# Projecting Lucroy's Rest of Season Stats.

In the past few weeks I have been working on putting together code to project "what if" scenarios using xStats. The first results of which you can find in Eno Saris' articles about Justin Bour and dejuicing the baseball. These both made use of the "what if the ball hadn't changed between 2015 and 2017?" what-if scenario. Which is interesting to talk about, but it isn't the only question I have to ask.

Obviously the trade deadline has just passed us by, so that lead to many interesting "what if" questions. What if a player played in a certain stadium? Well, that's what I'm here to talk about today. What would Jonathon Lucroy's stats look like as a Rockie?

In order to address this problem, I wrote a script that measures a batter's batted ball profile. That is, which angles and exit velocities he is more likely to produce, and then created a large pool of "potential batted balls." I then took a large number samples from this pool, and applied them to the remaining schedule for the batter. So, in this case, I imported the Rockies' schedule, selected a bunch of random "potential batted balls", and assigned them to games on the schedule with regard to estimated playing time.  I also added in projected strike outs and walks and randomized them in with the batted balls.

I'm not the best at coding, and it might be done more efficiently by someone more proficient, but with this method I can simulate just about 7 seasons in about 30 minutes. In this case I ran the code for about 90 minutes, which came out to about 23 simulated seasons worth of data.

The average results are:

Sim 1B 2B 3B HR AVG OBP SLG BABIP wOBA
Average 33.5 8.7 1.1 3.9 .283 .337 .413 .310 .326

Here are the worst two sims, which are almost identical, along with the best.

Sim 1B 2B 3B HR AVG OBP SLG BABIP wOBA
Worst 27.7 7.9 1.1 3.0 .214 .292 .315 .255 .262
2nd Worst 25.0 9.2 0.9 4.8 .214 .293 .352 .256 .272
Best 38.2 11.4 1.3 7.3 .313 .368 .506 .398 .360

Twenty three sims probably isn't enough to draw a strong conclusion. You may wand to see hundreds, thousands or tens of thousands of sims before making up your mind. However, in a time crunch, 23 isn't a terrible number. It is better than one, and it took me about 90 minutes to complete, and that was all I could set aside for today on this particular project.

With all that said, the average results are roughly equal to Lucroy's career numbers, and this is with a hefty boost from his new home ball park. So, maybe that is saying something. This season to date, xStats feels Lucroy has been largely unlucky, having lost 8 singles, a triple, and half a home run to what you might consider "bad luck." His expected slash line to date has been .268/.322/.366, a far cry from his .242/.297/.338 game production. His expected slash line is much closer to his career stats, and much closer to what everyone expected out of him this season.

Of course, his expected results are what the Rockies are trading for, and hope to get out of him from here on out.

When you compare that expected slash line to date to the rest of season projection from these sims, you don't find too large of a difference. It is about 15 points to batting average and on base percentage and 50 points to slugging. The boost in slugging coming from the spacious Coors Field outfield. Note, only about 55% of the simulated plate appearances are in Coors, which reflects the rest of season schedule.

Okay, so all of the above are the results of a simulation I ran, this next section is separate.  I have gone through Lucroy's spray chart and translated it to Coors Field, having run an algorithm to translate batted ball distances to the high altitude.

I made all the various failed at bats shades of gray, home runs red, and everything else shades of green and blue. Really, I want you to look at the number of non-red dots that go beyond the fences here. These are all of the batted balls from Lucroy over the past two and a half seasons. You might only expect 10% or so as many batted balls from here on out for Lucroy in Coors Field, but this park should really help his opposite field power. Even when you factor in the tall Coors Field fences.

Okay, so that spray chart was an aside. I thought it was cool, so I included it. Let's get back to the big picture. I am working to build this sort of "what if" calculations into the core of xStats, and use it as a tool to hopefully project players going forward. I'm hoping I can get this up and running in the near future, and cover many of the free agent deals and major trades during the off season. I hope you find it worthwhile.

# A New Stat! bbFIP

A few weeks ago I began dabbling with a stat that combined extremely well hit balls and walks plus extremely poorly hit balls and strike outs. In other words, automatic successful at bats versus automatic failed at bats. The aim here is to blend the line between Defense Independent Pitching, which largely ignores BABIP, and the newer, more nuanced approach towards judging batted ball quality.

When I announced this plan on twitter, Tom Tango pointed me towards an article he had written all the way back in 2010, which accomplished something very similar. Tom used line drives, ground balls, infield fly balls and outfield fly ball designations to create two separate categories of plate appearance value, which he called bigs and smalls.

$\mathrm{Bigs}=\left(\mathrm{BB}+\mathrm{LD}\right)-\left(K+\mathrm{iFB}\right)$ $\mathrm{Smalls}=\left(\mathrm{oFB}+\mathrm{GB}\right)$

The Bigs were given a weight of 11, and the smalls a weight of 3. Sum the two and add a constant (whatever number is required to bring the league average figure up to the league average ERA).

$bb{FIP}_{Tango}=11×Bigs+3×Smalls+C$

After reading this, I set out to create my own up to date version of bbFIP using Statcast derived numbers.

In the following weeks I have gone through many iterations of this stat, using many different starting points. I tried using hit probabilities, and while the results were okay, they weren't on par with what I was expecting. I have since moved to xOBA values, which has helped quite a bit. I toyed with including or excluding walks, hit by pitch, strike outs, home runs, medium hit balls, and many other factors. In the end, I came up with the following formula.

$\mathrm{OUTs}=\frac{.77×W+.17×K-.98×BB-.69×HBP-1.52×S-2.52×sHR}{PA}$

where W = weak contact (xOBA ≤ .245), S = strong contact (xOBA ≥ .634), and sHR = strong home runs (xHR% ≥ .55).

This gives you what I call "OUTs".  You can then convert this to bbFIP by multiplying by -11 and adding a constant, which is roughly 5.6.

Strikeouts have an xOBA value of 0, that means the strikeout is double counted in this formula. The first time it is given a value of .77, and the second time it is given a value of .17, so together it has a total value of .94.

The same is true for walks, hit by pitch, and home runs. They have a total value of 2.50, 2.21, and 4.04, respectively.

I will continue to test and iterate, so I may stumble upon superior combinations of stats over time. However, I've already ran through nearly 400 combinations and these have generated the best results.

# Why use bbFIP?

I've looked at every pitcher over the past 2+ seasons who threw at least 20 innings in one season. Of these 1380 pitchers (many are double or triple counted), FIP had an MSE of 1.027, and bbFIP .968. In other words, for roughly two thirds of pitchers, bbFIP was within .968 runs of their ERA, while FIP was within 1.027 runs.

You can consult the table below for the in season results.

Year w-kFIP FIP bbFIP
2015 1.45 1.02 0.93
2016 1.54 0.96 0.93
2017 2.02 1.13 1.08

And the Year to Year results.

Year w-kFIP FIP bbFIP
15-16 1.80 2.98 2.10
15-17 2.71 3.92 3.23
16-17 2.27 2.84 2.72

In all but one case, bbFIP is superior. The case where it isn't? Comparing this season to last. That is an odd one, I'm not sure I can explain it. Maybe you can? Either way, bbFIP appears to have less error over time, and the error is, for the most part, less than a run. In the past two seasons, it has hovered around .93 of a run, although the first half of this season is up towards 1.08. The 2015 to 2016 numbers and 2015 to 2017 numbers are both promising, and perhaps by the end of the season the 2016-2017 numbers will fall into place as well.

In my original post, I made an error in the numbers.  This error was caused by sloppiness, in which I accidentally combined two groups of data that should not have been combined. I was able to recreate the error, so I know exactly what I did wrong. I have reran the numbers, this time without that sloppiness, and I have updated this post with those results. I have also included walks minus strikeouts FIP.  Which follows a simple formula.

${FIP}_{w-k}=\frac{BB+HBP-K}{PA}+C$

In terms of predictive ability, bbFIP might be somewhere between w-kFIP and FIP. Simplicity still wins the day.

However, I still believe this stat is a worthy of your attention, and I am curious how it fares as time goes on.

I know that FIP was originally developed to be easy to calculate. You just take a few numbers multiply by integers, add them up, and divide by innings pitched. You just needed a sheet of paper to do the long division, or a simple calculator. This version of bbFIP is not as easy. That is unfortunate. In principle it isn't very difficult, just multiplication, addition, and a bit of division. However, calculating the xOBA and xHR values cannot be done by hand. And they aren't available publicly, although you could use the xwOBA which might give similar results.

In my spreadsheets I have supplied bbFIP for all pitchers, and OUTs for all batters. You can easily convert from OUTs to bbFIP by multiplying by -11 and adding 5.6-ish. Tell me if you'd rather have bbFIP for batters.

Finally, here are the league average OUTs and bbFIP Constants.

Year OUTs bbFIP C
2015 .152 5.627
2016 .132 5.642
2017 .117 5.604

Dexter Fowler has not had a great start to the season after signing an $82.5 million deal with the St. Louis Cardinals. He's batting .230/.323/.446 with a .328 wOBA and a 0.8 WAR a third of a way through the year, a far cry from the level of excellence he established during his time with the Cubs in 2015 and 2016. But fear not my friends. Fowler has had a sluggish start to the year, I believe he might be hitting the ball better than ever. ## Power Yes. I believe Dexter Fowler is hitting the ball better than ever. And I think a lot of this has to do with him discovering a power stroke. Dexter Fowler has never been known as a home run hitter. His .429 SLG ranks 130th among qualified batters since 2012. However, this year Fowler is posting a .216 ISO, by far the best of his career. His previous high water mark was in 2012 when he had a .174 ISO with the Rockies. This time we can't point to a Coors Field effect, something has changed. The first thing that stood out to me was a decline in walk rate, which has been a strong suit for Fowler in recent years. This hints towards a change in approach, which can also be demonstrated using his Swinging Strike rate. Here is a chart from Fangraphs displaying the rolling average of his Swing%. This shows Fowler has definitely been more aggressive. Since 2012, 63.9% of Dexter Fowler's pulled fly balls were categorized as a hard hit by Baseball Info Solutions. This ranks near the top 10% of baseball, minimum 50 batted balls. This year, 79% of his pulled fly balls have been hard hit. In addition, his fly ball rate is up to 40.8%, up 5.5% from last season. His pulled rate, 43.3%, is a bit higher than his career average of around 40%, but roughly in line with his numbers in recent years. In other words, Dexter Fowler is more efficiently taping into his underlying strength as a batter: pulled fly balls. Alas, more power. Pitchers have even seen this too, pitching him outside more often. Here are his pitch heatmaps from last year and this year. (click the image or the arrows to switch between seasons) ## Luck Dexter Fowler has probably been one of the unluckiest hitters in the league this year, which has hidden the wonderful strides he's made in his approach. The concrete results have not been there (yet), with Fowler sporting a .769 OPS and 103 wRC+. xStats says he has been hitting more along the lines of a .860 OPS, with bad fortune holding him back. The xStats believe in the power too, tabbing him at 10.9 xHR, compared to his actual 9. About a third of the way through the season, he is already more than halfway past his xHR total from last year. Here is some more evidence to suggest his added power is for real. Season PH% VH% Flyball EV High Drive EV 2016 18.9% 5.4% 86.6 90.1 2017 20.0% 7.2% 93.3 93.0 So in short, Fowler has been able to make better contact, especially in the air. This is a great recipe for more balls to land over the fence. ## Added Power, But No Added Strikeouts? The thing that has probably impressed me most about Fowler has been his ability to add some pop without sacrificing his plate discipline for the most part. This is very hard to do. His strikeout rate is around his career norm still. Like I mentioned above, he has seen a decline in walk rate. Though, he still is no slouch. Here is a list of hitters with at least 200 plate appearances, 11 BB% or higher, a .210 ISO or better, and less than 21 K%. Name PA HR R RBI BB SO Joey Votto 273 18 47 50 44 32 Mike Trout 206 16 36 36 36 42 Bryce Harper 243 15 49 46 38 51 Paul Goldschmidt 279 14 55 51 46 50 Nelson Cruz 242 14 30 46 28 44 Kris Bryant 269 14 40 27 43 53 Anthony Rizzo 278 13 36 37 39 31 Anthony Rendon 244 11 29 38 35 34 Todd Frazier 222 10 27 31 32 46 Zack Cozart 243 9 38 33 32 44 Dexter Fowler 235 9 34 24 28 49 Not a bad list to be on, especially if you want to be considered a power hitter. ## Future With this new approach, I expect some success to start showing up for Fowler. If he can keep pulling the ball with success, like he has been, I can definitely see him finishing with a slashline of something like .275/.365/.465. I might be getting bold here, but I think a new career high in home runs isn’t out of the question. That would be a very pleasant surprise for Cardinals. So expect the batting average to start trending the right way for Dexter Fowler. And expect the power to stay. # Zack Godley And His Fastball Are For Real In the past, we’ve seen many pitchers have success as starters after stints as relievers. David Price, Chris Sale, Matt Shoemaker, and Danny Duffy to name a few. We are currently seeing a player have ease with that same transition. Arizona Diamondbacks righty Zack Godley. Not a pitcher that will blow hitters away, Godley’s bread and butter is the movement on his pitches. A dropping sinker, a curveball with a lot of 12-6 movement, a cutter with depth, and changeup make up his repertoire. After being drafted by the Chicago Cubs in 2013, he pitched in a relief role, not even making his first professional start until being sent over to the Arizona Diamondbacks in the Miguel Montero deal in 2015. He made 17 starts that year between the Diamondbacks A+ and AA teams. Also making his major league debut that year, he pitched an impressive 36.1 innings 2016 was a different story though. Among 207 pitchers with 70 innings last year, Godley had the 5th worst ERA. This landed him back in the minors to start 2017, where he posted a 3.67 FIP in 24 innings. He wasn’t there long, as the Diamondbacks recalled him to fill a spot in the rotation earlier in late April. And so far, the results have been terrific. Godley’s recent success hinges on his ability to generate weak ground balls. Since his first appearance of the season on April 26, Godley has posted the third highest GB% in baseball. Godley has always been able to generate ground balls in his career, but never with this amount of consistency and effectiveness. In the past, he has struggled with command on his fastball, but their seems to be some improvement with that. It is exceptionally difficult to quantify command directly, so instead here are the batted ball stats from his fastballs in 2016 and so far in 2017. Season xAVG xSLG xOBA VH% PH% Exit Velocity Launch Angle 2016 .377 .695 .455 10.7% 25.0% 88.9 2.6 2017 .332 .502 .390 7.6% 27.9% 85.9 -2.0 To date, there has been significantly less damage done to his fastball this year when compared to last year. Here you can view his 2016 and 2017 heatmaps side by side (click the image to switch view, each heatmap should be labeled in the top left corner). When he can command that fastball, along with his above-average movement, he’s untouchable. Instead of a throwing a fastball that was all over the zone in 2016, Godley has been able to keep the pitch down more often. That is where he gets hitters to swing at a bad launch angle, therefore producing ground balls. He’s even getting more strikeouts. Explained further in these GIFs. 2016: 2017: And why is he having this success? I guess maybe a new release point could be a probable cause. So to summarize, I think Zack Godley is pitching well and a lot of it has to do with his fastball. I think the success is here to stay too. I believe we still have yet to the best of that sinker. If Godley can pitch well over a longer period of time, the Diamondbacks might have something matriculating with that pitching staff. # The Struggles of Johnny Cueto Recently, San Francisco Giants$130 million dollar man, Johnny Cueto, had been reported to have blisters on his pitching hand. This had explained his recent struggles, which had been highlighted by a poor performance against the Chicago Cubs Tuesday night.

Johnny Cueto has been no stranger to rough stretches too. After the Kansas City Royals had invested a lot in him during a World Series run by giving up three nice pitching prospects, he scuffled along the final months of the season, going 4-7 with a 4.76 ERA.

This didn’t scare away the San Francisco Giants though, as they gave him elite money during the 2015 offseason. Cueto was more than fine in 2016.

But 2017 has been a different story. The worries are back. Sure, it might be just a simple blister. Don’t tell Rich Hill that.

# Problems

The problems for Cueto this season have been all over the place. It’s everything. Velocity is slightly down. Spin rate is down. The Movement is whack. Look at how much more of the plate he is catching.

Here is a heat map of his pitches last year.

This isn’t particularly a good thing when you’re not spinning your pitches as well as you have. For example, in 2015 and 2016 combined, Cueto ranked in the top third of baseball in spin rate on fastballs. This year he ranks 353rd out of 475 pitchers with at least 25 fastballs thrown. But what really is suffering is his changeup. Last year, it spun at an RPM of 1522. This year it is at 1408. That is 146th out of 153 pitchers with at least 50 thrown changeups this year.

Another concerning thing is the movement on his stuff. Take a look at this visual from Brooks Baseball on the vertical movement of his pitches every month of his career.

Look at the end of this chart. The month of May has been disastrous. I want to highlight his changeup. He's usually had great 12-6 movement on it. This made hitters put some bad swings on the ball. Among the 88 pitchers with at least 50 changeups put in play last year, Cueto posted the 19th lowest launch angle at 3.2, the eighth highest mark in the majors.  This led to Cueto producing a lot of ground balls on this pitch.

Cueto doesn’t have that same 12-6 movement this year, and his groundball rate is down by 38%. If Cueto pitched the 219.2 innings he did last year, he’d be on pace to only get 48 ground balls off his changeup.

Just take a look.

The GIF from 2016 shows that great changeup by Cueto getting Carlos Gonzalez to groundout to first to finish a complete game.

The one from 2017 lacks that same snap. It is left out over the plate for Jake Lamb to demolish. Speaking of getting demolished, that's happening to Cueto a lot more often this year.

# xStats

So far this month, Cueto is posting a Value Hit percentage (high quality contact) of 8.5%. That is the second highest mark since 2015 when the stat became available. Cueto's worst month in 2016 was only 7.4%, although there seems to be a general upward trend over time when you factor out the peaks and valleys between each month.

## VH% by Month

Last year, Cueto had an xSLG of .392. This year, though in a small sample size, he is holding a xSLG of .513.

For even further measure, here is a chart of the monthly average exit velocity for Cueto since the beginning of 2016.

## Exit Velocity by Month

Since about the middle of last year, hitters have been making better contact off Cueto.

# Conclusion

These issues can easily be attributed to Cueto’s reported blister this year. But also, let’s not forget that this may of been steadily happening since the middle/end of last year.

Could this suggest Cueto has an injury going on behind the scenes? Could/should this warrant a DL stint? I don’t know. Cueto says no, according to CSN Bay Area.

“That’s the type of efficient performance the Giants came to expect from Cueto last year. Cueto still expects it from himself, but his fingers aren’t cooperating. Asked if he would take a short stint on the DL to get right, Cueto said he can’t. He needs to keep pitching and have callouses form. Plus, any break without throwing would be a significant blow to a team trying desperately to stay within shouting distance of a playoff spot.
“Basically, it makes no sense whatsoever,” to take a break, Cueto said.”

I believe this shouldn’t be taken lightly.

# How SunTrust Park May Impact Freddie Freeman

Yesterday The Hardball Times published my article about estimating the home run rate of SunTrust Park. You can read that article here. Today I will publish a snippet regarding Freddie Freeman which was left on the editing room floor.

You may be interested in reading another piece I wrote about Freeman on Rotographs, which sought to address a similar concern, albeit prior to finalizing the data for SunTrust Park.  I recommend reading both of these articles prior to reading this one.

## How It May Impact Freddie Freeman

You can’t talk about the Braves without mentioning Freddie Freeman, especially when they bring in the right field fences. Freeman is the most prolific left handed power hitter on the team. However, Freeman has never been a pull hitter, instead displaying power to all fields.

I took all of Freeman's batted ball data and—using xStats—determined his expected home run totals to each section of Turner Field over the prior two seasons, along with a projection for 2017 in SunTrust Park.  You can see the results in the chart below.

Indeed, he seems to have more home runs in SunTrust, 21.7 as opposed to the 20.6 in 2016. While the difference is projected to come from right field, I'd hardly say one home run is the boost many people expected to see for Freeman after moving in the fences between 13 and 18 feet.

The cut off for what may be considered center field or the gaps is a bit arbitrary, so the exact locations of these balls might be fungible. My models for SunTrust Park claims 25% of Freeman’s balls hit above 87 mph with a launch angle greater than 15 degrees will be home runs. Last season, in Turner Field, 23% of those balls expected home runs—meaning they would have left a neutral ballpark—and 17% were actual home runs. So, in his home games, that is a 8% increase in expected home run totals, and a 47% increase from his actual 2016 home run total.

This means Freeman will be disproportionately helped by the new ballpark, but only by a small margin. The bigger difference in performance will come from leaving Turner Field, which held him back significantly.

Last season he also hit 19 home runs on the road, versus his 15 at home. In other words, he hit roughly 26% more home runs on the road. This is roughly on par with where Turner Field home run rates compared to league average home run rates.

Turner Field was 29% below average, so with 15 home runs there, you’d expect roughly 19.4 home runs on the road. Obviously you can't hit .4 of a home run, so 19 is the closest integer.

Long story short, you could expect Freeman to hit about 19% more home runs during a season while calling SunTrust Park his home.

Granted, there is a very real possibility that Freeman could adapt to pull the ball more in order to take advantage of the right field wall. The right field dimensions are especially friendly for high fly balls down the line, which may be an alluring target for any left handed batter on the team.

# Francisco Lindor is a Legit Power Threat

It isn’t a secret. Francisco Lindor is a phenomenal baseball player. Whether it’s making an amazing play at shortstop, generating great contact, or even drawing a walk, Francisco Lindor is drawing the attention of baseball fans.

Francisco Lindor has never been thought of as a power hitter. He doesn’t appear as one either, standing at 5-11, 190 lbs. Here’s what ESPN Prospect Writer, Keith Law had to say.

“Lindor doesn't look like a power hitter but has exceptional lower-half strength and his swing will allow him to eventually get to that power even though he doesn't finish with a ton of loft. Even at 12-15 homers, which is probably a neutral projection for him, he'll be an All-Star thanks to grade-70 defense and OBPs up near .400 with plenty of doubles and 20-plus steals a year.”

And John Sickels of Minor League Ball.

That's in the short term. In the long term, I suspect that Lindor can develop into a better hitter than most people currently expect. I like his swing. He makes easy contact, controls the strike zone reasonably well, and should grow into more gap power as he matures physically. He isn't going to be a 20-homer guy, but it wouldn't surprise me at all to see him hit 10-15 homers per season at maturity, with a respectable number of doubles and above-average batting averages and OBPs. Add that to his defense and you'll have a helluva player by the time he's in his mid-20s.

This was a reasonable statement too. Many viewed Lindor as an all-around, good baseball player. Great plate discipline, switch hitter, great glove, great baserunning, etc. Many also believed there was power in that small frame. And like Keith Law said, the strength in his lower body was capable of spraying the ball all over the field with a line drive approach. 12-15 homers was a foreseeable amount for him, an amount most teams would take for one of their middle infielders.

But maybe there is more.

# Ball in the Air

To get an overall understanding of Lindor’s tendencies, let’s rewind back to his rookie year in 2015. That year, Lindor posted a vertical angle of 4.5, well below the major league average of 10.5 that year.

Not surprisingly, these numbers led to a very above-average 50.8% groundball rate.  But with his great speed, this was a good thing. Out of 252 batters that hit at least 100 groundballs in 2015, Lindor ranked second in BABIP at .337. Though, this did hurt his power game, as his xSLG was at .432 in 2015.

Lindor then made some improvements in 2016, posting a vertical angle of 9.3, more than doubling the number from the previous year. In doing so, he was making better contact, increasing his exit velocity, average batted ball distance, and line drive percentage.

With this, we started to see some power.  In 2016, he hit 19 home runs, with xStats tabbing him at 15.4.

Looking back at Lindor’s average launch angle by month, I noticed something. In September Lindor posted the highest launch angle of his career by a decent margin, coming in at 13.9. But the results weren’t showing. From the looks of it, Lindor was struggling to make good contact posting the highest PH% of his career.

# Results

Now, if you’ve seen Francisco Lindor in so far this, it’s scary. He’s on pace to have the best month of his career by far. It looks like he’ll have the highest xSLG, xOBA, and xOBA+ in any month of his career.

And this all because of a drastic change in his launch angle.

Like I said, Lindor had the highest launch angle of his career by a decent margin last September. Well, this April has been a different world. Lindor has had a launch angle of 17.3 so far this year. And guess what, that isn’t even his biggest change.

Lindor is swinging the bat faster. On well strick balls (21 to 36), he is swinging the bat 68.2 MPH, well above his career average.

Put this all together, what do you get? More homers! Lindor is leading all of the majors in xHRs, at 6.7. Next closest is Khris Davis at 5.9. He’s second among qualified hitters in xSLG.

# Conclusion

This power streak by Lindor might be here to stay. As long is keeps digging the bat downwards, his lower body strength and quick swing (ranks 2nd in swing speed with launch angle of 21 to 36) will generate some serious pop for a middle infielder.

Lindor’s power might be heading more in the territory of someone like Carlos Correa or Corey Seager. He might not have quite the pop of some of the elite power shortstops, but he’s trending there.

And if this is for real, Francisco Lindor might be on a path that leads him to Cooperstown.

# xStats and Fantasy Uses for Statcast

This is the most up to date article about xStats. It describes the current methodology and the descriptive and predictive qualities of the stats. It also delves into a quick and dirty method for adjusting the stats using a histogram of their exit velocities.

This article was featured on the Hardball Times on March 9th, 2017, and it is even John Sickels Approved. I consider it a must read for anyone even tangentially interested in xStats or Statcast in general.

# Player Deep Dives

Over the past few weeks I have written a number of articles covering a number of players I feel are misrepresented by xStats, and other projection systems, due to either small sample size issues or sudden and drastic changes in circumstance.

First Published 1/26/17

First Published 1/12/17

First Published 2/23/17

First Published 2/23/17

# Introducing xFantasy Parts I, II, III, and IV.

xFantasy is a system based on xStats that integrates hitters' xAVG, xOBP, and xISO in order to predict expected fantasy production (HR, R, RBI, SB, AVG). The underlying models are put together into an embedded "Triple Slash Converter" in Part 2. Part 3 compares the predictive value of xFantasy (and therefore xStats) vs. Steamer and historic stats, ultimately finding that for players under 26, xStats are indeed more predictive than Steamer.

First Published 12/22/16 Written by Ryan Brock

First Published 12/23/16 Written by Ryan Brock

First Published 1/21/17 Written by Ryan Brock

First Published 2/24/17 Written by Ryan Brock