Statcast Projected Isolated Power Performance in Predicting 2017 Performance

Last February, I took Statcast batted ball data to project Isolated Power performance.  The idea was that big in game power comes from hitting the ball hard and barreling up on it.  I lacked launch angle data, but considered batted ball types.  However, incorporating ground balls, fly balls, and line drives did not improve the model.  Regardless, a model knowing only average distance and barrel per at bat, you could fairly accurately predict Isolated Power performance.  Therefore, if a player underperformed according to the projection then you might expect a bounce back the following season.  However, I wondered whether there were reasons why a player would typically under or over perform the projection.

Looking at the 2016 season, it found the following players whose actual 2016 ISO was the most underperforming in comparison to the model projections:

	2016 ISO	2016 xISO	Diff
Miguel Cabrera	.247	.295	-.048
Josh Harrison	.105	.147	-.042
Brandon Belt	.199	.239	-.040
Howie Kendrick	.111	.149	-.038
Kendrys Morales	.204	.242	-.038
Buster Posey	.147	.184	-.037
Albert Pujols	.189	.226	-.037
Alex Gordon	.160	.197	-.037
Adeiny Hechavarria	.075	.109	-.034
Yonder Alonso	.114	.147	-.033
Troy Tulowitzki	.189	.222	-.033
Nick Markakis	.129	.161	-.032
Mitch Moreland	.189	.220	-.031
Yadier Molina	.120	.151	-.031
Adam Jones	.171	.201	-.030

Looking at this data, Kendrys Morales jumped out to me.  He was quickly scooped up by the Blue Jays in what seemed like a fairly curious move.  He had been a decent hitter for the Royals, but was not doing anything incredibly productive.  His lack of position also hurt roster flexibility.  A three year deal for a player like that seems a bit like folly.  At the time of looking at this model, I thought well maybe the Blue Jays think his ISO underperformed because of Kaufman Stadium.

Mid-season, you heard similar things about Adeiny Hechavarria when the Rays traded for him.  He was hitting the ball hard and barreling it, so perhaps the Rays thought they could better channel that into more productive hitting.  Anyway, how did these guys do in 2017 compared to 2016?

	'16 ISO	'17 ISO	Diff
Albert Pujols	.189	.145	-.044
Yadier Molina	.120	.166	.046
Miguel Cabrera	.247	.149	-.098
Kendrys Morales	.204	.196	-.008
Howie Kendrick	.111	.161	.050
Alex Gordon	.160	.107	-.053
Nick Markakis	.129	.110	-.019
Troy Tulowitzki	.189	.129	-.060
Mitch Moreland	.189	.197	.008
Adam Jones	.171	.181	.010
Buster Posey	.147	.142	-.005
Yonder Alonso	.114	.235	.121
Josh Harrison	.105	.160	.055
Brandon Belt	.199	.228	.029
Adeiny Hechavarria	.075	.145	.070
		32+	-.020
		31-	.047
		All	.010

One thing you will notice is that I reordered them by age.  Moreland and up are 2017 seasons played as 32 or older.  Jones and down are age 31 and younger.  What this paltry little sample seems to suggest is that underperforming your expected ISO is a major red flag for players in the mid to late 30s.  It is indicative of something else happening that is eroding performance.  However, for younger players, who are in less of a decline phase age-wise, show significant rebounding in performance.

What does this mean going forward?  Below are the players who most underperformed their expected Isolated Power performance:

Player	2017 ISO	2017 xISO	Diff
Miguel Cabrera	.149	.230	-.081
Mitch Moreland	.197	.248	-.051
Alex Gordon	.107	.157	-.051
Kyle Seager	.201	.248	-.047
Jose Peraza	.066	.113	-.047
Justin Turner	.208	.252	-.044
Matt Carpenter	.209	.250	-.041
Nicholas Castellanos	.218	.258	-.040
Jed Lowrie	.171	.209	-.038
Alcides Escobar	.107	.144	-.037
Albert Pujols	.145	.180	-.035
Chris Davis	.208	.241	-.033
Shin-Soo Choo	.162	.194	-.032
Dansby Swanson	.092	.124	-.032
Ian Kinsler	.176	.207	-.031
Jose Bautista	.164	.195	-.031
Hanley Ramirez	.188	.218	-.030
Nick Markakis	.110	.140	-.030
Yadier Molina	.166	.196	-.030
Joe Mauer	.112	.142	-.030

What we see above in this list is a lot of older players who failed to live up to the projections.  If the 2017 data is indicative of anything, this does not bode well for most of these guys.  Younger players on the list are a mix in availability like Nick Castellanos are supposedly available in trade or like Dansby Swanson are not available.  It is these players who we might expect as they age they refine their skills and are able to turn their barreling and distance into something more useful.

Chris Davis, for the Orioles interested readership, will be entering his age 32 season.  Above, that barely places him into the upper range that saw a major erosion in performance.  Molina and Kendrick were really the only two players who bounced back.  The others treaded water or further collapsed.  For those hoping for Davis to reclaim his past greatness, it is a weak indicator that perhaps that simply is unlikely to be in the cards this upcoming season.

The Orioles Need Better Outfield Defense

The Orioles outfield defense was considered poor by defensive metrics. Per Fangraphs, the Orioles outfield had a UZR of -21.1 runs, ranked 29th out of 30 teams. I’ve been known to be skeptical of UZR in the past, but this year UZR has just three teams with an outfield defense worse than 10 runs (1 win) and six with an outfield better than 10 runs. This is a significantly different from other years, suggesting a drastic change in methodology. Statcast’s outs above average metric tells a similar story as it ranked the Orioles outfield defense as worth -15 outs above average, tied for second worst in the majors. According to its sprint speed metric, the Orioles outfield was very slow and this helps explain its ineffectiveness.

Adam Jones has been the foundation of the outfield defense for the past ten years. He’s been fast enough to play center field while having above average offensive production. But good things can’t last forever and Jones has finally faded defensively. Statcast ranked Adam Jones as being worth -7 outs above average, 201st out of 210 outfielders. The reason why he was so ineffective was because he’s now slow. Per Statcast, his sprint speed was only 27.1 feet per second ranking 53rd out of 58 center fielders. It isn’t clear whether his defensive positioning had a detrimental impact on his speed, but this suggests that he needs to be a corner outfielder going forward. This could be problematic because his bat isn’t good enough to make him any better than an average right fielder offensively. Depending on his 2018 performance, the Orioles probably shouldn’t offer Jones a qualifying offer, nor should they extend him unless he signs a short-term deal at a significant discount from his current contract. There’s only so much a team should pay for leadership.

Fangraphs ranked Trey Mancini’s outfield defense as being worth -7.9 runs per 150 innings while Statcast valued him at -5 outs above average. Trey Mancini ranked 39th out of 58 left fielders with a sprint speed of 26.9 feet per second. That’s probably fast enough to play left field for the next few years, but it does mean he’ll need defensive help. With Trumbo and Davis on the squad for the next two years, Mancini will be forced to play left field barring an injury or a platoon.

Fangraphs ranked Trumbo’s outfield defense as being worse than -10 runs per 150 innings, while Statcast valued his defense as -5 outs above average. Mark Trumbo was graded as a DH, but had a poor sprint speed of 26.3 feet per second. Trumbo clearly has no business playing in the field, but his inability to hit as a DH suggests that he needs to play in the outfield. A center fielder with good speed and range can make up for Trumbo’s inability to run, but that center fielder isn’t Adam Jones at this point in his career.

Fangraphs ranked Smith’s outfield defense as being worth -5 runs per 150 innings and he ranked 49th out of 51 right fielders with a sprint speed of 26.4 feet per second. Seth Smith is a free agent this offseason and will likely not be a factor in the Orioles’ 2018 plans. However, his limited speed certainly contributed to the Orioles outfield defensive woes.

There is limited help on the roster. Chris Davis, with a sprint speed of 25 feet per second, was the 45th slowest out of 48 first basemen and now is about as fast as Matt Wieters. Davis has played in the outfield before, but can’t do so in the future if he is so slow. It would make sense to use him as a DH at this point. Austin Hays has had limited playing time as a major league outfielder, but had poor defensive results according to both Fangraphs and Baseball Reference as a right fielder. Statcast doesn’t have enough information to have a useful opinion of his speed/defense. He appears to be a defensive upgrade over Mancini/Trumbo but still below average.

The sole bright spots were Rickard and Gentry. Joey Rickard ranked 28th out of 51 right fielders with a sprint speed of 27.5 feet per second and Craig Gentry ranked 5th out of 51 right fielders with a sprint speed of 28.5 feet per second, suggesting that Gentry would be a valuable pickup on a minor league contract. However, Rickard’s .241/.276/.345 line suggests that he’s most valuable as a defensive replacement/pinch runner. In addition, he’s merely bad against left-handed pitching, suggesting that he could even be a platoon outfielder in a pinch. 

Camden Yards isn’t a large outfield, but even the Orioles need some outfield speed in order to play adequate defense and it is unlikely that anyone on their roster can provide that speed with acceptable offense. This will have to be a factor when the Orioles look at free agent outfielders if they want to improve.

The Orioles have been linked to Carlos Gonzalez, and MLBTR predicts that the Orioles will sign him for 1 year and $12 million. This would be a poor decision as Gonzalez’s sprint speed ranks 45th out of 51 right fielders at 26.6 feet per second. Not only is Carlos Gonzalez extremely slow, but he was ineffective last year against left-handed pitching with an .206/.241/.321 line with a 3.6% walk rate and a 30.7% strikeout rate. He was decent against left-handed pitching in 2016, but was terrible against it in 2015. With his limited speed, I’d expect his BABIP to plummet in future years. At this point in his career, he’s a defensively challenged outfielder that can only play against right-handed pitching. This has value to some teams, but the Orioles should probably pass given that they already have speed challenged players like Mancini, Davis and Trumbo on the roster and are lacking a fast center fielder to make up for their inability to cover ground.

Curtis Granderson is another player linked to the Orioles, but his sprint speed ranks 57th out of 58 center fielders at 26.6 feet per second. In addition, his low BABIP of .228 in 2017 and .256 in 2016 suggests that his speed is perhaps slower than it seems or that he’s vulnerable to shifting. In any event, he’s not fast enough to help solidify the Orioles’ defense playing right field.

A number of other free agent outfielders have the same issue. Jay Bruce might be an interesting addition, but his sprint speed is 47th out of 51 right fielders at 26.5 feet per second. Jonathan Jay is an interesting option, but his sprint speed ranks 56th out of 58 center fielders at 26.6 feet per second. Cameron Maybin ranks 43rd out of 58 center fielders with a sprint speed of 27.8 feet per second. Austin Jackson ranks 46th out of 58 center fielders with a sprint speed of 27.6 feet per second.

Maybin and Jackson are potentially decent corner outfield options with the ability to help the Orioles defense improve its range slightly. Neither of the two are considered top 50 free agents by MLBTR, while BORAS thinks they’ll each receive roughly 2 years and $20 million. 

Carlos Gomez ranks a passable 36th out of 58 center fielders with a sprint speed of 28.1 feet per second, but that’s a significant drop from his 2016 speed of 28.7 feet per second and his 2015 speed of 28.5 feet per second. At 33, one needs to wonder how long his speed can hold up. MLBTR projects him to earn 2 years and $22 million while BORAS has him at 3 years and $31 million. Gomez has been better against right-handed pitching than left-handed pitching over the past three years and could be platooned with Rickard. He’s a definitely reasonable free agent option for the Orioles.

At 32, Lorenzo Cain is one of the fastest runners in the majors with a sprint speed of 29.1 feet per second and would almost definitely help the Orioles outfield defense. He’s been above average offensively in the past and would be a strong leadoff option. MLBTR and BORAS are nearly in agreement about what Cain will earn, roughly $65-70 million over 4 years and will likely receive a QO. The Orioles will need to decide whether they can afford to sign him and whether it’s worth giving up the draft pick necessary. They’ll also have to see how long they think Cain can retain his elite speed.

Jarrod Dyson is another fast runner with a sprint speed of 28.8 feet per second. Jon recommended signing him in his final blueprint, and I think that he’d make sense. BORAS projects Dyson to earn a prohibitive 3 years and $32.5 million, but MLBTR has him receiving 2 years and $12 million. Dyson is old, but is above average defensively in center field and would allow the Orioles to use Jones in right field. Dyson historically struggles against left handed pitching, making him a viable platoon option with Joey Rickard. The Orioles almost certainly won’t offer 3 and $30 million to Dyson, but offering 2 years and $15 million would be an offer I could see them make. Dyson should be the Orioles first choice given their payroll situation and the fact that they need three new starting pitchers even if Cain is the better player.

The Orioles outfield defense was poor last year because their outfielders were slow. Their outfielders are only getting older and slower and so their defense will only continue to degrade without adding talent from outside the organization. Signing a player like Jarrod Dyson, Carlos Gomez and Lorenzo Cain would be a definite upgrade to the Orioles’ outfield defense and help them compete in 2018.

Trey Mancini is as Fast as Adam Jones

Baseball is a rather wonderful sport with a variety of ways to appreciate it.  It can range from a pure reactionary level of enjoyment with bias expectations at every turn, the pure notion of fanaticism, to heavily entrenched awareness of historical outcomes and uncertainties related to those potential outcomes.  One elusive area for many who appreciate the game has really been the scouting side.

Scouting to many is a black box.  A scout watches a player, tries to often qualitative evaluate ability and future ability.  That then is conveyed down a path to the public at large.  It can be frustrating to try to understand the process.  It is also curious how the public sphere of scouting resembles an echo box of scouting writers hitting and missing on the same exact players where one might consider there to be more variation.  Largely, the public industry has become rankings and enough of a description to be able to tweet out or participate on a message board with some manner of scouty credibility.

What has improved over the year is the technology and the ability to access that technology.  We can easily acquire fastball velocity, movement of pitches, and flight paths of balls.  We can see how able a player is at covering ground and making catches in the outfield.  We can see a player's coverage at the plate and exit velocity of batted balls.  We also can now see maxed out running on the basepaths to give us a good idea on player speed.

In the past, we have largely relied on play by play derived measures to develop speed metrics.  We look at stolen bases and caught stealing, we look at defensive range in the outfield, we compare ability to advance on batted balls, and how well a player stretches out hits.  Now, we have another tool in our tool belt and it does something grand: it measures a player's top speed on the basepaths.

Statcast measures sprint speed, the top speed a player achieves when maxed out running the basepaths.  For reporting, MLB requires ten events of maxed out running and calculates top speed by the fastest second.  What this can miss is acceleration.  Players may be able to reach top speed more quickly than others in the course of a 4 second or so run, but this metric gives us a good idea of top end running ability.

By looking at all qualified players this year, we can devise a frame of mind for a 20-80 scale.  My scale is based on the assumption that the average qualified baserunner is a 50 score.  I also assumed a more traditional take that every increase in score of 10 is equivalent to a standard deviation of the population.  We we wind up having is an average speed for a MLB player of 27.1 ft/s and a standard deviation of about 1.2 f/s.  This gives us the following tool grade table:

Grade	Speed (ft/s)
80	30.7
75	30.1
70	29.5
65	28.9
60	28.3
55	27.7
50	27.1
45	26.5
40	25.9
35	25.3
30	24.8
25	24.2
20	23.6

Byron Buxton has recorded the highest average sprint speed with 30.2 ft/s, so this scale would see him as a 75.  As you would expect the possibility of a player being three standard deviations from league average would be pretty astounding, so even a player like Buxton is unlikely to be rated an 80 based on this methodology.

However, that is not true for 20 grade speed.  We actually have four who quality as 20 grade speed when rounded: Miguel Montero (23.8 ft/s), Juan Graterol (23.4 ft/s), Brian McCann (23.3 ft/s), and Albert Pujols (23.0 ft/s).  Graterol, McCann, and Pujols actually are recorded below the 20 rating, which shows that an extreme lack of speed can be made up for with other qualities and context.  Largely, being a designated hitter or catcher where speed is not completely required with some element that past success and a big contract can keep you on a roster for awhile.

Where do the Orioles stack up?

	Speed (ft/s)	Grade
Gentry, Craig	28.5	60
Beckham, Tim	27.5	55
Rickard, Joey	27.5	55
Jones, Adam	27.1	50
Tejada, Ruben	27.0	50
Mancini, Trey	27.0	50
Schoop, Jonathan	27.0	50
Machado, Manny	26.9	50
Smith, Seth	26.4	45
Trumbo, Mark	26.3	45
Hardy, J.J.	26.2	40
Joseph, Caleb	25.8	40
Castillo, Welington	25.1	35
Davis, Chris	25.1	35

I think a couple things jump out to me.  Trey Mancini may be able to unlock some potential in left field if he can figure it out.  I have been getting negative reports on his fielding ability and that his athleticism is decreasing.  However, if he can maintain a 27 ft/s sprint speed for a few years and improve through experience in left field, then he can be an average defender out there instead of the mess (with infrequent very nice plays) that we have witnessed this year.

The second thing that popped out to me is that the Orioles are not exactly a club that values team speed.

For the most part, the above graph does not show that speed means success.  Speed often means being terrible because speed is a young man's tool.  Fast teams are typically young teams and teams who employ young players tend to be terrible teams.  Still, one might be concerned with how uniquely slow the Orioles are.

Thinking long-term, I wondered how the Orioles have faired over the past few years.  Particularly, I wondered how established future Orioles have measured up.

	2015	2016	2017
Tim Beckham	27.6	27.7	27.5
Adam Jones	27.7	27.6	27.1
Jonathan Schoop	26.4	27.1	27.0
Manny Machado	27.7	26.6	26.9
Mark Trumbo	27.1	26.7	26.3
Chris Davis	26.1	26.2	25.1

The sprint speeds tend to follow what we would expect.

• Beckham shows solid average speed for a shortstop or second baseman, wherever he may finally wind up.  He does not shows any decreasing trend.  
• Adam Jones went from about what one would consider a 55 rating to a 50 rating, which is a sizable drop.  He comes off as the fifth slowest CF in baseball for 2017.
• Schoop's depressed 2015 was likely due to his knee injury.  On the slower end of second basemen, but not exceptionally so.  He also looks to have below average, but acceptable speed for a corner outfield position.
• Machado is what we generally expected. He has bulked up and he is not the guy who stole 20 bases a few years ago.  He is settling into the 45/50 range, which he should maintain through his prime years.
• Trumbo's numbers are what we have seen in the outfield this year.  As a RF, he would be tied for second slowest (with Nick Markakis) and just a tad slower than Seth Smith.  Although he seems to hit better when playing in the field, it seems the corner outfield will not be an acceptable place for him.
• Davis dropped from slow to dreadfully slow this year.  That has shown up in his 2B:HR rates and his decreased movement around first base.  That level of speed erosion is troubling for a player who is in the second year of a very long contract.  The hope here is that Davis has a lower body injury that will heal this off season, but I have heard nothing about that.

Statcast's Sprint Speed will be something we should take a note of in the years to come to get a better handle on a player's physical abilities as well as maybe being an indication for injury.

Chris Davis Needs To Swing Smarter

Chris Davis is having a good, but not great year. His .238/.350/.464 line is good for a wRC+ of 119 and has him on pace to be worth 2.7 fWAR. These are good numbers, but not the elite numbers that Davis has put up in the past and that fans perhaps were hoping to see. It is possible to blame his lack of production on his increasing walk and strikeout rates. With a strikeout rate of 36%, Davis is on pace to have the highest percentage of strikeouts in a season for his major league career. His 14.4% walk rate is also the highest of his career, but doesn’t nearly make up for the strikeouts. Remember, Chris Davis crushes the ball when he puts it into play, so he benefits by doing it as much as possible.

A number of people believe that he’s struggling due to his low swing rate. He’s only swung 42.3% of the time in 2017, compared to 49.1% of the time over his entire career. It’s pretty clear that a batter can’t put the ball into play unless he actually swings at pitches. Is Chris Davis’s low swing rate causing him to struggle?

For starters, it is worth noting that his z-swing (swings at pitches in strike zone) is 51.4% while his o-swing (swings at pitches out of the strike zone) is at 35.2%. That’s a difference of only 16.2% between his z-swing and o-swing compared to his difference of 22%. This suggests that not only is he swinging less, he’s also swinging at worse pitches.

I’ve written previously about how batters typically have better results when they swing at pitches in the strike zone compared to when they swing at pitches out of the strike zone. They do a better job putting the ball into play and typically do significantly more damage when they do put the ball into play. In addition, Eli Ben Porat wrote an article awhile back discussing how distance from the center of the strike zone has a correlation with batter performance. This means we can measure Davis’ performance by looking at his swing rate, ball in play rate and wOBA in play rate based on distance to determine whether he should swing more often and where his performance is degrading.

It turns out that distance from the center of the strike zone does have a significant impact on hitter performance. But it’s also the case that it’s easier to understand the effects by putting the data into categories rather than looking at linear models. This is because distance doesn’t have an impact on performance right away. It’s just as easy (and maybe even easier) to hit a pitch that’s .4 feet away from the strike zone than .2 feet. Distance doesn’t begin to really become a factor, at least for Chris Davis, until a pitch is about .5 feet away from the strike zone. After a bunch of testing, I was able to split the data into eight unique bins. These bins are very probably player-specific, so it’s possible that hitters with better range would have different bins. The chart below shows Chris Davis’s performance for 2013-2017.

As the data shows, with the exception of pitches that are between 2 and 2.25 feet from the strike zone (Davis swung at 93 of these, whiffed at 82 of them while putting 2 into play of which 1 was a single), production decreases significantly based on distance. Chris Davis has above average results when putting pitches less than .95 feet into play, average results when putting pitches between .95 to 1.15 feet into play and below average results for anything past that. This suggests that in an ideal world, he would swing solely at pitches in the first two bins and at pitches in the third bin only when there are two strikes.

The data also show that he doesn’t live in an ideal world. While his swing rate decreases based on distance, he still doesn’t always swing at pitches in the first two bins while he does swing at pitches in the last three bins that he’s unlikely to hit or be productive when putting into play. However, he does historically swing at the vast majority of pitches in those two bins. It’s very possible that a more sophisticated analysis that takes pitcher hand, spin, perceived velocity and other relevant variables into account may show that the pitches in those bins he doesn’t swing at are harder to hit than just distance would suggest. It’s also possible that it’s hard for Chris Davis to hit a fastball down the middle if he’s expecting a curveball on the outside corner.

In addition, even when he does swing at pitches in the first two bins, he only puts them into play roughly 35% of the time. Chris Davis may be very successful when he puts the ball into play, but he struggles to do so even when swinging at appropriate pitches. This shows the challenges of putting even clear strikes into play, and hence explains why sluggers may want to be patient and avoid borderline pitches that may be balls. His results have been less favorable in 2017 as the following chart shows.

His wOBA on pitches put into play is actually slightly higher in 2017 than it has been from 2013-2017. His results are worse than expected on pitches that are less than .5 feet from the middle of the strike zone, but he’s doing surprisingly well against pitches put into play from .5 to 1.35 feet. All in all, his production when putting pitches into play is acceptable.

But unfortunately, he’s swinging at fewer pitches in the top three bins. In the first bin, pitches less than .5 feet away from the center of the strike zone, he’s swinging at only 60% instead of 77%. To some extent, he makes up for this due to the fact that he’s putting 46% of those pitches that he swings at into play, but his performance would likely be better if he swung more at those pitches. In addition, his contract rate isn’t significantly up for the second and third bin, and therefore the percentage of those pitches that he’s putting into play is down.

In fact, he’s swinging at nearly as many pitches in the fourth bin as the second and third bin. Historically, pitches in the fourth bin have been a called ball 79% of the time (81.8% in 2017) compared to 17% of the time in the second bin and 54% of the time in the third bin. Ideally, Chris should rarely swing at pitches that are more than 1.15 feet away from the middle of the strike zone.
Obviously, swinging at more pitches far away from the middle of the strike zone and swinging at fewer pitches close to the middle of the strike zone has had an impact on Davis’s performance. This would suggest that his problem isn’t so much that he needs to swing more or swing less, but rather that he needs to swing smarter.

Going forward, this chart suggests a way for the Orioles training staff to get Chris Davis to improve. It’s reasonably clear to see the distances where Chris Davis has success. It should be easy enough to instruct him in batting practice to only swing at pitches that are a certain distance from the center of the strike zone. Improving his eye should help his performance just as much as ensuring that his swing is working properly.

Chris Davis isn’t the type of batter that should be swinging frequently because he’s unlikely to put pitches into play. Rather, he’s the type of batter that needs to develop a patient eye, so that he gets the opportunity to swing at pitches that he is able to hit. With a batter like Chris Davis, it’s less important to swing frequently than to swing intelligently.

The Orioles Outfield And Catch Probability

The newest tool created by the people that brought us Statcast is Catch Probability. Catch Probability attempts to determine the difficulty to catch each ball hit into the outfield based on how far the fielder had to run to catch the ball and how long he had to get there. Based on the ability to quantify each play, they’ve put many catches into five categories:

5 Star Plays: Converted between 0 to 25 percent of the time and require a speed of 30 feet per fastest second.
4 Star Plays: Converted between 26 to 50 percent of the time and require a speed of 29 feet per fastest second.
3 Star Plays: Converted 51 to 75 percent of the time and require a speed of 28 feet per fastest second. 2 Star Plays: Converted 76 to 90 percent of the time while requiring a speed of 27 feet per fastest second.
1 Star Plays: Converted 91 to 95 percent of the time and require a speed of 26 feet per fastest second.

MLB has also created a 2015 and 2016 Statcast Catch Probability Leaderboard on their data portal at Baseball Savant. For each outfielder in these years, Baseball Savant tells us, the number of opportunities in each of these five categories that an outfielder had and how many times he converted an opportunity. Using this data for each outfielder with at least 50 opportunities, it’s possible to determine the league average in a category and how many catches above or below average an outfielder is presuming he faced an average difficulty of catch in each category.

There are at least four reasons why such a metric will be imperfect. Statcast doesn’t provide position information, so I’m comparing corner outfielders to center fielders even though center fielders likely have better range. This likely overvalues center fielders and undervalues corner outfielders. Such a metric presumes that each catch has equal value, but some catches are more likely to prevent extra bases than others. This metric tells us nothing about a fielders’ performance when fielding a pitch likely to be caught 96 to 100% of the time. Finally, if a fielder faces easier or harder opportunities in a bracket, then their value will not be valued properly. Note that this mostly doesn’t even take into account the positional problem that Statcast says exists. Still, even if this metric is flawed, it’s likely to have some value.

Jeff Sullivan from Fangraphs found average catch probabilities and frequencies for each of the five categories that Statcast created. The highest frequency of opportunities are 1-star plays despite the fact that this category only covers balls that are caught 91-95% of the time. One would expect balls caught 50-75% of the time to have a higher frequency of opportunities. The next highest frequency are 5-star plays which are converted only 8% of the time. These results are similar to the data collected by Inside Edge. If the data continues to be similar, then the overwhelming amount of chances will be in the 96-100% bucket and will be largely converted by all outfielders with minimal defensive competence. Given that in a full season, an outfielder has about 300 putouts, and the highest number of opportunities on this list is around 170, it’s safe to say that the data will continue to be similar.

This suggests that outfield defense follows something akin to the Pareto principle. Elite outfielders that can cover a lot of ground are significantly more valuable than average outfielders because they can convert tough plays. Terrible outfielders that can’t cover any ground are significantly less valuable than average outfielders. But most outfielders are more or less interchangeable --- especially in the corners. This is because there are relatively few plays that a good outfielder can make that just a bad outfielder can’t.

In 2016, out of 162 total outfielders with 50 or more opportunities, 115 (70%) were between -5 to 5 catches above average. Per 77.6 opportunities (average opportunities per outfielder in the sample) ranked in one of the categories quantified by Statcast and shared with the public, 93 were between -5 to 5 catches above average. In 2015, 120 out of 162 outfielders were between -5 to 5 catches above average (74%). On a rate basis, 102 were between -5 to 5 catches above average. Again, elite outfielders are extremely valuable and terrible outfielders are a liability, but there’s little difference between good defensive outfielders and bad ones.

There are a number of interesting players. As Jeff Sullivan stated, Mark Trumbo was terrible in 2016. He only converted 11 of 19 1-star plays good for a 58% rate. On a rate basis, Trumbo was easily the worst outfielder in 2016. It is likely that there was some bad luck involved, as Trumbo had nearly the same conversion rate on 1-star plays and 2-star plays, but Trumbo’s inability to make difficult plays further shows his lack of range. Trumbo wasn’t as bad in 2015, but was still one of the worst outfielders whether considering rate stats or his actual performance. Statcast suggests that Trumbo is best at either first base or DH which is problematic given that the Orioles already have Chris Davis.

Nick Markakis also had bad results using this method. He ranked 154th in rate stats in 2015 and 112th in 2016 but 171st in 2015 and 148th using actual counting stats. This method likely underestimates Nick because it doesn’t take into account his sure hands when fielding balls hit near him nor his strong arm. However, it is another data point that supports the sabermetric consensus that his range is terrible.

Andrew McCutchen ranked 160th in 2016 and 164th in 2015 looking at his counting stats. Part of that is due to the fact that he had a lot of chances, but he was still worse than 5 catches above average in both 2015 and 2016. This could suggest that he’s a worse fielder than normal metrics suggested and therefore his value may be limited. This could be why he is being moved from center field to right field next year.

Adam Jones ranked 28th in 2016 with 4.5 catches above average (actual result) and 10th in 2015 with 11.2 catches above average. This suggests that Adam Jones does indeed have above average range. However, this leader board doesn’t tell us how many of the catches he misses go over his head and therefore result in extra bases for the hitter. Still, it’s likely that Adam Jones has been underestimated by the statistical models.

Joey Rickard ranked 26th in 2016 with 5.3 catches above average. While he struggled to make two-star catches, he converted all of his one-star opportunities and was surprisingly successful with three-star and up catches. With a slight improvement in his route running, Rickard may just be able to become an elite outfielder. Pairing him with Adam Jones next year could pay significant dividends defensively although whether or not he can hit is another question.

Mr. Kim had especially interesting results. His rank was 145th in 2016. However, he was as good as the average outfielder on one-star and two-star plays. He was only slightly below average on three-star plays, which given that he had only eight opportunities may just be due to small sample size. However, he was 0-23 for four-star and five-star plays. This suggests that his fielding skills are acceptable, but his range/speed isn't good enough to play in the majors without significant help from Adam Jones. On the other hand, it may mean that an outfield of Kim/Jones/Rickard may be successful defensively because the other outfielders could cover for Kim's lack of range.

This data seems to suggest that most outfielders perform reasonably similar to each other but a few outfielders can be outliers. Outfielders with elite speed can be considerably more valuable than the average fielder. Outfielders with poor speed can be considerably worse, suggesting that the lower bound for acceptable outfield defense is higher than I might have suspected. This also suggest that trying to use players like Alvarez, Mancini, Walker and Trumbo in the outfield is a bad idea that will likely fail.

Statcast defines a one-star play as one that requires a player to reach a peak speed of 26 feet per second. Players that can’t successfully convert these plays the vast majority of the time will likely be unsuccessful outfielders. If so, it’s safe to say that teams can determine whether a player is fast enough to be an outfielder if he can reach a peak speed of 27 or 28 feet per second when trying to field a ball. If a player isn’t that fast, then using him in this capacity is a waste of time. Obviously, players like Nick Markakis with especially good hands and a strong arm, or players that are able to run good routes can be acceptable with a slightly lower speed. Players that run bad routes need to be able to go somewhat faster than that. It seems fair to argue that slow players could indeed be legendarily bad if they’re put in the outfield.

My understanding is that Statcast will continue to roll out and improve this metric over the 2017 season. If they include this data in their play-by-play datasets, then this will significantly advance our understanding of outfield defense and allow us to learn what it takes to be successful in the outfield.

Using Statcast to Project Isolated Power

Through my youth, there were basically only a few statistics one needed to know.  Homeruns, batting average, runs batted in, stolen bases, and, only if you were a bit wonky and had time on your hands, doubles and walks.  My youth was largely dictated by what someone back in the late 1800s who knew more about cricket than baseball thought newspaper readers would want to know.  In the past 16 years, though, we have experienced a renaissance as fans and clubs have begun to use computers, statistics, and approaches developed in other field to know more about the game.

Some of these new approaches required new technology.  One such approach is Statcast, which uses multiple cameras to identify elements such as players, the baseball, and a bat.  This is combined with radar data and the reward is a ton of data.  While one can use this data to evaluate pitchers, baserunners, and fielders, we will be using it in this column to discern ability in hitters.  Specifically, whether exit velocity of a batted ball can be related to the power metric, isolated power.  And, then, if one or two seasons of exit velocity data can be used to accurately project future performance.

Now, the first step in figuring out how useful these measurements might be is to compare them in season.  I only looked at player who had 300 plate appearances in both 2015 and 2016.  What we find is that average hit distance (forgive the error in the graphic below, it is average hit distance not home run distance) and barrel rate correlate very strongly with isolated power in the same year (p < 0.01 for both variables).  This means that these two ways to measure velocity and contact quality are related in-season to isolated power.  The regression model connecting those measurements to the metric isolated power was also significant (<0 .01="" p="">
Below is a graph comparing expected ISO with ISO for 2015 with the accompanying R2 value:

So all of this informs us that hit quality is connected to isolated power.  That should be obvious, but it is helpful to be able to see that here.  However, what we are really interested in is whether these values are meaningful from one year to the next.  In other words, is this simply a descriptive correlation or is it a predictive correlation. 

We will do a very simple comparison.  We will simply compare R2 values for expected ISO using the 2015 developed model vs. 2016's ISO.  This simple comparison will help show whether the formula using Statcast measurements better correlates with next season's values than simply using the actual ISO from the year before.  The comparison between 2015 ISO and 2016 ISO is not shown, but the R2 was 0.5026

What we find is that the Statcast method improves the predictive capability by about 15%.  That is remarkable, but is not earth shattering.  If your decision making process was simply finding the players with strong ISO, then this technique would help but it might take a decade or so for that to be able to be seen through the noise.  In general, I do not find this to be much of a silver bullet.  That said, it may be a hesitant flag for some players and suggest some players that should be expected to regress downward or upward.

Here is a list of players who the model thinks most underperformed.  In other words, who does this model think should have had a bigger 2016 than they actually did.

	2016 ISO	2016 xISO	Diff
Miguel Cabrera	.247	.295	.048
Josh Harrison	.105	.147	.042
Brandon Belt	.199	.239	.040
Howie Kendrick	.111	.149	.038
Kendrys Morales	.204	.242	.038
Buster Posey	.147	.184	.037
Albert Pujols	.189	.226	.037
Alex Gordon	.160	.197	.037
Adeiny Hechavarria	.075	.109	.034
Yonder Alonso	.114	.147	.033
Troy Tulowitzki	.189	.222	.033
Nick Markakis	.129	.161	.032
Mitch Moreland	.189	.220	.031
Yadier Molina	.120	.151	.031
Adam Jones	.171	.201	.030

One name that jumped out to me was Kendrys Morales.  He had a solid year last year, but the model thinks it should have been considerably better.  If the model better accounts for his talent, then we might see something closer to that expected isolated power.  It may well be that playing in Kansas City depressed his value a bit and some of his hard hit balls should have fallen in.  A different point of view would be that perhaps his isolated power was depressed because he is below average in converting singles into doubles.  That might explain why Pujols is up here as well.

Here is a list of players who the model thinks most overperformed:

Player	2016 ISO	2016 xISO	Diff
Brian Dozier	.278	.201	-.077
Nolan Arenado	.275	.224	-.051
Mookie Betts	.216	.170	-.046
Robinson Cano	.235	.190	-.045
Ryan Braun	.233	.189	-.044
Curtis Granderson	.228	.187	-.041
Jay Bruce	.256	.216	-.040
Edwin Encarnacion	.266	.229	-.037
Zack Cozart	.172	.137	-.035
Anthony Rizzo	.252	.217	-.035
Ben Zobrist	.174	.140	-.034
Carlos Santana	.239	.208	-.031
Didi Gregorius	.171	.140	-.031
Jose Bautista	.217	.187	-.030
Gregory Polanco	.205	.175	-.030
Josh Donaldson	.265	.235	-.030
Rougned Odor	.231	.201	-.030

I would have thought that the model would list speedster after speedster, guys who stretch singles into doubles.  That does not appear to be the case here.  Many of these players are rather plodding.  The closest Oriole on this list is Jonathan Schoop who comes in at a -.024, which is not a good thing to hear given how uneven and somewhat underwhelming his season was last year.

This made me wonder though about how things change over time.  For instance, is the over or under production from batted ball performance to expected batted ball performance a skill.  Are over producers always over producers.  What was remarkable was that the average difference between 2016's difference and 2014's difference was .011.  The greatest difference was .046.  This suggests that there is some element that I am missing.  The ability to over or under produce appears to be repeatable, so therefore likely having to do with a skill.  The next step is finding that skill.

A Quick Glimpse At Exit Velocity

Baseball statistics have gotten increasingly complicated over time. Once upon a time, people primarily used counting stats like RBIs, home runs and stolen bases. Some of the most complicated statistics were stats like batting average, fielding percentage and ERA basically requiring little more math than average division. Then, there were statistics like FIP, wOBA and WAR. These are difficult to calculate, but are reasonably simple to understand. A higher wOBA and WAR is better than a lower one. Now, with data from Statcast and Pitch Fx available to the public, it allows us the creation of new statistics that are difficult to both calculate and understand. One of these metrics is exit velocity.

Exit velocity is a relatively new stat that I believe was introduced with the creation of Statcast. It measures how the speed of a baseball after it is hit by a batter. According to Sports Illustrated, the Rays solely use exit velocity to measure batter performance. It’s also popular with players. Jake Lamb has said that he thinks that exit velocity tells you exactly what you need to know and that he likes this stat even though he generally doesn’t care for stats. It probably didn’t hurt that this stat ranked him highly.

However, it can be difficult to understand. Take ERA for example. There’s no special bonus for giving up a low amount of runs just like there’s no special penalty for giving up a large amount of runs. Each run allowed per nine runs increases a pitchers ERA by a run. Each double hit increases a batters OPS the same as every other double. In a word, these statistics are linear. You put all the numbers into a formula and it spits out an answer that values each result similarly.

It isn’t clear that this would necessarily be the case for exit velocity. If one hits a pitch lightly, then it means that a fielder may have to charge the grounder and make a throw to first while running to get the out. Depending the game situation, the fielder’s ability or the runners speed, a fielder may decide not to make the throw. If he does make the throw, there’s the possibility that the runner will beat the throw or that it will result in an error. In contrast, a grounder hit at a normal speed will likely be easily fielded with enough to throw for first for the out. When hit into the air, it will likely result in a routine fly ball. If exit velocity isn’t linear, then it means that we’ll need to come up with different ways to understand it.

My hypothesis is that graphing exit velocity by wOBA would result in a parabolic shape. Balls with high exit velocity would have the highest average wOBA. Those with the lowest exit velocity would have a medium wOBA, and those with average velocity would have the lowest wOBA. In order to test my hypothesis, I downloaded a summary table created by Baseball Savant that has exit velocity and wOBA and graphed my results. It turns out that I was somewhat right and wrong.

The chart below shows the average wOBA by exit velocity as measured in miles per hour. The graph isn’t a parabola per se, but there’s definitely some similarity. Batters do extremely well when they hit the ball over 100 mph. From 91-99 mph, their performance begins to decrease significantly. In the sample measured by the summary table, batters had a .542 wOBA when they had an exit velocity of 99 mph and a wOBA of .257 when they had an exit velocity of 91 mph. Batter wOBA continues to drop based on exit velocity until batters hit the ball at 35 mph. Once exit velocity drops to 35 mph, wOBA increases with it to the .350 mark. It seems that hitting the ball very lightly may be better than hitting the ball 90 mph. Here’s the chart.

This next chart shows average wOBA based on exit velocity as measured in groups of either 5 or 10 mph (only for 50-59 and 60-69 mph). This chart is probably a bit clearer to understand than the first one and clearly shows how a small increase in velocity leads to a large increase in production only if the ball is hit with an exit velocity of over 95 mph. It does seem to show that batters have a small bump in performance if they hit the ball between 60-74 mph, but this could just be a quirk of the data that wouldn’t show up if we used a different sample.

These charts show why exit velocity is different than other more standard stats. There’s a huge bonus for hitting the ball really hard. But if you hit the ball at an average speed, then it doesn’t really matter much if you hit the ball 60 or 70 mph. Your results will likely be substandard. There is also evidence that suggests that an exit velocity of 20-34 mph will result in better results than hitting the ball at 60 mph. To be fair, there were only 119 balls put into play between 20-34 mph and Statcast has difficulty measuring pitches hit that weakly. This may just be the result of an error in the data and may not actually exist in real life.

This is why there’s only a .561 correlation between exit velocity and wOBA when looking at all balls put into play measured by Statcast by all batters. It’s true that a hard hit ball is better in general than a softly hit ball, but that doesn’t make it true in all instances. As such, this is one reason why average exit velocity has limited value.

Instead of looking at average exit velocity, it is therefore important to look at exit velocity in bins. Pitches hit with an exit velocity of 105+ mph should be considered highly valuable. Pitches hit with an exit velocity between 100 and 105 should also be considered successful at bats. Pitches hit between 95-99 mph are not as valuable but still likely to be somewhat productive. Anything lower than 95 mph should probably be ranked as needing improvement.

Potentially, this may mean that a batter with an average exit velocity of 85 mph can be less valuable than a batter with an average exit velocity of 75 mph. If the batter with an average exit velocity of 85 generally puts the ball into play with an exit velocity between 80 and 90 mph, then he’s not going to be productive. However, a batter with an average exit velocity of 75 mph but puts a lot of pitches into play with an average exit velocity of over 100 mph or under 40 mph could be extremely useful. Especially if he’s a quick runner and can beat out a tough throw to first. In addition, it’s possible that an acceptable exit velocity depends on the type of pitch thrown.

Some of the new statistics available to the public are excellent and can help advance our understanding of baseball but don’t necessarily work like statistics have in the past. As a result, we’ll have to use different methodologies to gauge their value and what they’re telling us about baseball players.