean though? This system of calculation is based off the changes in run expectancy due to the outcome of each pitch, which is extremely complicated and tedious to calculate. I felt that there had to be a simpler way to quantify the quality of a pitch.
Background
Back in August, I posted an article entitled “Baseball’s Most Extreme Pitches from Starters, So Far” that posited the idea of total bases per hit allowed. In other words, I wanted to look at who was getting hit the hardest. Now, it was rightly suggested in the comments that this wasn’t the greatest way to determine a pitch’s quality. For example, let’s look at the following two extremely hypothetical examples. One pitcher throws his fastball exactly 100 times. In those 100 pitches, he throws 99 of them for strikes. On the 100^{th} pitch, he gives up a home run. Now, by looking at TB/H, this pitch has a rating of 4.00, which is the worst possible rating. However, he only gave up 0.04 total bases per pitch, which is excellent. By comparison, the second pitcher throws exactly 100 fastballs as well. He gives up 100 singles. By TB/H, his fastball has a rating of 1.00, which is significantly better than the first pitcher. However, he gave up 1.00 total bases per pitch, which is awful. If a pitcher gave up a base runner each time he threw a pitch, he probably would cease throwing that pitch very quickly.
That got me to thinking that total bases per pitch may be a much better way to determine the quality of a pitch, but there are also glaring problems with this method as well. For example, 100 balls thrown in 100 pitches would a value of 0.00 total bases per pitch. Clearly, a pitcher’s ability (or inability) to throw a pitch for a strike needed to be incorporated as well.
Proposed Solution
To try and solve the problems suggested above, I propose the following simple formula:
adjTB/P = [1B + 2*2B + 3*3B + 4*HR + xBB] / Pitches
where,
xBB = Balls/4
With that said, I know some pitches are thrown out of the strike zone intentionally (i.e. the waste pitch). At the end of the day, a waste pitch only puts you one step closer to walking a batter and adds one pitch to the pitch count. Every coach would prefer their starter to throw a Maddux each time out, so efficiency is the name of the game. In order to test this formula, let’s look at a sample calculation.
According to Baseball Prospectus and their PITCHf/x leaderboards, A.J. Burnett threw 614 four-seam fastballs this regular season. On those 614 pitches, he allowed 10 singles, nine doubles, five home runs, and had 202 of those pitches called balls. Burnett allowed 58 total bases and 50.5 xBB. Doing some quick arithmetic, he allowed 0.1767 adjTB/P.
At first glance, I’m sure your reaction is similar to my initial reaction. Okay, so what does that mean? On its face, a correct response may contain the words “I’m not really sure”. If we look at the summation of each four-seam fastball thrown by starters this year, we find that the league allowed 0.1800 adjTB/P, so A.J. Burnett threw a slightly above average four-seam fastball this year. To come to that conclusion though, you’d have to know both a player’s rate and the league rate. We can present this information in a much nicer and easier to understand way.
To do this, I decided to turn to the old standby from every scout in baseball, the 20-80 scale. As you’re probably well aware, the 20-80 scale attempts to rate a player’s skills numerically. 50 is average. 60 represents exactly one standard deviation above average. 30 represents exactly two standard deviations below average, and so on and so forth. By taking the weighted standard deviation of the data set, we can determine how many standard deviations above or below average a certain pitch is. Looking at the full season data, the weighted standard deviation for four-seam fastballs is 0.0262 adjTB/P. Another quick calculation tells us that A.J. Burnett rated as 0.13 standard deviations above average. Converting that on a 20-80 scale rating, Burnett’s four-seam fastball gets a rating of 51. On quick glance, the 51 rating makes much more sense than 0.1767 adjTB/P, which helps solve one of our problems.
Results
Now that we understand how to calculate the values and what they mean, let’s look at a scale for whose four-seam fastball really excelled and whose really was problematic. To qualify for the full season, 600 total four-seam fastballs had to be thrown. This gave me 103 qualified starting pitchers. The Top 10 qualified starters were:
Rank |
Pitcher |
Rating |
1 |
Lance Lynn |
66 |
2 |
Anibal Sanchez |
65 |
3 |
Matt Harvey |
65 |
4 |
Zack Greinke |
65 |
5 |
Jonathon Niese |
62 |
6 |
Hector Santiago |
62 |
7 |
Bartolo Colon |
62 |
8 |
Madison Bumgarner |
62 |
9 |
Clayton Kershaw |
61 |
10 |
C.J. Wilson |
60 |
For comparison, the Bottom 10 qualified starters were:
Rank |
Pitcher |
Rating |
94 |
Ervin Santana |
43 |
95 |
Ricky Nolasco |
42 |
96 |
Jeremy Hellickson |
42 |
97 |
Jason Vargas |
40 |
98 |
Scott Diamond |
40 |
99 |
Tim Lincecum |
37 |
100 |
John Danks |
35 |
101 |
Josh Johnson |
35 |
102 |
Tom Koehler |
34 |
103 |
Justin Grimm |
31 |
On a monthly basis, a minimum of 100 four-seam fastballs had to be thrown. The best and worst pitches each month this season were:
Month |
Pitcher |
Rating |
Month |
Pitcher |
Rating |
March-April |
Anibal Sanchez |
66 |
March-April |
Brett Myers |
23 |
May |
Jose Quintana |
67 |
May |
Burch Smith |
23 |
June |
Tim Hudson |
65 |
June |
Dylan Axelrod |
30 |
July |
Anibal Sanchez |
71 |
July |
Justin Grimm |
24 |
August |
Rick Porcello |
66 |
August |
Andre Rienzo |
20 |
September |
Lance Lynn |
68 |
September |
John Danks |
22 |
Only three starters qualified as above average in each month of the regular season. Their monthly ratings are shown below. No starter qualified as below average in each month this season.
Pitcher |
March-April |
May |
June |
July |
August |
September |
C.J. Wilson |
53 |
51 |
61 |
57 |
64 |
55 |
Clayton Kershaw |
56 |
56 |
52 |
58 |
65 |
60 |
Lance Lynn |
63 |
62 |
58 |
55 |
53 |
68 |
I plan to continue this study by analyzing both other pitch types and relievers. Baseball Prospectus provides data for the following pitches: four-seam fastball, sinker, cutter, splitter, changeup, curveball, slider, screwball, and knuckleball. At the completion of all the pitch types, I’ll post the ratings for complete repertoires as well. If well-received, I’ll try and provide monthly updates as next season rolls along.
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The A.J. Burnett math:
10 singles, nine doubles, five home runs = 48 total bases (not 58)
Typo on my part. Supposed to be 20 singles rather than 10. The final values (adjTB/P, Rating) are still correct. Thanks for pointing this out.
It took a bit of thinking but I think I know why Bartolo Colon’s four-seamer rated ahead of Clayton Kershaw’s. I think it has to do with use when ahead and behind in the count. According to Brooks Baseball, the four-seam fastball is rarely used by Colon when behind in the count, when results are worse, and is used much more ahead in the count, especially against lefties, when results are best. Kershaw uses his four-seam fastball as his main pitch when behind in the count over 80% of the time against both left and right-handed batters.
That probably has a fair amount to do with it. Sequencing isn’t considered here. I was looking for something that was fairly quick and easy to calculate and didn’t require any information rather than just raw data totals.
Also, Kershaw threw roughly 2.5x more four-seamers than Colon. Their ratings are roughly 0.5 apart if you don’t round (61.78 vs. 61.22). Looking at the big picture, their really isn’t any difference here. To use a really simple example, is batting .325 over 500 ABs really more impressive than batting .320 over 650 ABs? Essentially same level of excellence over a larger sample size. Player B may actually be the better hitter, but Player A still wins the batting title.
I say all that to say that the ratings themselves shouldn’t be taken in a vacuum. It may have been prudent on my part to include the number of pitches each person threw. I appreciate your comments. I’m still trying to figure out what’s the best way to present this information or even if it’s worth presenting at all.
Great article. However, I feel like the effectiveness of a pitcher’s 4 seam fastball is very dependent on his other off speed pitches and when he throws his fastball in the count. I know this is asking a lot, but is there anyway that pitch sequencing and such can be incorporated into evaluating a pitch?
Then again, perhaps I’m overthinking it,
While I definitely agree that the effectiveness is influenced by sequencing to a certain degree, I was attempting to quantify pitches “in a vacuum” if you will. The PITCHf/x ratings that can be found on FanGraphs (wFA/c for example) do take into account the sequencing, count, etc. Admittedly, the way I’ve presented is much a simpler way of evaluating a pitch. I know this isn’t the answer you were probably hoping for. I’ll work on it some more and see what I can come up with.