HardHit/9: Statcast's Best Hard Contact Metric | pitcher list (2023)

In World Series Spiel 5,Tyler Glasnowhad a rough day. Of the 13 balls in play he allowed, 11 were hit at 95 mph or harder. The Dodgers' at-bats were so loud that MLB.com's Mike Petriello did some research and found that Glasnow's tough day in terms of hard hit rate was actually the worst postseason performance ever in the Statcast era.

Highest hard hit percentage allowed, 2015-20 postseason, by a pitcher with at least 10 balls batted in a game. (There were 404 such games.)pic.twitter.com/J0ux5HtySS

— Mike Petriello (@mike_petriello)26. October 2020

As Statcast defines, HardHit% is defined as the percentage of balls in the game that are hit at 95 mph or more. And it matters at all because 95 mph is the start when the uphill wOBA trend starts - that's when exit speed actually starts to matter. When we speak of this as a percentage, we pretend that the only "whole" sample that counts is the balls in play.

That's why I felt something bad when I saw Petriello's tweet. Of the six other names on the list, three were from pitchers known for their flashy strikeout totals. Glasnow himself batted out seven to five innings. Was he pitching poorly, or just knocking people out instead of inducing weak groundballs?

Instead of focusing on one game, I decided to take a closer look at Glasnows 2020. And I decided to compare him to someone I consider his polar opposite:Dallas Keuchel.

2020 Hart getroffener BIP: Glasnow vs. Keuchel

playerTyler GlasnowDallas Keuchel
Hard-hit balls in play5362
Baseball Events125198
hard hit%(percentile)42,4 %(24.)31,3 %(90.)
Total batters faced238257
Hard Hit/TBF(percentile)22,3 %(79.)24,1 %(55.)
innings pitched57.163.1

It only takes one bad statistic to tell the wrong story. Despite Keuchel's reputation for squelching hard contact, he actually gave up more hard-hit balls both per batter and per inning than Glasnow.

If you're not familiar with HardHit/TBF or HardHit/9, that's because Statcast doesn't show them. But you should be: HardHit/9 is more meaningful than any traditional hard contact statistic while also reaching confidence thresholds faster than BB/9.

One of the best and easiest tools for evaluating contact suppression had been hiding under our noses the whole time.

What is the problem with HardHit%?

Let's start with a look at Glasnow's Statcast "sliders" for 2020.


HardHit/9: Statcast's Best Hard Contact Metric | pitcher list (1)

The bottom row - all speed and spin - are on a per pitch basis. Whiff% is per swing. And all expected stats and K% are per PA. And the three "Contact" stats - Barrel %, Exit Velocity, and Hard Hit % - are the only stats scaled to balls in play.

Barrel rate in particular is an odd case. Barrels/BBE% is displayed on the player pages. But Baseball Savant also lists Barrels/PA% in its Exit Velocity & Barrels Leaderboard, making it the default sort. The Pro PA version is not shown anywhere else on the entire site.

The difference between these two stats is somewhat similar to the difference between breath rate and swing rate. A pitch with a good whiff rate might be hard to hit, but that might not be a good thing—many curveballs with tremendous whiff rates end up far below the zone too often, leaving pitchers behind in counts. But a pitch with a high swing strike rate is usually successful in both convincing batters to swing and inducing puffs. That makes it the preferred statistic in almost all cases: it tells more of the story.

Data is only as good as what we ask of it. And in the event of a hard-hit rate, Statcast urges us to ask the wrong questions. Both hard hit rate and barrel rate — two of the most cited Statcast contact stats — cause us to think about contact management solely in terms of the amount of weak contact that pitchers give up. If we try to compare two pitchers using these stats, the pitcher who hits more batters - arguably the most efficient way to quell hard contact - will be at a disadvantage. And that leaves a big gap in both our descriptive and predictive toolkits.

What should we ask?

Data can answer two questions: What happened and what will happen? And it's very rare that one number helps answer both.

Descriptive statistics answer the first question. And among them, FIP has been among the best for over a decade. It attempts to gauge what a pitcher's past ERA should have been by cutting out the noise of team defense and keeping only what it thinks a pitcher can control: strikeouts, walks, and home runs. It bakes in some noise - park factors affect home runs, for example - but its strong correlation with ERA makes it extremely useful for spotting pitchers whose results don't quite match their inputs.

But as much as the Red Sox pitchers of 2020 have tried to show us otherwise, home runs aren't exactly common occurrences. The FanGraphs statistical library tells us that a pitcher's home run rate only after 1,320 batters is more likely to predict his future home run rate than just using the league average. This is the reasoning behind xFIP replacing home runs with flyballs and assuming a league average HR/FB%. Common events like the fly ball rate are much more stable, making them much more useful for predicting the future. The problem with this approach, however, is that these common events often have little impact on previous results. FIP massively outperforms xFIP in describing, but xFIP beats FIP in predicting the future.

I bring up these shortcomings to emphasize what good contact statistics will do for us. Holding FIP and xFIP up to the light we can see the path to what we are looking for in the space left by what they miss:

  1. To reduce variance, it should use input that occurs more frequently than home runs.
  2. It should be something we can confidently show pitchers are in control of.
  3. To eliminate noise, it should not depend on parking factors or team defense.
  4. We shouldn't need much context (other than sample size) to use to compare two pitchers.

How will we know if we are successful?

One of the tricky things about predictive statistics is that there is no universal ruler by which to measure their success. We're not trying to delete any R2number (although I'll set some arbitrary benchmarks later). We're looking for better than what we have. My approach was pretty blunt — I just drew every pitcher's season with at least 100 innings since 2015 and started checking the correlations that datasets gave me. I'll recap the R2at the bottom (for those of you using tiny mobile screens, that should make things easier!), but you can hover over the trend lines to get the details. Darker trend lines indicate stronger correlations, and near-invisible trend lines mean weak ones.

So to get our bearings, let's first look at a few things we feel good about: strikeouts and walks.

You'll find that the average distance from the trendline is much shorter for strikeouts than for walks -- that's about what correlation measures. The R2of .273 for strikeout rate would mean that, on average, 27.3% of a pitcher's ERA in a given season can be explained by his strikeout rate. Put these skills to work and some interesting observations surface early on: K% looks a lot more informative than K/9, but BB/9 seems to be more important than BB%. K-BB% seems to be a solid indicator while K/BB is not. And overall, it looks like strikeouts are far more important than walks. Nothing groundbreaking here; We are currently using this dataset as a guide.

With that in mind, let's start with home runs. We know they are not very forward looking. But we do know that they have a lot of explanatory power. But how much?

The most effective measure was HR/9 with a staggering 0.427 R2. So if we're trying to develop a contact statistic, the bar for a good descriptive statistic is pretty high. Homers per nine do the job pretty well.

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This particular stat against HR/PA which I suspected would be an effective measure. Why could that be? Well, ERA and HR/9 both have the same denominator, so they tend to correlate. It's also possible that the runs-to-outs ratio contains some implicit information about a pitcher's ability to generate outs that HR/PA does not contain. If you look at BB% and BB/9, we see a similar trend. Worth watching.

So we have some guidelines for descriptive statistics. But if we want to know how meaningful a statistic is, we have to perform two tests. First, it's important to know how well a statistic predicts itself. And secondly, knowing how well it correlates to next season's ERA is helpful if we want to bake it into prediction statistics. Here, too, we start with deletions.

We can say that the strikeout rate is relatively stable, even one season into the future. Your 0.567R2is our highest mark for predictive power. Walk rate of 0.358 R2with next year's results aren't as stable, but compared to what we'll see next at home runs it's still decent. We knew pitchers change from year to year. This data only tells us how and by how much.

As for the correlation with ERA, the results are not all that strong.

Wondering why you can't see a trend line on walks? It's because functionally there aren't any. While the .209 R^2 is weak for K%, it's still palpably there. But the R^2 for the walking rate? Only 0.029. However, K-BB% beats K%, which tells us that walks deserve inclusion in ERA indicators, but that they're not a good way to compare two pitchers as a standalone stat. We would be happy to see a contact stats rival of K% for predictive power, but more realistically we would be happy to find one that is more correlated than BB/9.

And in case you're curious, home runs don't even come close.

This is the diagram that explains why xFIP exists. We should have little confidence that a player's home run rate in a season - measured in any way - will mean anything going forward. The best formulation of this, HR/9, has only 0.062 R2with himself. If you're wondering why these lines are flat, it's because the formula ignores the inputs and simply uses the league average instead. In other words, put little to no supplies in a house that's allowed, even for an entire working season.

As for correlation with ERA? It gets worse.

The best correlation here is only 0.016 R2for HR/9. That's actually the only relationship on this plot with a p-value below 0.05, and given the weakness of the correlation and context, that's not enough for me to strongly reject the null hypothesis either. In layman's terms, we should doubt that a pitcher's home run stats are related to his future ERA.

To recap, here's the R of each stat2the correlation for ERA in the season, ERA in the next season and for itself in the next season. Values ​​in red have a p-value greater than their correlation, and statistics in green represent the leading indicator in that column.

Strikeout, walk and home run R2of correlation, 2015-2019

StatS1 WASS2 HONORS2 self
HR/TBF %0,3330,0090,062

We can see how big the gap is between HomeRuns/9 and K% - they clearly have different uses. K-BB% tops the S2ERA correlation sweepstakes and also shows the importance of considering multiple inputs simultaneously.

The completely arbitrary R2The goals I have set for a new contact status are as follows:

  1. For S1 ERA: better than K%.
  2. For S2 ERA: better than BB/9.
  3. For himself in Season 2: midway between BB/9 and HR/9 (greater than .202).

Which Statcast Numbers Perform Best?

Choosing which numbers to look at is the most difficult task. I knew I wanted to take a closer look at both hard hit rate and barrel rate, but I also know that raw exit velocity is quoted often enough to be worth a look.

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After some early testing, I decided not to pursue exit velocity any further - there were both correlation and prediction issues. I'll include it in my summary at the end, but I'm not suggesting building around it. We use the hard hit rate because wOBA gains in exit velo are not linear, so calculating an average can provide an unhelpful measure. The expected results of four balls going 90 mph aren't the same as three balls going 85 mph and one going 105 mph, but that would give the same average.

So what types of hard hit rate and barrel rate were most useful?

HardHit/9 was the most telling pitcher input stats I found. It is 0.459R2will not beat ERA estimators like FIP, but this statistic requires multiple inputs. As a single indicator, it explains ERA better than home runs. For that reason alone, it deserves to be among the top stats we use to evaluate pitchers. Even its slightly less descriptive cousin, HardHit/TBF%, likely plays a role. K-HH% is worth considering as a stat on the back of the napkin.

As for barrels, they look pretty much like home runs, don't they?Six Man Rotation's Connor Kurcon has had great success with Barrels/BBE% as part of pCRA, but this arrangement was actually the least individual description of the six I include here. Again, using per-nine stats seems to be the best way to capture variance, probably because it contains more implicit information about a pitcher's ability to get outs. Whether this is good or not remains to be determined. But anyway, it looks like kegs are less effective than home runs in describing a pitcher's past ERA. And that makes sense: Not every barrel has scaled a fence, but every home run has.

As we look forward to next season, HardHit/9 and HardHit/TBF not only outperform barrels across the board - they're almost as stable as BB%.

Starting with the self-prediction, HardHit/TBF% - which, as a reminder, has the same denominator as K% - narrowly beats HardHit/9 as the most consistent stat. I don't think the margin makes sense - both should be useful. This is further proof that K-HH% probably has a bright future (as long as you use HH/TBF%).

The gap between barrels/9 and barrels/TBF% is also small, but the bigger news is that they just aren't that sticky. A0.12R2is twice the year-over-year correlation for home runs, but still comically lags behind the walk rate. In other words, we should have some doubts about whether looking into the future can help us. Given that home runs surpass barrels in terms of vividness, it's not clear what role barrels should play in our pitcher rating - there's always a better option.

When we look at S2ERA correlations, these lessons play out again.

This corresponds exactly to our expectations. As it stands, HardHit/9 seems to be the second most predictable input we can find, only behind Strikeout Rate. The gains versus HR/9 or the traditional barrel rate are gigantic in relative terms.

So, to recap, here's what we've learned so far:


StatS1 WASS2 HONORS2 self
Hard Hit/BBE0,1950,0280,176
Hard Hit/TBF0,3050,1030,277
Legend:new bestMeets the goalNo improvement

HardHit/9 achieved all three of my totally random goals. Not only is it more meaningful than HR/9 — it's an order of magnitude more sticky and predictive of future ERA. It's head and shoulders above the field in everything except when predicting itself where it's at the top along with HardHit/TBF%.

This data questions whether HardHit/BBE% and Barrels/BBE% deserve to be prominently featured by Baseball Savant. They're both just a hair better at explaining past ERA without predicting the future too well. They may still have a place in formulas that put them in the right context, but as standalone stats they're more of a distraction than a help.

I have more general questions about whether we should concern ourselves with kegs for jugs. Even in their more helpful standalone form, they don't give much improvement in home runs. I think they're probably helpful when we're trying to rate a pitcher who's going to a new team. But unless a pitcher's home park and defense changes, there probably isn't much to be gained by using barrels, especially considering HardHit/9 has beaten Barrels/9 at every step.

And as for exit speed? Somewhere it might be helpful. But since exit velocity returns are not linear, we know there are holes in this statistic. The numbers prove that.

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When can we trust these statistics?

Earlier, when I quoted numbers about the reliability of home run rates for pitchers, it was based on a statistical test that can be reproduced. Statistically, reliability is a somewhat difficult characteristic to measure well. With the help of Pitcherlist's in-house data guru, I decided to test it myself.

The method I chose was a Cronbach's alpha test. You can read more about it at Fangraphs, whereJonah Pemstein and Sean Dolinar have provided an extraordinary amount of information,including part of the code used. However, one of the limitations of this method is that it is exceptionally difficult to calculate reliability for per-9 statistics. I decided to only find the stats per hitter. Since the per-nine stats were about as efficient at predicting themselves, we should be able to use the data to draw inferences about both.

So how did HardHit/TBF and Barrels/TBF hold up against more traditional stats?

The order in which the stats become reliable the fastest is pretty much the same as the stat with the highest R year-over-year2. The exception? HardHit/TBF% actually becomes reliable faster than BB%! Assuming about 20 hitters per start, which is a little more than double the order, it would take less than 10 starts to estimate what a pitcher's HardHit/9 will be for the rest of the year. And if we assume that the per-nine stats take about that long to even out - think of the annual R2Values ​​- then HardHit/9 is probably also useful in about 10 starts. This means that we get meaningful results almost ten times faster when using HardHit/9 instead of HomeRuns/9.

A side note on my data: I suspect the drop in reliability we're seeing around 300 batsmen face is because I also included data for 2020. I suspect I wouldn't be seeing this if I only found the numbers for 2015-2019. The same effect shows up after about 750 batters are faced, which is about the maximum. So the individual outputs become very messy due to the lack of additional data. Pemstein describes the effect pretty well - check out his work if you're curious.

results at work

The first and simplest application is K-HH%. But what I thought was a significant step up doesn't seem to be. It's just a little less helpful than K-BB%. That doesn't sink it as a metric, but I had higher hopes.

However, my first attempt at an ERA estimator is extremely promising. I call it HHERA (I'll let you guess what that stands for). It's pretty simple - I used K%, BB/9 and HardHit/9 in a linear regression to estimate S2ERA.

HRERA 1.0:4.875703– (7,991393×K%0,06004585×HardHit/90,1678067 ×BB/9)

The good news? After running several k-fold cross-validations, I found that the average R2was 0.238 with an RMSE of 0.838. That's right behind itDan Richards' market leading FRA, Andpotentially before pCRA(It's a black box so I can't make fair comparisons, but HHERA beats its public numbers). I still need to work to assess whether I should include other variables or use nonlinear models.

I plan to give this project some additional work and with the help of some people who know how to get an ERA estimator working. If that's you, make sure you sign up.

Below you can explore HHERA, HardHit/9 and HardHit/TBF%.


The simplest and most obvious conclusion is that HardHit/BBE% doesn't deserve to be the stat we call "Hard Hit Rate". The same goes for barrels/BBE%, but at least we have the ability to easily and quickly check these numbers on the Statcast leaderboards.

In addition, the doors are open for people to take this data and work with it. There is so much room to apply what I have started here to different projects e.g. All the data I used is from Fangraphs leaderboards which is a great way to import reasonably sized files with exactly what you need.

(Video) What’s So Hard About Contact Suppression? w/ Alexander Chase (PitchCon 2021)

In terms of analysis, I hope this provides compelling evidence that pitchers have some control over the type of contact they give up. Exit velocity itself might not be the best way to measure it, but it seems we can learn a lot by treating hard hits as events rather than descriptors. I'm curious to see if other statistics benefit from a similar perspective.

In any case, it seems that HardHit/9 deserves its place among the few core stats we use when discussing how effective a pitcher has been and how effective he will be. It tells us more about past and future achievements than walking speed, while becoming reliable about as quickly. What's not to like?

Photos by Cliff Welch, Zach Bolinger / Icon Sportswire | Adapted by Jacob Roy (@jmrgraphics3 on IG)


What is a good hard hit percentage for pitchers? ›

Pitch TypeHard-Hit %Avg. Exit Velocity

What is hard hit rate statcast? ›

Definition. Statcast defines a 'hard-hit ball' as one hit with an exit velocity of 95 mph or higher, and a player's "hard-hit rate" is simply showing the percentage of batted balls that were hit at 95 mph or more.

Who has the highest exit velocity in MLB? ›

Exit Velocity & Barrels Leaderboard
Exit Velocity (MPH)
1Judge, Aaron118.4
2Alvarez, Yordan117.4
3Trout, Mike114.4
62 more rows

What is a good hard hit? ›

Statcast defines a “hard-hit ball” as one with an exit velocity of 95 mph or higher.

How hard should a d1 pitcher throw? ›

Prototypical Division I pitching recruits throw anywhere between 87 and 95 MPH on a consistent basis. It is important to remember that coaches are looking for pitchers to consistently throw at this velocity, not just touch it every once and awhile.

Is pitching 70 mph good? ›

The average fastball is between 50-60 mph. Although at this age the players may begin to reach puberty, and for that reason, it is not unusual to see a pitcher throwing around 70 mph.

What is a good hits per 9? ›

What is a good Hits Allowed Per 9 Innings?
  • Good Career Hits Allowed Per 9 Innings. Less Than 8.456.
  • Bad Career Hits Allowed Per 9 Innings. Greater than 9.947.
  • Good Season Hits Allowed Per 9 Innings. Less Than 8.
  • Bad Season Hits Allowed Per 9 Innings. Greater than 10.068.

Is .230 a good batting average? ›

For non-pitchers, a batting average below . 230 is often considered poor, and one below . 200 is usually unacceptable.

What is a good hit percentage? ›

As already mentioned, 300 and above is the ideal range for hitting percentages, with 200 still being good, and 100 is just below average but still workable. What does it mean when the hitting percentage is 0? When a hitting percentage is 0, it means that statistically, they are a wash.

Who threw a 108 mph fastball? ›

Nolan Ryan: 108.1 MPH

The Ryan Express was really bringing the heat that night in 1974, throwing the fastest pitch ever recorded in a Major League Baseball game. Just one of a long list of accomplishments in his historic career.

Who can throw a baseball 100 mph? ›

As far as the technology of the time could tell us, Nolan Ryan threw a 100 mph fastball. Randy Johnson was clocked as high as 102. Bob Feller may have hit 104 in his day, although we only have some creative science experiments to rely on for that figure.

Who hit the farthest home run ever? ›

In 1987, Joey Meyer, playing for the Triple-A Denver Zephyrs, launched this ball an astonishing 582 FEET!

What is the hardest-hit ball? ›

Oneil Cruz has hardest-hit ball recorded by MLB: 122.4 mph.

What is the hardest-hit to get in baseball? ›

Below is a quick look at the top 19 -- including the postseason -- as well as the best of the non-Stanton/Judge group.
  • 1) Stanton: 121.7 mph. Date: Aug. ...
  • 2) Stanton: 121.3 mph. ...
  • 3) Judge: 121.1 mph. ...
  • 4) Stanton: 119.8 mph. ...
  • 5) Kyle Schwarber: 119.7 mph. ...
  • 6) Manny Machado: 119.6 mph. ...
  • 7) Judge: 119.4 mph. ...
  • 8) Stanton: 119.3 mph.

Who has the lowest exit velocity home run? ›

Harold Ramirez's 85.4 mph shot right down the LF line at the Trop had the lowest exit velocity of any over-the-wall HR ever tracked by Statcast.

What is the average D1 fastball velo? ›

The average fastball velocity in Division I baseball is between 87 and 95 miles per hour.

How fast should a D1 outfielder throw? ›

High D1/ Elite JUCO Middle Infielder

In terms of arm strength, elite middle infield recruits will throw the ball across the diamond anywhere between 85 MPH and 95 MPH.

How fast should a D3 pitcher throw? ›

D2 pitchers are generally upper 70s to low or mid 80s, with the rare exception of high 80s to low 90s. D3 guys are more consistently low to mid 80s, with a fair number able to touch 90s. Competitive D3 divisions will sometimes have guys that sit 87 to 90.

What is the average mph for a 15 year old pitcher? ›

Average freshman pitcher (14 to 15 year old) cruising speed would be about 70 mph. Average cruising speed for a good high school pitching prospect at 14 to 15 years old would be about 75 mph.

How long does it take a 60 mph fastball to reach home plate? ›

At this speed, it takes about four tenths of a second for the ball to travel the 60 feet, 6 inches from the pitcher's mound to home plate, where the batter, with muscles as tense as coiled springs, like a predatory animal about to pounce, waits for the precise moment to swing at the ball.

How fast does a 100 mph pitch go? ›

A 100-mph fastball reaches home plate in under 400 milliseconds. The swing itself takes about 150 milliseconds. That leaves less than a quarter of a second for a batter to spot the pitch and decide whether and where to swing.

Who has the lowest WHIP in MLB? ›

Who has the lowest WHIP in MLB history? The lowest recorded WHIP in MLB history for a season is Pedro Martinez. In 2000 Pedro Martinez posted a 0.7373 WHIP for the Boston Red Sox. Addie Joss holds the record for career WHIP with a 0.9876.

Has anyone had 7 hits in a game? ›

In a 22-0 shellacking of the Chicago Cubs that day, Pittsburgh Pirates second baseman Rennie Stennett would record a hit in all seven of his at-bats – becoming the first and to date the only modern-era player to accomplish this in a nine-inning game.

What hitter has the most hits? ›

As of November 2022, the major league baseball (MLB) player, Pete Rose, tops the ranking of all-time hits leaders with 4,256 hits throughout his career. Rose is followed within this ranking by Ty Cobb and Hank Aaron who have amassed 4,189 hits and 3,771 hits respectively.

Is .307 a good batting average? ›

In modern times, a season batting average higher than . 300 is considered to be excellent, and an average higher than . 400 a nearly unachievable goal.

Is batting over 400 good? ›

In baseball, batting average (AVG) is a measure of a batter's success rate in achieving a hit during an at bat, and is calculated by dividing a player's hits by his at bats. The achievement of a . 400 batting average in a season is recognized as "the standard of hitting excellence", in light of how batting .

Who has the lowest batting average in the Hall of Fame? ›

Only three hitters have made it to the Hall of Fame with a sub-. 260 batting average: Killebrew, Rabbit Maranville and Ray Schalk.

What is the most important hitting stat? ›

Without a doubt, batting average is important. It shows a hitters ability to reach base on a swing, a vital part of baseball.

What is a perfect batting average? ›

To have a perfect batting average of 1.000, you have to have very few at-bats. The record for most at bats while maintaining a 1.000 batting average is three, held by John Paciorek. No player has come up to bat four times or more and gotten a hit every time.

Is a 110 mph fastball possible? ›

The fastest of them tops out at 105 MPH. WIRED examines why the 110 MPH fastball is almost impossible.

How fast was Sandy Koufax fastball? ›

Koufax only had two pitches: a 97 mph fastball that physicists denied but all facing batters said was gospel truth: the ball would suddenly hop up before crossing the plate. Koufax also threw a curve that would drop 10-12-inches off the table.

Do any MLB pitchers throw 100 mph? ›

Aside from Greene's two flame-filled performances this year, Jacob deGrom's start on June 5, 2021, is the only other pitching appearance in recorded history to feature 30-plus pitches over 100 mph. Greene's fastest pitch of the day reached 101.9 mph and he tallied 22 swings and misses during his six innings of work.

Did Nolan Ryan throw 100 mph? ›

During a September 7, 1974 game against the Chicago White Sox at Anaheim Stadium, Ryan became the first Major League pitcher to have his pitch speed measured during a game. A primitive radar gun clocked a ninth-inning fastball at 100.8 miles per hour (162.2 km/h) when it was 10 feet (3.0 m) in front of home plate.

How rare is a 100 mph pitch? ›

In the pitch-tracking era (since 2008), there have been 223 MLB pitchers to hit 100 on the radar gun, according to Baseball Savant's Statcast.

Did Nolan Ryan throw 235 pitches? ›

On This Date: Nolan Ryan pitched 13 innings, threw 235 (!) pitches and struck out 19. Ian Anderson and 8,219 others like this.

Has anyone thrown a no hitter and lost? ›

On April 23, 1964, Ken Johnson of the Houston Colt . 45s became the first pitcher to throw a nine-inning no-hitter and lose. In fact, he is still the only individual to throw an official (nine-inning) no-hitter and lose.

Has anyone ever hit a 600 foot home run? ›

Babe Ruth was said to have hit a home run over 600 feet. A Mickey Mantle homer was originally estimated to have gone 734 feet.

How far was Babe Ruth's farthest home run? ›

Babe Ruth, 575 Feet (1921)

Who hit the baseball 122 mph? ›

Pittsburgh Pirates rookie Oneil Cruz rips 122.4 mph liner, hardest-hit ball in Statcast era. PITTSBURGH -- If the goal of swinging a bat is to hit the ball hard, then Oneil Cruz did it better than anyone.

Whats the hardest a human can throw a baseball? ›

The Guinness Book of World Records still acknowledges Nolan Ryan's 100.9-mph pitch in 1974 as the fastest ever recorded. Yet pitchers Joel Zumaya and Mark Wohlers have since thrown 104- and 103-mph fastballs, respectively, since Ryan's throw, but Guinness didn't certify the results from the guns used to measure them.

What is the hardest MLB field to hit a homerun? ›

That honor goes to Coors Field. Even though it is the league's largest ballpark, the altitude in Denver helps sluggers get extra distance on their fly balls. So far in 2022, Coors Field ranks first in ESPN's MLB Park Factors for home runs, meaning it helps batters homer more than any other ballpark.

Is it hard to throw a baseball 90 mph? ›

Velocity Myth #2: “I Throw 90”

Despite it being more common than ever, still, very few pitchers can do this. On the average Division-I baseball team, each team usually has 4-8 players capable of touching 90mph, though perhaps only 1 or 2 who can average it.

What is the hardest exit velocity ever? ›

122.4 MPH exit velocity! 😮 Oneil Cruz just hit the hardest-hit batted ball in Statcast era history!

What is the highest ever exit velocity? ›

Oneil Cruz: 122.4 mph on Aug. 23, 2022. Giancarlo Stanton: 122.2 mph on Oct. 1, 2017.

What exit velocity do you need to hit a homerun? ›

For the hitters that I work with that hit the highest percentage of Home Runs, most of them have a max exit velocity of 76-82 mph and an average exit velocity of 64+. Having said that, you don't necessarily have to hit the ball that hard to hit a home run. If there is no wind and average conditions (humidity, etc.)

What bat speed do you need to hit a homerun? ›

Notice that all home runs are hit between 20 and 40 degrees. Even more important, you have to create enough bat speed to hit the ball 95+ miles per hour.

Has there ever been a 500 foot home run? ›

Stanton belted the first 500-foot homer in Statcast™ history by extending way up the Coors Field bleachers in the left-center power gap. It took a Stanton-ian combination of a 115.8-mph exit velocity and a very low 18-degree launch angle for Stanton to reach that part of the park.

What is a good hitting percentage in MLB? ›

300 or higher is considered to be excellent, and an average higher than . 400 a nearly unachievable goal. The last Major League Baseball (MLB) player to do so, with enough plate appearances to qualify for the batting championship, was Ted Williams of the Boston Red Sox, who hit . 406 in 1941.

What is the league average hard hit percentage? ›

The league average is around 24.5%. While each of these metrics has a different denominator, together, they can tell an interesting story about a player. These metrics require approximately 80 batted balls for HH% and 100 PA for whiff% to stabilize.

What is the average hitting percentage in MLB? ›

One of the oldest and most universal tools to measure a hitter's success at the plate, batting average is determined by dividing a player's hits by his total at-bats for a number between zero (shown as . 000) and one (1.000). In recent years, the league-wide batting average has typically hovered around . 250.

What is the best stat to judge a hitter? ›

The Best Ways to Evaluate Hitters

For those who don't know, OPS is "on-base plus slugging," or a hitter's on-base percentage (OBP) plus his slugging percentage (SLUG).

What is the most important stat for a hitter? ›

Since the beginning of baseball, one stat has reigned supreme over all others: the batting average. Simply put, the best hitters are always considered to be those who possess the highest.

Is .280 a good batting average? ›

260 batting average is the league average then a batting average of . 280 is pretty solid, . 300 is very good, and significantly higher than . 300 is great.

Is OPS the best stat? ›

The ability of a player both to get on base and to hit for power, two important offensive skills, are represented. An OPS of . 800 or higher in Major League Baseball puts the player in the upper echelon of hitters. Typically, the league leader in OPS will score near, and sometimes above, the 1.000 mark.

What is the hardest hit to get in baseball? ›

Below is a quick look at the top 19 -- including the postseason -- as well as the best of the non-Stanton/Judge group.
  • 1) Stanton: 121.7 mph. Date: Aug. ...
  • 2) Stanton: 121.3 mph. ...
  • 3) Judge: 121.1 mph. ...
  • 4) Stanton: 119.8 mph. ...
  • 5) Kyle Schwarber: 119.7 mph. ...
  • 6) Manny Machado: 119.6 mph. ...
  • 7) Judge: 119.4 mph. ...
  • 8) Stanton: 119.3 mph.

How rare is a perfect inning? ›

An immaculate inning is rarer than a perfect game, according to baseball statistician Ryan Spaeder. In a 2018 research project, Spaeder noted that there were just 23 perfect games – or 0.0106%—out of over 216,000 games in baseball history.

How strong is the average MLB player? ›

According to the study, players in rookie ball averaged a weight of 202 lbs, a 1.57 second 10 yard sprint and 10,798 vertical jump peak power. Major league players averaged a weight of 223 lbs (bigger), 1.52 second 10 yard sprint (faster) and 11,542 watt vertical jump peak power (stronger). Here is the conclusion (1).

What percentage of people can throw a baseball 90 mph? ›

Velocity Myth #2: “I Throw 90”

Despite it being more common than ever, still, very few pitchers can do this. On the average Division-I baseball team, each team usually has 4-8 players capable of touching 90mph, though perhaps only 1 or 2 who can average it.

What is the most important stat in baseball? ›

Batting average, RBIs, and home runs are the most commonly referenced batting statistics. To this day, a player who leads the league in these three statistics is referred to as the "Triple Crown" winner. For pitchers, wins, ERA, and strikeouts are the most often cited traditional statistics.

Does a walk count as an at bat? ›

Similarly, players who walk infrequently also typically record a higher-than-usual number of at-bats in a season, because walks do not count as at-bats.


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