Now that Chris Sale’s historic strikeout streak has ended, it seems an appropriate time to marvel at the dominance Sale has shown. Over an eight-game stretch from May 23 through June 30, Sale had the following line:

I’ve included BABIP to show that Sale was not on some incredibly low BABIP streak. League BABIP this season currently sits at .297. Sale’s career BABIP is .286. Without showing the normal indications for extreme luck, Chris Sale turned opposing lineups into a bunch of light-hitting middle infielders. For comparison, below is the season-to-date line for Indians SS Jose Ramirez.

The fact that the White Sox were only 4-4 in those eight games speaks to how badly the White Sox have played during 2015. Of those eight starts, six occurred during the month of June. It is these starts I’d like to focus on through the lens of pitch values relative to the rest of the league. I’ve posted some previous work on pitch values. The framework for the calculations can be found here. I’ve made some tweaks to the calculations, mainly to allow for player specific Balls/BB and Strikes/K to be calculated and league specific adjustments rather than MLB-wide constants.

According to Brooks Baseball, Sale threw 694 pitches during the month of June with a pitch mix of 45% Four-seam Fastball, 25% Changeup, 22% Slider, and 8% Sinker. The sinker was clearly Sale’s worst pitch in June. With the 55 sinkers thrown, Sale managed to give up six hits (11%). With the other 639 pitches, he gave up 21 hits (3%). So just how good were his other pitches? Chris Sale accumulated 2.9 WAR by my calculations over the month of June. He accumulated 0.0 of that from his sinker. For reference, here is the top ten in WAR from the month of June.

Sale lapped the field no matter which calculation you look at. Now, let’s take a look at a slightly adjusted version of that top-ten list.

We can split Sale’s June into two separate pitchers, and both “Sales” were top-ten in Pitch Value WAR accumulated. The Chris Sale that threw nothing but fastballs (Four-seam Fastballs and Sinkers) was the best pitcher in baseball in June. The Chris Sale without a fastball (Sliders and Changeups) was the ninth-best pitcher in baseball. With that said, we can dig a little deeper into the value of each of his pitches. First, let’s look at Sale compared to other four-seam fastballs. The table below gives the top five most valuable four-seam fastballs as well as a pitch rating based on June data set to a 20-80 scale.

Since WAR is a counting stat, there are two components to accumulating a high total. First, you have to throw a lot of the specified pitch type. Pitchers that threw 300 four-seam fastballs almost always accumulate more PV-WAR than those who only threw 30. Secondly, the pitch has to be of certain quality. Throwing 1000 of the world’s worst four-seam fastballs isn’t nearly as valuable as throwing 100 of the world’s best four-seam fastballs. In June, nine pitchers threw more four-seam fastballs than Chris Sale. No one that threw at least 90 total four-seam fastballs threw a better four-seam fastball than Sale. In fact, Sale’s four-seam fastball was the third highest rated qualifying pitch in June. To qualify, the pitch had to be thrown more than average for that pitch type. For example, if there were 4,000 curveballs thrown in June by 40 total pitchers, anyone who threw more than 100 curveballs would qualify. Moving on, Sale’s second most used pitch was his changeup. Here’s the table for changeups.

Once again, Sale tops the PV-WAR rankings. He was “slacking” on his quality on his changeup though. His changeup only ranked 14^th out of 126 qualifying changeups, so I guess there’s always room for improvement. The last pitch of interest for Sale is the slider. It’s the pitch I most associate with Sale, but it’s only his third most used pitch. Below is the corresponding table for sliders.

I think a trend is developing. Chris Sale threw the most valuable slider in June as well. By rating, Sale finished fourth out of 129 qualifying sliders. The three pitchers above him in rating (Andrew Miller, Mark Lowe, Darren O’Day) are all relievers. To sum up, in June, Chris Sale had three pitches make up over 90% of his pitch mix. All three of those pitches were the most valuable pitch in their respective pitch types in June. He had the overall best rated four-seam fastball and the best slider thrown by a starter. His changeup was the “worst” of his three pitches and was still a top 15 rated changeup. Chris Sale was completely dominant in June. His nearest competitor for most valuable pitcher in June only accumulated a little more than half of Sale’s value. Who knows when we may see a pitcher in this much of a groove again? For curiosity’s sake, I’ve included a table with the most valuable and highest rated of each pitch type for your perusal. The overall highest rated pitch was Andrew Miller’s slider.

Introduction

A couple months back, I introduced a new method of calculating pitch values using a FIP-based WAR methodology.  That post details the basic framework of these calculations and  can be found here .  The May and June updates can be found here and here respectively.  This post is simply the July 2014 update of the same data.  What follows is predominantly data-heavy but should still provide useful talking points for discussion.  Let’s dive in and see what we can find.  Please note that the same caveats apply as previous months.  We’re at the mercy of pitch classification.  I’m sure your favorite pitcher doesn’t throw that pitch that has been rated as incredibly below average, but we have to go off of the data that is available.  Also, Baseball Prospectus’s PitchF/x leaderboards list only nine pitches (Four-Seam Fastball, Sinker, Cutter, Splitter, Curveball, Slider, Changeup, Screwball, and Knuckleball).  Anything that may be classified outside of these categories is not included.  Also, anything classified as a “slow curve” is not included in Baseball Prospectus’s curveball data.

Constants

Before we begin, we must first update the constants used in calculation for Jule.  As a refresher, we need three different constants for calculation: strikes per strikeout, balls per walk, and a FIP constant to bring the values onto the right scale.  We will tackle them each individually.

First, let’s discuss the strikeout constant.  In July, there were 47,449 strikes thrown by starting pitchers.  Of these 47,449 strikes, 4,585 were turned into hits and 13,750 outs were recorded.  Of these 13,750 outs, 3,725 were converted via the strikeout, leaving us with 10,025 ball-in-play outs.  10,025 ball-in-play strikes and 4,585 hits sum to 14,610 balls-in-play.  Subtracting 14,610 balls-in-play from our original 47,449 strikes leaves us with 32,839 strikes to distribute over our 3,725 strikeouts.  That’s a ratio of 8.82 strikes per strikeout.  This is exactly the same as our from 8.82 strikes per strikeout in June.

The next two constants are much easier to ascertain.  In July, there were 26,244 balls thrown by starters and 1,328 walked batters.  That’s a ratio of 19.76 balls per walk, up from 19.36 balls per walk in June.  This data would suggest that hitters were slightly less likely to walk in July than previously.  The FIP subtotal for all pitches in July was 0.52.  The MLB Run Average for July was 4.17, meaning our FIP constant for May is 3.65.

The following table details how the constants have changed month-to-month.

Pitch Values – July 2014

For reference, the following table details the FIP for each pitch type in the month of July.

As we can see, only three pitches would be classified as below average for the month of July: sinkers, cutters, and changeups.  Four-Seam Fastballs and curveballs also came in right around league average.  Pitchers that were able to stand out in these categories tended to have better overall months than pitchers who excelled at the other pitches.  Now, let’s proceed to the data for the month of July.

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Pitch Ratings – July 2014

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Monthly Discussion

As we can see, Cole Hamels takes the top for this month due to the  strength of his overall repertoire.  Hamels was classified as throwing five different pitches in July (Four-Seam, Sinker, Cutter, Curveball, and Changeup) and managed to earn at least 0.1 WAR from all five.  The most valuable pitch overall in July was Ian Kennedy’s Four-Seam Fastball.  The least valuable was Drew Hutchison’s Changeup.  As far as offspeed pitches, Garrett Richards’s 0.5 WAR from his slider lead the way.  The least valuable fastball was Hector Noesi’s cutter.

On our 20-80 scale pitch ratings, the highest rated qualifying pitch was Chase Whitley’s changeup.  The lowest rated pitches were the changeups thrown by Drew Hutchison and Mike Minor, Hector Noesi’s cutter, and Josh Beckett’s curveball.  The highest rated fastball was Drew Hutchison’s four-seam fastball.

Pitch Values – 2014 Season

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Year-to-Date Discussion

If we look at the year-to-date numbers, AL FIP and MLB WAR leader Felix Hernandez still sits in the top spot.  Current MLB FIP leader Clayton Kershaw ranks ninth.  The least valuable starter has been Marco Estrada.  On a per-pitch basis, the most valuable pitch has been Ian Kennedy’s four-seam fastball.  The most valuable offspeed pitch has been Garrett Richards’s slider.  The least valuable pitch has been Marco Estrada’s four-seam fastball.  The least value offspeed pitch has been Marco Estrada’s changeup.  Needless to say, it’s been a rough year for Marco.  Qualitatively, I feel fairly encouraged by the year-to-date results so far.  The leaderboard is topped by two no-doubt aces, both of whom currently in the top two in their respect leagues in FIP, and Marco Estrada comes in at the bottom after posting the highest FIP among qualified starters so far.  For reference, the top five in the year-to-date overall rankings are currently 1st, 12th, 10th, 2nd, and 9th on the FanGraphs WAR leaderboards respectively.

Introduction

A few weeks back, I introduced a new method of calculating pitch values using a FIP-based WAR methodology.  That post details the basic framework of these calculations and  can be found here.  This post is simply the May 2014 update of the same data.  What follows is predominantly data-heavy but should still provide useful talking points for discussion.  Let’s dive in and see what we can find.  Please note that the same caveats apply as last month.  We’re at the mercy of pitch classification.  I’m sure your favorite pitcher doesn’t throw that pitch that has been rated as incredibly below average, but we have to go off of the data that is available.  Also, Baseball Prospectus’s PitchF/x leaderboards list only nine pitches (Four-Seam Fastball, Sinker, Cutter, Splitter, Curveball, Slider, Changeup, Screwball, and Knuckleball).  Anything that may be classified outside of these categories is not included.  Also, anything classified as a “slow curve” (here’s looking at you, Yu) is not included in Baseball Prospectus’s curveball data.

Constants

Before we begin, we must first update the constants used in calculation for May.  As a refresher, we need three different constants for calculation: strikes per strikeout, balls per walk, and a FIP constant to bring the values onto the right scale.  We will tackle them each individually.

First, let’s discuss the strikeout constant.  In May, there were 52,100 strikes thrown by starting pitchers.  Of these 52,100 strikes, 5,005 were turned into hits and 15,110 outs were recorded.  Of these 15,110 outs, 4,058 were converted via the strikeout, leaving us with 11,052 ball-in-play outs.  11,052 ball-in-play strikes and 5,005 hits sum to 16,057 balls-in-play.  Subtracting 16,057 balls-in-play from our original 52,100 strikes leaves us with 36,043 strikes to distribute over our 4,058 strikeouts.  That’s a ratio of 8.88 strikes per strikeout.  This is up from 8.47 strikes per strikeout in March and April.  Hitters were slightly harder to strikeout in May that the previous two months.

The next two constants are much easier to ascertain.  In May, there were 29,567 balls thrown by starters and 1,575 walked batters.  That’s a ratio of 18.77 balls per walk, up from 18.50 balls per walk in March and April.  This data would suggest that hitters were slightly less likely to walk in May than previously.  The FIP subtotal for all pitches in May was 0.75.  The MLB Run Average for May was 4.32, meaning our FIP constant for May is 3.58.

Pitch Values – May 2014

For reference, the following table details the FIP for each pitch type in the month of May.

As we can see, only two pitches would be classified as below average for the month of May: four-seam fastballs and changeups.  Sinkers also came in right around league average.  Pitchers that were able to stand out in these categories tended to have better overall months than pitchers who excelled at the other pitches.  Now, let’s proceed to the data for the month of May.

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Pitch Ratings – May 2014

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Monthly Discussion

As we can see, Felix Hernandez ascended to the throne for this month riding the overall quality of his entire repertoire.  Hernandez was classified as throwing five different pitches in May (Four-Seam, Sinker, Curveball, Slider, and Changeup) and managed to earn at least 0.1 WAR in each category.  His best two pitches were his Sinker (0.4 WAR) and Changeup (0.3 WAR).  The most valuable pitch overall in May was the Four-Seam Fastball thrown by Phil Hughes.  The least valuable was Marco Estrada’s changeup.  As far as offspeed pitches, R.A. Dickey’s 0.6 WAR from his knuckleball lead the way.  Excluding Dickey’s knuckleball due to the sheer number of times it was thrown, the most valuable offspeed pitch was Jason Hammel’s slider.  The least valuable fastball was Edinson Volquez’s sinker.

On our 20-80 scale pitch ratings, the highest rated qualifying pitch was Yu Darvish’s slider.  Unsurprisingly, the lowest rated was Marco Estrada’s changeup.  It’s difficult to generate -0.7 WAR with a single pitch unless it was just awful.  The highest rated fastball Jake Odorizzi’s splitter, and the lowest rated fastball was Edinson Volquez’s sinker.

Pitch Values – 2014 Season

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Year-to-Date Discussion

If we look at the year-to-date numbers, Felix Hernandez still sits in the top spot.  Current AL and NL FIP leaders Corey Kluber and Aaron Harang rank third and fourth respectively.  The least valuable starter has been Wandy Rodriguez.  On a per-pitch basis, the most valuable pitch has been R.A. Dickey’s knuckleball, which should be the case for much of the season due to the heavy pitch totals.  Other than Dickey, the most valuable pitch has been Ian Kennedy’s four-seam fastball.  I guess there’s something to the idea of throwing a lot of fastballs in an extreme pitcher’s park after all.  The most valuable offspeed pitch has been Jose Fernandez’s curveball.  The fact that he still tops this list even after being injured and missing starts is simply astounding.  Get healthly Jose, we all miss your brilliance.  The least valuable pitch has been Marco Estrada’s changeup.  The least value fastball has been Mike Minor’s four-seam.  Qualitatively, I feel fairly encouraged by the year-to-date results so far.  The leaderboard is topped by two no-doubt aces, with the current FIP leaders coming in right behind them.  For reference, the top five in the year-to-date overall rankings are currently 1st, 6th, 2nd, 14th, and 22nd on the FanGraphs WAR leaderboards respectively.  Please feel free to provide feedback in the comments section.

Introduction

The baseball pitcher is one of the few positions in all of team sports where a supreme talent can pretty much individually win a team a game.  A great hitter can be pitched around or not pitched to at all.  A quarterback is only as successful as his offensive line and defense allow him to be.  A great scorer in basketball can be double-, triple-, or quadruple-teamed (e.g. the Stephen Curry defense in the 2009 NCAA Tournament) as necessary.  Pitching is the only team sports endeavor where someone can complete a perfect game.  The term doesn’t even exist in other team sports.  You can roll a perfect game at the bowling alley.  You can play a Golden Set in tennis, but you can’t score a perfect game in basketball.  Even if you hit every shot you take, you could have always taken one more.  The closest thing in other team sports to the influence a dominate pitcher has is a goalie in hockey, but a goalie can only post a shutout.  Pitchers can post shutouts and not be perfect.  I imagine the same is true for goalies.  It is this unique aspect of pitching that makes it the most interesting position in all of sports to me.

For a few years, I’ve attempted multiple times to come up with a new way of determining the value a pitcher provides.  Even further than that, I’ve searched for a way to evaluate individual pitches in a pitcher’s repertoire.  FanGraphs provides linear weight values for pitch types, and they can be useful in determining the quality of a pitch.  The problem exists in understanding the numbers.  Sure, we know that, in the linear weight analysis of pitches, the greater value is the better pitch.  What exactly does a pitch being 15 runs above average really mean though?  One of the major tenets of sabermetric pitching analysis is that runs allowed in strongly influenced by the defense, and thus out of a pitcher’s control.  Why are we discussing pitch values as run values then?  The currency of baseball is the win.  When we talk about Mike Trout being a 10 win player or Clayton Kershaw being a six win pitcher, its not hard to explain to someone what that means.  Shouldn’t we then talk about pitch values in terms of wins as well?

Methodology

The first step in determining the win value for a pitcher per FanGraphs WAR methodology is to determine the pitcher’s FIP.  For reference, here’s the formula for FIP.

FIP = ((13*HR)+(3*(BB+HBP))-(2*K))/IP+constant

In the formula, the constant is used to bring the value equivalent to league average ERA.  For the purposes of WAR, an adjustment is made to convert league average ERA into league average RA.  The constant used in this scenario will therefore adjust each pitcher’s FIP to league RA.  No adjustment will be made for individual leagues.  With that said, we must determine each of the six variables.

1.) Home Runs Allowed

This is the easiest factor to determine.  The fine folks over at Baseball Prospectus provide PitchF/x leaderboards for each of nine pitch types.  One of the things they tabulate is home runs allowed on a given pitch.  That simplifies our task a little.

2.) Walks Allowed

Once again, we can consult the PitchF/x leaderboards for this information, though not directly.  Yes, the PitchF/x leaderboards provide a column for walks, but this is the number of walks that ended with a certain pitch.  For our purposes, we’ll need to calculate expected walks based on the frequency of called balls.  Luckily, there’s a column for called balls to help us out.  Now, how should we proceed?  Well, maybe since four balls equal one walk we should just divide this number by four.  This isn’t correct though because even the best pitchers will only through something like 70% strikes.  In a 100 pitch outing, the divide by four methodology would yield 7.5 walks each game, which is only slightly ridiculous.  Clearly, the number we are looking for is not four.  According to FanGraphs, there were 28,172 balls thrown and 1,523 walks allowed by starting pitchers in March and April.  That’s a ratio of 18.50 balls per walk.  Back to our 30% example, that equates to 1.6 walks per 100 pitches, which is much more realistic.  For our purposes, one walk will be credited for each 18.50 balls thrown by a pitcher.

3.) Hit-by-Pitch

I have been unable to find any data from which to determine how many times a pitcher hit a batter with a certain pitch.  HBP is normally a small factor in the overall FIP equation though, so we will just assume zero hit-by-pitches.

4.) Strikeouts

Similar to the section on walks allowed, we cannot simply take the number of strikeouts tabulated in the PitchF/x leaderboards, as this is the number of strike threes on a certain pitch.  Once again, we have a tabulated value for called strikes, but we cannot simply divide by three.  First, swinging strikes need to be included.   Foul balls need to be included as well because they can count as strikes.  For our total strikes thrown, we can start by using Called Strikes + Swinging Strikes + Foul Balls.  Swinging strikes can be calculated by multiplying Whiff/Swing x Swing Rate x Pitches Thrown.  Foul balls can be calculated by multiplying Fouls/Swing x Swing Rate x Pitches Thrown.  Now, we need to determine the number by which to divide our total strikes.  Well, in March and April, 49,293 strikes were thrown by starters to record 4,057 strikeouts.  That’s a ratio of 12.15 strikes thrown per strikeout.  Should we use 12.15?  No, we shouldn’t because hits and batted ball outs are included in the 49,293 strikes thrown.  Starters allowed 4,647 hits in March and April.  They also pitched 4,780.2 innings, which converts to 14,342 outs.  If we subtract out strikeouts from the outs recorded, we’re left with 10,285 batted ball outs.  If we subtract, 10,285 and 4,647 from 49,293, we’re left with 34,361 strikes left.  34,361 strikes and 4,057 strikeouts is a ratio of 8.47 strikes per strikeout.  This is the divisor for which we were looking.

One thing we need to consider more closely though for our raw strike total is foul balls.  Some pitchers, such as Phil Hughes, have many more pitches fouled off than others.  This shouldn’t be used to arbitrarily increase a pitcher’s expected strikeout total.  To combat this, a pitcher’s foul rate on a pitch is compared to the league’s foul rate on that same pitch.  This is done by dividing the league rate by the pitcher’s rate.  Pitchers with less than average foul rates have all of their foul balls included.  For pitcher’s with higher than average foul rates, the foul ball total is reduced to the number of expected foul balls at the league average rate.

5.) Innings Pitched

In order to estimate the number of innings pitched with a certain pitch, we must first determine the number of total pitches each pitcher threw per inning.  By dividing the total number of pitches thrown by the total number of innings pitched, we are able to determine for each pitcher how many pitches were required on average to complete an inning.  By dividing the number of each individual pitch thrown by this ratio, we can estimate the number of innings thrown using a certain pitch.  For example, there were 77,465 total pitches thrown by starters in March and April.  Dividing this by our 4,780.2 IP from earlier gives us an average value of 16.20 pitches per inning.  If a pitcher had thrown his curveball 100 times, we would estimate he would have thrown 6.17 innings with his curveball.  Rather than using the league average value, the Pitch/Inning ratio is calculated for each individual pitcher.

6.) Constant

Using all of the starters pitches thrown in March and April tabulated by Baseball Prospectus, the league FIP subtotal calculates as 0.61.  League average RA for March and April for starters was 4.29.  For March and April, our FIP constant is 3.68.

After calculating the FIP for each pitch, we can then use park factors to make the numbers park neutral and run the FIP value through the FanGraph WAR methodology to get a win value for each pitch.  The pitcher’s overall win total is the sum of the individual pitch types.  Please note that the player win totals will most likely not match the standard win total calculated by FanGraphs.  This is because only nine pitch types are tabulated by Baseball Prospectus: four-seam fastball, sinker, cutter, splitter, curveball, slider, changeup, screwball, and knuckleball.  Pitches that could be classified as slow curves or eephus pitches are not included by Baseball Prospectus in their curveball leaderboards.  Any unclassifiable pitch is also not included.  Also, there is always inherent issues with pitch classification.  For example, Baseball Prospectus classified one C.J. Wilson pitch as a knuckleball.  Now, I find it incredibly hard to believe that Wilson decided to break out exactly one knuckleball over the first five weeks of the season having never been known to throw one before.  Simply put, we are at the mercy of the pitch classification system.

Results

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Pitch Ratings

One of the only issues with WAR is that it is a counting stat, so we’re very much tied to playing time (or in this case, number of pitches thrown).  It can also be useful to study, on a rate basis, the quality of a pitch.  Using the park adjusted FIP values used in the WAR calculations above, we can provide 20-80 scale values for each pitch based on the number of standard deviations above or below an average pitch.  The baseline used is the overall average pitch, not the average within a pitch type.  In other words, Jose Fernandez’s curveball will be evaluated against all pitches analyzed, rather than just other curveballs.  Only qualified pitches are shown.  To qualify, a pitcher had to throw an above average number of each pitch.  That is the pitch count had to exceed the total number of pitches within a pitch type divided by the total number of pitchers in a pitch type.

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Discussion

Well, there we have it.  By valuing his pitches, Nathan Eovaldi was the most valuable pitcher in MLB in March and April, and Wandy Rodriguez was the least valuable.  The most valuable pitch was Eovaldi’s four-seam fastball.  The most valuable offspeed pitch was Jose Fernandez’s curveball.   The least valuable pitch was Danny Salazar’s slider.  The least valuable fastball was Johnny Cueto’s cutter.

By ratings, the best overall pitch was Jose Fernandez’s curveball.  The worst overall pitch was a three way tie between Tim Lincecum’s slider and the changeups thrown by Bruce Chen and Matt Cain.  The best fastball was Dallas Keuchel’s sinker.  The worst fastball was Johnny Cueto’s cutter.

I feel this is the strongest iteration yet in my attempts to value individual pitches.  Hopefully, you agree.  Of course, similar analysis could be done on relievers.  I would expect more extremes, especially in the 20-80 ratings from relievers.

DISCLAIMER: I know certain players are more likely to outperform/underperform the league-average BABIP based on their specific player profiles. This is just a fun exercise to consider if everyone’s “luck” was the same.

With that disclaimer out of the way, I began wondering who the best/worst hitters are in baseball if batted-ball luck didn’t figure into the equation. We hear analysis frequently about Player X who’s having a breakout year, and the refrain is consistently that he is having better luck on batted balls than he had been having in the past. For example, BABIP was one of the main reasons cited for how Chris Johnson batted .321 in 2013 after hitting .268 over the previous two seasons. Many people look for fantasy sleepers based on a much lower than normal BABIP. The effects of BABIP are undeniably real and have been well documented. If we take BABIP out of the equation though, who rises to the top?

Before we get to the results, let me go over my methodology. It’s extremely simple, and you can probably guess how this is done. If you don’t want the boring details, please skip ahead. The first step to these calculations was to keep all factors not included in the BABIP formula constant. Each player still hits the same number of home runs. Each player still walks at the same rate. Each player still strikes out the same amount. The only component that changes is hits that don’t leave the yard (1B, 2B, 3B). I took the denominator of the BABIP equation for each player (AB-K-HR+SF) and multiplied it by the league-average BABIP (.297). This gives us the number of non-HR hits a player would have tallied if luck was removed. To get the number of singles, doubles, and triples each player hit, I took the ratio of Actual Hit Type/Actual Total non-HR and multiplied by expected non-HR. For example, Mike Trout hit 115 singles, 39 doubles, and 9 triples in 2013. That means that 70.6% of his non-HRs were singles, 23.9% were doubles, and 5.5% were triples. When adjusted for BABIP, we would expect Trout to hit roughly 129 non-HRs this past season. Multiplying 129 by the component percentages gives us roughly 91 singles, 31 doubles, and 7 triples. Everything else remains the same.

Batting Average Leaders

To answer the question posed in the introduction, we can look at many different stats. We already discussed how much an effect BABIP can have on a batting average, so maybe we should start there. For what it’s worth, the MLB leader in BABIP in 2013 was Chris Johnson at .394, and the worst BABIP belonged to Darwin Barney at .222.

Looking at those tables, the first thing that jumps out to me is that only two players (Edwin Encarnacion and Miguel Cabrera) in all of Major League Baseball would have hit .300 last year if luck is removed. The American League seems to possess better luck-independent hitters as the NL “batting champ” would have finished tied for fifth in the AL. Also, if Andrelton Simmons could actually hit .291 each season, he’d be an MVP candidate. I also find it interesting to look at which players benefited and suffered the most from their respective BABIPs.

On-Base Percentage Leaders

Perhaps we shouldn’t limit ourselves to just simply batting average. Isn’t it more important to avoid outs that it is to just get hits? Let’s look at the OBP results.

Once again, only two hitters (Encarnacion and Cabrera) would have reached based at a .400 clip. A trend is definitely starting to emerge. The gap between the AL and the NL is much less pronounced here though. As for the biggest changes in the MLB, consider the following tables.

As you might expect, these tables don’t look all that much different from the batting average change tables. Other than some reordering, the only difference here sees Allen Craig replace Mike Trout on the most negative change table.

On-Base + Slugging Leaders

Getting on base a lot is a promising start, but you win baseball games by scoring runs. What hitters were best at driving the ball while avoiding outs? Let’s look at the OPS results.

Once again, our Top 2 are Encarnacion and Cabrera, with a considerably gap between Cabrera and third place Chris Davis. The AL/NL split is at its most pronounced here. To see if our trend in the biggest changes tables continues, consider the following tables.

The trend continues as expected. Also, the negative regressers are harder hit than the positive regression candidates.

Weighted Runs Created Plus Leaders

This is FanGraphs though, so we can’t simply look at traditional stats. We need something that’s park-adjusted and comparative to league average. Let’s look at wRC+. (NOTE: These numbers aren’t adjusted for individual leagues as is normally done with wRC+. I’m lazy and didn’t take the time to do that extra step, so the wRC+ values won’t make up exactly with what is listed elsewhere on this site.)

As you might expect, Encarnacion and Cabrera top the charts again. Paul Goldschmidt is once again the National League leader. As for the biggest movers, they look very similar as well as you might expect.

Since we looked at the leaders in each category, let’s look at those who failed to meet such lofty standards in 2013.

Batting Average Laggards

The most startling thing I notice from these tables is that Dan Uggla gained .042 points in his batting average and still finished last in the league. Now, that’s impressive.

On-Base Percentage Laggards

Michael Bourn is our only carryover from the batting average tables that appears on the OBP tables as well. Probably not a great sign for Cleveland.

On-Base + Slugging Laggards

Uh-oh, Bourn is back again, and the only player relatively close to him is Adeiny Hechavarria. Hechavarria is a fine defensive shortstop who has noted offensive woes. Bourn was a big free agent signing for Cleveland expected to jump start the Indians offense. Those represent completely different expectations.

Weighted Runs Created Plus Laggards

Nothing here is meant to be used to draw conclusions about any hitters. I’m not advocating for Edwin Encarnacion as the best regular in baseball or Starlin Castro as the worst. I just thought this would be an interesting simple exercise to consider. Just for fun though, let’s look at the AL MVP race one more (“luck-independent”) time.

If we take luck out of the debate, Cabrera is an 8% better hitter compared to league average than is Trout. I guess the BBWAA doesn’t think Trout is an 8% better fielder and base runner than Cabrera. Surely they know what they’re talking about though. I mean they do get to decide who belongs in the Hall of Fame after all. They’re the smartest baseball folks out there.

I’m writing this as a response to Dave Cameron’s two articles on December 19 and 20 concerning the Hall of Fame.  While I completely understand the point Dave is/was trying to make in both pieces, I felt that his methodology was slightly flawed and perhaps deserved a fresh look.  As mentioned multiple times in the comments section on both articles, the data he used included players that were elected via the Veterans Committee.  Also included were players elected by the Negro Leagues Committee.  The purpose of this post is to look at players elected strictly by the BBWAA.  That list includes 112 inductees, the most recent of which being Barry Larkin.

Using the data Dave listed in his follow-up article that limits the player pool to either 5000 PA or 2000 IP, we get the following results:

If you combine all the data, you get 112 elected players out of 1068 “eligible” players.  That works out to 10.5% of the eligible population being inducted.  If we remove the 1961-1970 births, it’s 110 elected out of 908 eligible, or 12.1%.  If we try and bring the 1961-1970 total up to the overall average, that would mean ~17 inductees.  To reach pre-1961 levels, we need ~19 inductees.  To reach the lowest percentage of induction, we need a total of ~12 inductees.  To reach the highest percentage, we need a total of ~36 inductees.  I think it is safe to assume that, with the scrutiny given by Hall voters to the Steroid Era, the possibility of 36 inductees is nearly zero.

Dave also listed six players that he felt would surely get inducted in the coming years.  That list included Greg Maddux, Ken Griffey Jr., Randy Johnson, Mariano Rivera, Tom Glavine, and Craig Biggio.  If we include those six with the two already elected from the era (Barry Larkin and Roberto Alomar), the Hall would only need to elect four more members from the era to reach the current lowest standard.  I would think that John Smoltz has a pretty persuasive case for the Hall of Fame as well, being the only pitcher with 200 wins and 150 saves.  Also, Smoltz is one of the 16 members of the 3000 Strikeout Club.  That list includes 10 current Hall of Famers (all elected by BBWAA).  The other members not currently inducted include Smoltz, Roger Clemens, Randy Johnson, Curt Schilling, Pedro Martinez, and Greg Maddux.  Dave already included Johnson and Maddux on his list of “should be in” Hall of Famers.  Martinez was born in 1971, so he isn’t included in this discussion.  That leaves Smoltz, Schilling, and Clemens.  Clemens’ story doesn’t need to be rehashed at this point, and Schilling received 38.8% of the vote on his first ballot last year.  Also, simply looking at traditional stats, you have to think Frank Thomas has a strong case as well (521 HR, .301 BA).

Another point I wanted to bring up involves the ages of the players elected by the BBWAA.  The average age of a player elected is 49.7 years, with the median age being 48.  The data gets skewed a bit by pre-1900s players (as the first election wasn’t until 1936) and by extremely young inductees like Lou Gehrig, Roberto Clemente, and Sandy Koufax .  Gehrig was elected by a special ballot the year he retired after being diagnosed with ALS.  Clemente was elected a year after his death.  Both were elected before the five-year retirement period required for most players elapsed.  Koufax only played 11 years in the MLB, a remarkably short time for a Hall of Famer.

If we use the ~50 year average age of election though, anyone born in 1964 or after still “has a decent chance” at election.  If we figure an even distribution of eligible players born each year between 1961-1970, that means 60% of eligible players, or 96, still can make a case.  That becomes 90 if we take out Maddux, Glavine, Griffey, Rivera, Johnson, and Biggio.  As I stated earlier, they only need to elect four more to reach previously seen levels of induction.  4/90 is only 4.4% needed.  That list of 90 players also doesn’t include still eligible players such as Don Mattingly, Roger Clemens, Edgar Martinez, Fred McGriff, and Mark McGwire.

I’m not trying to take a stand on either side of the PED Hall of Fame discussion.  I’m just trying to point out that maybe the Hall of Fame isn’t being so much more strenuous on eligible players as they’ve been throughout history.  Just something to think about.

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:

For comparison, the Bottom 10 qualified starters were: