Dissecting the Power of STATS TVL Data


Advanced analytics in baseball have come a long way since STATS was founded almost 37 years ago. Advanced metrics derived from complicated formulas have changed how teams and media at all levels analyze the game, and STATS’ advanced analytics team has made powerful contributions to that movement.

One of the tools at the forefront of this movement is STATS TVL. STATS TVL tracks pitch type (T), velocity (V) and location (L) to better explore pitchers’ efficiency and hitters’ approach at the plate. Analysis has gradually drifted from basic, easily calculated numbers like ERA, batting average, home runs and RBIs. And despite teams’ most recent foray into deep data for performance improvement and conditioning strategy, this evolution has been largely influenced by fans and media.

Baseball consumers crave the behind-the-score story, wanting to know the how and why that support those basic statistics. Media groups are not only looking to enrich their content, but they also vote for player awards. The outcomes of these awards are almost entirely determined by unique differentiators that are only detectable through advanced analytics. In that sense, every single pitch thrown either tells its own story or contributes to a greater narrative. STATS TVL not only provides in-depth and unique pitch-by-pitch data, but that data supports predictive element that allows media, fans and teams to look into the future of a player’s career.

Take, for example, this article from STATS following the Chicago Cubs signing Yu Darvish, which broke down Darvish’s numbers against left-handers last season by pitch type and usage. The NL Central is loaded with quality left-handed hitters, and Darvish’s second-half performance versus lefties was quite average. Using that data, combined with the TVL data showing those NL Central sluggers fared against the pitches Darvish throws most, STATS arrived at conclusions regarding how Darvish should approach particular hitters – and which pitches hitters should wait to see.

STATS TVL also powers STATS Video Solution (SVS), which allows teams, players and media to easily search video of any pitch thrown during an entire season with filters for particular pitcher-hitter matchups. By combining these products, STATS used TVL data and SVS video to help us understand how AL East aces should approach a revamped New York Yankees lineup that now features Giancarlo Stanton and Aaron Judge in the newest version of the Bash Brothers. Previous matchups were analyzed through pitch type and location to give a sense of what was successful for both the hitter and pitcher. One conclusion drawn from this level of analysis determined Stanton didn’t hit very well against sliders from right-handers. With Tampa Bay’s Chris Archer thrives on the slider and 19 regular season divisional match-ups against Tampa Bay in 2018, there’s a story building that fans would love to get their hands on.

But one story that got an extreme amount of attention this past offseason was MLB teams’ pursuit of young Japanese two-way star Shohei Ohtani, who eventually signed with the Los Angeles Angels. With STATS’ 600-league coverage span, TVL data was collected from the Nippon Professional Baseball league in Japan, allowing STATS to dive into Ohtani’s pitching performance as well as his approach at the plate. From the mound, which pitches did Ohtani throw the most and how effective were they? What percentage did hitters swing and miss on each pitch? How many of each pitch ended up outside the strike zone? From the plate, STATS TVL data showed Ohtani sat on fastballs and crushed them to the tune of a .353 average throughout his Japanese career. But when he faced a splitter?  A meager sub-Mendoza .191. SVS video from his at-bats showed how Ohtani turned on an inside heater, but rolled over on a splitter. The hype around Ohtani paired with the lack of domestic historical data begs for this level of analysis. What will we see when this rumored phenom take to the field for the first time in the MLB? The fans demand prediction, and TVL delivers just that.

So whether you’re looking to carefully examine the impacts of a divisional shift, the potential of a league new-comer, STATS TVL delivers the innovative unique data points media groups need to carefully and accurately analyze baseball in the way the fans crave.

Analyzing the NCAA Tournament’s ACC Powers Using STATS SportVU


Questions arise annually for the NCAA Tournament selection committee when the bracket is revealed on Selection Sunday. No one ever is fully happy with the results when bubbles burst, and those who received a bid often wonder about their seed or where they’re headed on opening weekend.

The ACC doesn’t have much to complain about this year, though, tying the conference record set last season with nine teams in the tournament, the second-most ever for any conference. Virginia is the tournament’s No. 1 overall seed, North Carolina and Duke both landed on the No. 2 line, and a couple teams toward the bottom of the league standings snuck in, too.

During the ACC Tournament, STATS had its SportVU player-tracking cameras placed above the Barclays Center court and accumulated data during each game to deliver advanced analytics to the ESPN broadcast and fans watching or following on social media. Now, we’ll go even deeper into that data for the first time.

Below is a breakdown of SportVU data gathered during Virginia, Duke and North Carolina games and how those analytics could help or hurt each team heading into the NCAA Tournament. We’ll also delve into some KenPom.com metrics from the mind of Ken Pomeroy, who receives his statistics from STATS’ industry-leading data feeds to power his proprietary model and deliver the most respected college basketball advanced metrics in the game.

According to KenPom, Virginia has the second-best chance of winning the national championship at 17.6 percent, with Duke third (12.1) and North Carolina sixth (5.7).

Virginia (31-2)

KenPom rating: 1
Seed: No. 1, South Region
First-round opponent: UMBC

If you haven’t heard overwhelming praise for Virginia’s defense by now, you wouldn’t be reading this. The Cavaliers rank No. 1 in KenPom’s adjusted defense rating with an average of 84.4 points allowed per 100 possessions. Virginia is dead last in the nation with an average of 59.1 possessions per 40 minutes, slowing the game down to a snail’s pace and forcing long, closely guarded possessions on the defensive end. Does that mean the Hoos can’t score? Absolutely not.

The Cavaliers are 21st nationally in adjusted offense with an average of 116.5 points per 100 possessions. They balance their attack with only three guys averaging double figures in Kyle Guy (14.1), Devon Hall (12.0) and Ty Jerome (10.5) with a very deliberate offensive scheme.

Just how deliberate? During the Cavaliers’ three ACC Tournament games tracked by SportVU, they threw 0-2 passes on 15 percent of possessions, 3-5 on 41 percent, and 6+ on 44 percent. Virginia averaged 1.00 points per possession with 0-2 passes, 1.04 when throwing 3-5, and 1.31 points with 6+ passes.

For comparison’s sake, during Duke’s two ACC Tournament games, the Blue Devils threw 0-2 passes 45 percent of possessions and 6+ only 16 percent. Virginia is patient when it comes to shot selection, constantly setting screens to free up an open man.

During its three victories to win the ACC Tournament – including a win over the Tar Heels in the title game – Virginia led all teams with an average of 1.28 points per possession when setting a screen. It set 46 screens for Jerome according to STATS SportVU tracking, resulting in an average of 1.43 team points per possession. SportVU also compiles who sets specific screens, and the list below shows Virginia’s top three combinations involved Jerome during the ACC Tournament (click to enlarge).

A No. 1 seed has never lost to a No. 16 seed in the NCAA Tournament, and Virginia’s body of work suggests UMBC doesn’t stand a chance. Some might say UMBC needs to get extremely lucky. It so happens KenPom defines luck in college basketball as “a measure of deviation between a team’s actual winning percentage and what one would expect from its game-by-game efficiencies.”

Which team ranks No. 1 in KenPom’s Luck Rating? UMBC. And that luck seems to be kicking in already. Virginia announced Tuesday that ACC Sixth Man of the Year De’Andre Hunter suffered a broken wrist in the conference tournament and will miss March Madness.

If Virginia can overcome the UMBC luck factor and Hunter’s injury, it’ll still have to survive a bracket featuring No. 2 seed Cincinnati, third-seeded Tennessee and fourth-seeded Arizona, which seems awfully low for a team with a talent like Deandre Ayton.

Duke (26-7)

KenPom rating: 3
Seed: No. 2, Midwest Region
First-round opponent: Iona

The preseason No. 1 team is back to being a national title favorite despite finishing just 2-2 down the stretch. The Blue Devils certainly appear to have the most talent with freshman Marvin Bagley III – who is No. 3 in KenPom’s national player of the year standings – and senior instigator Grayson Allen, along with a supporting cast that features lottery pick Wendell Carter Jr.

Duke is one of only two teams to finish in the top 10 in both KenPom’s adjusted offensive efficiency (third) and defensive efficiency (seventh), with Michigan State – the No. 3 seed in the Midwest – being the other. The Blue Devils can beat any team inside or out given their shooters (Allen, Gary Trent Jr.) and their front line (Bagley, Carter).

STATS SportVU tracked four of Duke’s games this season, with two at the ACC Tournament and one each at Indiana and at Wake Forest. In those games, Bagley and Carter combined for 81 close touches (originated from a pass within 12 feet of the basket), leading to 1.26 team points per possession. Carter’s free-throw rate (free throws made/field goals attempted) was 64.3 percent on close touches and he shot 78.6 percent after a close touch.

The inside domination doesn’t stop there. According to SportVU, Bagley shot 12 of 14 when contested at the rim by two or more defenders, and Carter went 4 of 5 in those same situations. With only a single defender, Bagley was 8 of 14 and Carter 7 of 10. Essentially, if the ball gets inside, forget about it.

And if Allen is on his game from deep, the opposition stands little chance – unless defenders can keep him moving. In Duke’s tracked SportVU games, Allen shot 5 of 15 (33.3 percent) on non catch-and-shoot 3s. When he caught and fired? A red-hot 8 for 13 (61.5 percent).

Allen is at his best when he doesn’t have to create. When taking zero dribbles, Allen shot 11 of 18 overall and 9 of 14 from the 3-point line.

As for Trent? He’d rather be on the move. Trent went 11 of 22 (50.0 percent) from the field when taking at least one dribble in Duke’s four games SportVU tracked. When he didn’t take a single dribble, he went 7 for 23 (30.4 percent) overall and 4 of 17 (23.5 percent) from deep. Have a look at Allen’s and Trent’s numbers from the ACC Tournament (click to enlarge):

Bagley, Carter, Allen and Trent all played well in the ACC Tournament semifinals, but the Blue Devils ran into the Tar Heels, who came away with a 74-69 victory. Now the defending national champions are seeking a repeat.

North Carolina (25-10)

KenPom rating: 7
Seed: No. 2, West Region
First-round opponent: Lipscomb

A 10-loss No. 2 seed that finished sixth in its own conference with a home loss to Wofford on its resume? Crazier things have happened, even though North Carolina is 3-3 in its last six games. But a run to the ACC Tournament title game convinced the committee big things are in store for the Heels.

Theo Pinson, Luke Maye – who is No. 9 in KenPom’s national player of the year standings – and last year’s NCAA Tournament’s Most Outstanding Player Joel Berry II are the nucleus from a title team looking to make another run. And since the Tar Heels ranked 104th in the nation in 3-point percentage, they’ll go as far as their attack-the-hoop game takes them.

In North Carolina’s four ACC Tournament games tracked by SportVU, Pinson scored 16 points on a team-high 17 drives, which are defined as touches that start at least 20 feet from the basket and end within 10 – excluding fast breaks. Those attacks led to 26 team points, more than any other UNC player who drove to the hoop in Brooklyn.

But Berry and Pittsburgh graduate transfer Cameron Johnson had success, too. Have a look below at the team points per drive for each player, according to SportVU (click to enlarge):

All three teams have their strengths, but anything can happen during March Madness. STATS SportVU gave us a deeper look into ACC powers, and the advanced metrics listed above are just a sliver of the data collected during the conference tournament.

Imagine if we could evaluate every college basketball team the same way with SportVU player tracking.

South Side Surge: Yoan Moncada Ready to Break Out for White Sox


Complete teardowns are the new hip thing to do in professional baseball, with the Chicago Cubs and Houston Astros winning the last two World Series after years of rebuilding the farm system through trades and high draft picks.

The Chicago White Sox seem to be one of the next teams in line to break out, with a number of prospects acquired via trade or the draft who are almost ready to contribute on the South Side. Outfielder Eloy Jimenez, acquired last season from the Cubs in the Jose Quintana trade, is the No. 4 overall prospect in baseball according to MLB.com. There is Lucas Giolito, a former No. 1 overall prospect, acquired in the Adam Eaton trade with Washington.

Seven White Sox prospects are littered across MLB.com’s Top-100 ranking this spring, but possibly the most-hyped prospect of all gave up his rookie status last year with the White Sox — Yoan Moncada.

Moncada, acquired from the Boston Red Sox along with No. 10 overall prospect Michael Kopech in the Chris Sale trade, was himself a former No. 1 overall prospect out of Cuba. However, his .231 average and .753 OPS last season leaves much to desire from the young second baseman.

Moncada made his White Sox debut on July 19 last year, and proceeded to slash .105/.261/.263 for the rest of the month. He was better in August, slashing .238/.368/.413 in 18 games, but he was downright good in the final month of the season, slashing .305/.374/.500 over his last 20 games with four home runs and 10 RBI.

His final month upped his season WAR total to 1.9, although he only played in one-third of the White Sox’s games.

What adjustments did Moncada make to his game during the second half of his season? For one, he started by hitting the fastball better, according to STATS TVL data, which tracks pitch type (T), velocity (V) and location (L). He didn’t register a single hit off four-seam fastballs in July, and only one on a two-seamer. He upped that total number to 11 in August, and in September/October, he collected 18 hits off four- and two-seam fastballs.

That success came partly because Moncada was seeing the fastball better later in the season. Moncada swung and missed at 33 percent of four-seamers from righties in July, but improved to 26 percent in August and 28 percent in September. After swinging and missing on 20 percent of two-seamers against righties, he didn’t swing and miss a single time in August or September. And after registering a 20 percent swing-and-miss rate against lefty four-seamers in July, it dropped to 8 percent in August, and he didn’t miss a single one in September.

Here is a clip using STATS Video Solution showing Moncada getting overpowered and swinging through a four-seamer up in the zone in July, followed by a clip of him all over the same pitch in September, both off Kansas City’s Ian Kennedy:

It wasn’t just the swing-and-miss rate that improved for Moncada, though. A 55 percent groundball rate in July turned to 51 percent in August and 41 percent in September. That led to a higher hard-hit rate, which naturally leads to more extra-base hits, a higher slugging percentage, and more production overall.

Moncada had a 32 percent hard-hit rate in July, which is a respectable number (for reference, Francisco Lindor had a 33 percent hard-hit rate last year). But in August it bumped to 37 percent, and in September it moved to 38 percent. Overall, Moncada registered a 37 percent hard-hit rate last year, the same as Houston phenom Carlos Correa.

Obviously, Correa and Moncada are not the same hitter at this point in their careers. Correa hit 84 points better than Moncada last year, although their batting average on balls in play was only 27 points different. That boils down to how much Moncada struck out.

While the league-wide strikeout rate was just over 24 percent in 2017, Moncada struck out in 37 percent of his official at-bats. Just like with his swing-and-miss rate, though, he was trending in the right direction as the season got older. After striking out in 43 percent of his at-bats through the end of August, he lowered that number to 31 percent in September.

The combination of three things – making more contact, hitting the ball harder when he did make contact, and hitting the ball in the air more – led to a .818 OPS in September, which included five home runs and 11 RBI. Not-so-coincidentally, the White Sox had a 15-14 record the final month of the season after heading into September at 52-80.

If Moncada carries that production into his first full season with the White Sox, he’ll cement himself as a cornerstone of the franchise’s rebuild.

How STATS Helps Digital Media Outlets Enhance MLB Coverage


Likening the length of a Major League Baseball season to a marathon wouldn’t nearly do justice to the year-round sport. A six-month regular season, 40+ days of nail-biting playoffs, trade-rumor stacked Winter Meetings, and pitchers and catchers reporting mid-February for a six-week spring training just to do it all over again? Running 26.2 miles over a few hours suddenly feels like a walk in the park (or at least to first base).

Needless to say, the chance to captivate a country by way of America’s pastime is nearly a year-round opportunity chock full of thrilling milestones, record-breaking contracts, and tear-jerking sports magic moments. What’s daunting for digital media outlets, broadcast partners and fantasy sports companies is figuring out how to provide quality and unique coverage during MLB’s seemingly endless, expansive campaign.

MLB plays roughly 1,200 more games per season than any other North American major sport on a nearly daily basis, requiring every outlet to reach their customer base with essentially zero down time. And unless you’re outfitted with a full staff of data collectors, researchers, analysts, and writers, this massive opportunity quickly looks more like an expensive, nearly impossible task.

Enter: STATS. Only the correct data provider can help simplify the delivery of advanced analytics, real-time live updates, historical data, quality research and in-depth fantasy notes to fans of all 30 MLB teams. That’s where STATS’ MLB coverage steps up to the plate.

STATS is MLB’s official data provider and has a full slate of products that captures every moment during and surrounding the 2,430-game regular season before the month-long playoffs, ranging from the most minor of transactions to record-breaking player and team achievements. Whether using STATS PASS’ historical database tracking statistics back to 1876, or accessing unique advanced metrics through TVL data and STATS Video Solution, digital media outlets can fulfill any need necessary to satisfy fans’ MLB cravings.

And for the fans who want their baseball information in real-time? STATS’ data feeds provide not only some of the fastest but also most accurate in-game updates in the industry, which Northwestern University validated through a STATS-commissioned study. By tracking every pitch and producing the correct outcome within seconds, STATS is far and away the industry leader in delivering clients the tools necessary to set themselves apart during the MLB season.

The value of such high-quality, real-time data even extends to fantasy games and the outlets hosting a variety of leagues and contests. Fantasy baseball requires daily attention to player news and updates, injury statuses and performance trends to build the best collection of MLB stars. STATS’ serves digital media companies with daily, automated player notes and headlines through STATS Hosted Solution and up-to-the-minute in-game insights with STATS Widgets. It’s never been easier for clients to present relevant information in such an efficient manner.

That’s especially important for fans who have money on the line in fantasy baseball contests. STATS API produces award-winning Daily Fantasy Sports (DFS) projections available to digital media outlets looking to serve die-hard DFS players. Three times STATS has ranked No. 1 for most accurate baseball projections by FantasyPros and the Fantasy Sports Trade Association (FSTA).

Keeping focused throughout the long MLB season can be challenging and often requires a plethora of support tools to satisfy customers craving baseball data. STATS’ full suite of MLB products lightens the load for digital media companies and gives fans the means necessary to remain informed regardless of their needs.

Have a look at the MLB products STATS offers at www.stats.com/mlb.

STATS’ Advanced Analytics Featured in Sirius XM NFL Radio Interview


STATS’ advanced analytics team is constantly developing new metrics to enhance player evaluation and team performance in college football and the NFL, and those efforts received more national recognition at the NFL Combine.

STATS’ Manager of Advanced Analytics Kyle Cunningham-Rhoads appeared as a featured guest on SiriusXM NFL Radio show Late Hits with hosts Alex Marvez and former college and NFL coach Rick Neuheisel to discuss STATS’ unique advanced metrics and its new product, X-Scout. During the nearly 17-minute interview, Cunningham-Rhoads explained some of the science and research behind a new wave of analytics changing player evaluation at all positions.

“Everyone has access to data, whether it’s the tracking data that comes from the NFL or the third-party data like STATS provides,” Cunningham-Rhoads said during the interview. “The difference we can make is we help teams get the most out of that data through all of these metrics.”

Cunningham-Rhoads and his team have made revolutionary strides in player evaluation, including efficiency metrics for offensive and defensive line and cornerback positions. STATS’ new X-Scout product is a result of game-tracking data – such as pressures allowed, pressures forced and defensive targets, among many others – that powers a model producing an efficiency metric based on a player’s position.

X-Scout will assist NFL teams as they lead up to the draft and player-selection process, while also helping clubs evaluate college players during the season. During the Sirius XM interview, Marvez and Neuheisel asked which cornerback STATS rated the best heading into the 2017 draft, and Cunningham-Rhoads said LSU’s Tre’Davious White, who was the fifth cornerback selected, and 27th overall by Buffalo. White validated that lofty ranking by tying all rookies with 18 passes defended and also intercepting four passes.

“We’re by no means saying, ‘You should pick this player,’” Cunningham-Rhoads said regarding X-Scout. “It’s more like, ‘Hey, here’s a list of guys you should be looking at, and here’s how they might fit into your existing system. X-Scout is not replacing the value of the scout, but rather augmenting their skills, and making it possible to see so much more than basic stats well in advance of the combine.’”

Marvez and Neuheisel also pointed out Las Vegas oddmakers didn’t have much confidence in Philadelphia to win the Super Bowl, listing the Eagles with only 40-1 odds to be crowned champions. However, STATS’ combined roster evaluation metrics ranked Philadelphia as the No. 1 team heading into the 2017 season.

Age is Just Another Complicated Number for Bartolo Colon


Bartolo Colon started to show his age last season. Most major leaguers would when they’re just a couple months shy of turning 45. In fact, most major leaguers no longer call themselves major leaguers at that age.

Colon is an exception, and the wear and tear seems to be taking a toll. He was on the mound for 28 starts last season, and 33 the year before that. He was an All-Star in 2016 at the ripe age of 43 for the New York Mets, but sported a 6.48 ERA in 2017 between Atlanta and Minnesota.

Now, he’s in Texas Rangers camp trying to win a sixth spot in a rotation that has almost completely flipped from a season ago. On Opening Day 2017, the Rangers were slated to have Yu Darvish, Martin Perez, Cole Hamels, A.J. Griffin, and Andrew Cashner fill out the rotation. This season Hamels and Perez are the only holdovers, joined by newcomers Matt Moore, Doug Fister, Mike Minor, and the candidates for the sixth spot – Colon, Jesse Chavez, and Matt Bush, mainly.

If Colon wants to make the big league roster, he needs to prove he can manufacture the production he showed in 2016, and that he hasn’t finally fallen off with age, like 2017 might suggest.

What changed for Colon in 2017? Not much on the surface, according to STATS TVL data. His average velocity was the same, his pitch-mix didn’t fluctuate very much at all, and his strike percentage was roughly the same for all his pitches, along with his swing-and-miss percentage and chase percentage.

One major difference was Colon’s opponent’s batting average on balls in play – in 2016, it was .291, and in 2017 it was .331. That’s a significant bump, and it’s due mostly to guys teeing off on Colon’s two-seamer. In 2016, right-handed hitters slashed .266/.290/.378 off Colon’s two-seamer and lefties slashed .244/.284/.466. In 2017, righties slashed .352/.378/.557 and lefties slashed .349/.397/.627.

Colon threw it for a strike 70 percent of the time in both ’16 and ’17. His swing-and-miss percentage fell from 10 to seven, but that’s not major. The major difference was the fact that Colon missed inside the zone far more often in 2017 in critical counts than he did in 2016.

The two charts below show where Colon gave up hits on 0-1 counts with his two-seam fastball, from the pitcher’s perspective.

The chart on the left shows 2016. Colon gave up hits when he made mistakes in the zone (he almost always wants to paint the outside corner to righties with his two-seamer), but he did a good job minimizing those mistakes. In the chart on the right, 2017, he missed in the zone much more, and paid the price. Here are a couple videos pulled from SVS, the first from 2016 when Colon executed a 0-1 two-seamer to get Addison Russell to ground out, and the second from 2017, when he missed inside the zone and gave up a big fly to Ryan Braun.

He ran into the same mistakes against lefties on 0-2 counts in 2017. In 2016, lefties hit .167 and slugged .250 off 0-2 two-seamers from Colon. In 2017, they hit .429 and slugged .500 against the same offering. During Colon’s 2016 All-Star campaign, he didn’t give up a single hit inside the zone on 0-2 counts. Pitchers typically feel like they can waste a pitch or two in an 0-2 count trying to get hitters to chase, and Colon was no different. However, as you can see from the screengrab below, in 2017 he missed multiple times in the zone, and paid for it.

Colon got in trouble on full counts against righties, as well. In 2016, right-handed hitters hit .185 and slugged .259 against Colon’s two-seamer in 3-2 counts, and in 2017 they hit .389 and slugged .611 off him. Part of that is because Colon gave in to hitters more in 2017, and part of it is because he was up in the zone more this past year.

In 2016 on 3-2 counts when Colon threw two-seamers to right-handed hitters (these may seem like incredibly narrow parameters, but Colon threw a two-seamer 84 percent of the time against righties in a 3-2 count that year), he was in the strike zone 61 percent of the time. He was middle/up in the zone 43 percent of the time. In 2017, he was in the strike zone 71 percent of the time and middle/up in the zone 54 percent of the time.

The solace Colon can take from 2017 is that he found his changeup, and it turned into a good pitch for him after he used it only four percent of the time in 2016. He more than doubled his usage in 2017, and got better results.

In 2016, hitters slashed .367/.412/.600 off Colon’s changeup, but in 2017 slashed .243/.284/.443. And although he doubled his usage of the pitch, his strikeouts from ’16 to ’17 were five-fold.

Colon strikes out Elvis Andrus with a changeup in the following clip from STATS Video Solution. From the way Andrus’ head snaps back towards Colon after he swings and misses, you would think he had no idea Colon throws a changeup.

Colon may be wise to up his changeup usage even more this season to help keep hitters off his two-seamer, or else he may not find himself in the Rangers’ rotation come summer time.

Pick Your Poison: How AL East Aces Should Approach Judge and Stanton


American League East teams not located in the Bronx have a problem. It’s two problems, really, and they both stand at about 6-foot-6 and have over 500 pounds of pure muscle between them.

Aaron Judge and Giancarlo Stanton combined for 111 home runs last year, clearing outfield wall after outfield wall at a historic pace. They played their home games 1,300 miles away from each other, though. This year, they’re sharing a dugout.

That causes a problem for first-year Yankee manager Aaron Boone, though his is a good one. Where do you hit the new-age Bash Bros in the order?

If one follows the other, opposing pitchers won’t want to pitch to either of them. They wouldn’t be able to afford to pitch around both of them either, or they’re putting a guy into scoring position before bat meets ball. This kind of problem is the bad kind once again, and it falls back in the lap of opposing pitchers.

How do you choose who to pitch to, and who to tip-toe around? A lot of that has to do with matchups, of course. Some of it has to do with what you do well as a pitcher compared against what Judge and Stanton do well at the plate. STATS TVL projections – pitch type (T), velocity (V) and location (L) – take all of that into consideration (along with many other factors). We’ll take those projections for the 2018 season, and with an assist from STATS Video Solution we will go through which Bash Bro each of the projected Opening Day starters in the AL East should choose to attack.

Chris Sale, Boston Red Sox

For all the success Judge had last year, Sale owned him. The AL Rookie of the Year was 0 for 12 against Sale with 10 strikeouts.

The Boston ace pumped fastballs to opposing right-handed hitters about 50 percent of the time last year, and that was the case against Judge as well. Judge’s stat line against lefty fastballs was nothing to write home about: .203 AVG/.378 OBP/.469 SLG with five home runs, eight RBIs, and 22 strikeouts.

That’s probably why Sale finished off Judge with a fastball seven times. Watch Sale challenge Judge up in the zone twice before getting him to go down on strikes on July 15 of last year.

The 2018 projections against Sale are more favorable for Judge, though they still aren’t too flattering: .214 AVG/.462 SLG. He isn’t projected to hit higher than .187 against Sale’s four-seamer or slider.

Stanton has never faced Sale, though his numbers last season against lefty fastballs – .356/.465/.763 with six doubles, six home runs, 17 RBIs – make his 2018 projections against Sale much better than Judge’s. Stanton projects to hit .317 and slug .927 off the Red Sox lefty, while clobbering his slider to the tune of a .445 average and 1.675 slugging percentage.

Sale isn’t one to back off any hitter, but he would be wise to be careful with Stanton and attack Judge instead, just like he did last summer.

Chris Archer, Tampa Bay Rays

Archer has thrown against Miami twice in interleague play, so there’s a sample size for both Judge and Stanton against him.

Stanton didn’t figure Archer out in those two starts, going 0 for 6 with two strikeouts and no hard contact to speak of. In both at-bats that ended in a punchout (one in June 2014 and one in April 2015), Archer went slider-heavy. In that two-year span, Stanton hit .194 off righty sliders and slugged .453. Here is Archer getting Stanton on three straight sliders.

Those numbers haven’t gotten any better since. He raised his average to .214 against righty sliders, but his slugging percentage dropped to .373. For that, Stanton’s 2018 projections against Archer don’t project much confidence – .227 AVG/.477 SLG overall, and a .223 average and .412 slugging percentage off the slider.

Judge cracked a double off a hanging slider on the first pitch he ever saw from Archer in April of last year. It wasn’t a forecast of what was to come, however; since then, Judge has gone 0 for 8 with five strikeouts.

Like with Stanton, Archer’s slider has been the catalyst. Here is a video from SVS of Archer getting Judge on three consecutive sliders, just like he did with Stanton.

However, Judge’s 2018 projections against Archer don’t scream doom – .252 AVG/.593 SLG. That has a lot to do with the assumption Judge will eventually get fastballs to hit off Archer, as he’s projected for a .364 average and .953 slugging percentage off that Archer offering.

If Archer wants to steer clear of too much damage against the Yankees, he’s better off testing Stanton than Judge, and with a steady diet of sliders.

Marco Estrada, Toronto Blue Jays

Estrada has seen plenty (in our view and in his, probably) of Stanton and Judge, because of his stint in the National League with the Milwaukee Brewers.

Stanton is 4 for 11 with a double, three home runs, and six strikeouts against Estrada, but that’s a little skewed; Stanton struck out the first four times he faced Estrada. He has figured him out since then, and if you do some subtraction, you see he’s four for his last seven against him with those three home runs and the double.

Estrada is essentially a fastball-changeup pitcher against right-handers, leaning more on his changeup late in at-bats, and he’s more likely to start an at-bat with a curveball than he is to end one with it. That’s what’s gotten him into trouble with Stanton in their most-recent encounters.

Here he peppers him with three straight fastballs, before Stanton puts one over the center field wall in Miami.

Stanton’s 2018 projections against Estrada look similar to the moonshot in that video – .320 AVG/.745 SLG. His individual pitch projections (four-seam: .296 AVG/.678 SLG, changeup: .314 AVG/.779 SLG) don’t give him an avenue to get Stanton out, so he might be better off pitching around the slugger, if it weren’t for one detail – Judge’s projected numbers against Estrada are even better.

In Judge’s short career, he has already collected seven hits in 14 at-bats off Estrada, with two doubles, two home runs, three walks, and five RBI. That’s after starting his career 1 for his first 4 off him.

Six of his seven hits off Estrada have come on a fastball, which is why Judge’s 2018 projections off the Blue Jay righty scream to not throw him a fastball –  .385 AVG/.900 SLG overall and .439 AVG/1.080 SLG off the fastball.

Estrada fell behind in this at-bat against Judge (after, admittedly, a pretty good changeup) and was forced to throw a fastball. It ended up in the seats.

Choosing to pitch to either Stanton or Judge will be like pulling teeth for Estrada. The projections say less damage will be done by Stanton; common sense says tell the outfielders to move back either way.

Kevin Gausman, Baltimore Orioles

Gausman has suffered the same fate as Estrada against Judge, giving up six hits in 12 at-bats, including one double, three home runs, four walks, and five RBI.

The young Orioles righty was 65 percent fastball last year, which is appetizing for Judge, who hit .352 and slugged .819 off right-handed fastballs in 2017. Each of his three home runs off Gausman have been off four-seamers, including the one in the video below, which ended up about 10 rows deep in the center field seats.

However, if Gausman made the decision to go offspeed-heavy against Judge, he might have more luck. He struck Judge out three times with the slider in 2016, though Judge wasn’t the same player during his September call-up that year as he was in 2017. His splitter is his “out” pitch, and it projects well against Judge – .209 AVG/.341 SLG. He struck Judge out with a nasty one in this video in May.

His splitter really projects well against Stanton – .036 AVG/.241 SLG – and should give Gausman some clarity on who to attack between the two 50-home run sluggers. Stanton has struggled with the splitter in his career, hitting just .143 on the offering in 2017 with a 59 percent swing and miss rate. Overall, Stanton projects to hit .209 and slug .511 off Gausman, who he has yet to face in his career.

Each of these pitchers made Opening Day starts last season, except for Sale (when the Red Sox gave the nod to reigning Cy Young winner Rick Porcello instead), and they all could very well take the mound on Opening Day again this season. These are some of the best pitchers the AL East has to offer, and to see what Judge and Stanton have done to them in the past and what they are projected to do against them in 2018 should raise the eyebrows of every member of every pitching staff in the division and beyond.

To make matters worse, they might seem them back-to-back, and if they aren’t careful, they might see them trot around the bases back-to-back.

The What-If and Then-What of MLB’s Lingering Free Agents


MLB free agency is taking its sweet time to play out, so we went ahead and did it for them by placing remaining big names on a rumored team and further evaluating with STATS TVL data and STATS Video Solution.

Pitchers and catchers have reported. We made it. Finally, there is more to report on than the slow offseason. But those high-end free agents not yet signed are still going to dominate headlines until they find a middle ground with a team on a contract.

The slow offseason hasn’t stopped rumors from slipping out, however, and industry experts have tried their hands in predicting where some of those top free agents might end up. So for now, all we can do is take those predictions and project how those free agents might be of impact for those teams.

The outline of this piece will include an oft-rumored landing spot for each free agent, and counterparts he has both done well and not-so-well against in his career, with regular season stats included.

J.D. Martinez to the Boston Red Sox

Martinez and the Red Sox have been linked all offseason. It would make sense: The Red Sox were 27th in MLB and last in the American League in home runs in 2017. Martinez hit a home run once every eight at-bats with Arizona last season.

If Martinez does sign with Boston, it will be in an effort to keep pace with the New York Yankees. The former Detroit Tigers slugger has hit .337 lifetime against the Yankees, but with mixed results versus two of New York’s starters.

Martinez vs. CC Sabathia: 2 for 8 (.250)/.333 OBP/.250 SLG/1 RBI/2 K

Sabathia has done a good job of keeping Martinez off balance, with a mix of cutters in on his hands and changeups falling out of the zone away.

Busting Martinez in on the hands can be tricky business, especially if the pitch catches too much of the plate, but Sabathia has avoided that.

The big lefty has taken two different approaches the two times he has cut Martinez down on strikes. On April 9, 2016, he continually tried to get in on his hands with this sequence: cutter, cutter, two-seam, cutter, cutter.

A year earlier on April 20, 2015, Sabathia got Martinez swinging, coming nowhere close to his changeup. That sequence went like this: changeup, four-seam, changeup, four-seam, two-seam, changeup. Martinez whiffed on all three offspeed offerings.

UPDATE: J.D. Martinez signed a five-year deal with Boston on Feb. 19

Martinez vs. Masahiro Tanaka: 5 for 9 (.556)/.556 OBP/1.444 SLG/2 2Bs/2 HRs/3 RBI

There’s no gentle word to use when describing what Martinez has done to Tanaka pitches in his career.

It’s not for lack of trying on Tanaka’s part. He throws everything but the kitchen sink at Martinez. In that regard, it’s hard to determine exactly why Martinez has had so much success against Tanaka.

Martinez had two doubles off Tanaka on April 23, 2015. In his second at-bat, Tanaka started him off with a slider, then went with splitter, cutter, splitter, cutter, slider. Martinez doubled to left field.

His next at-bat, Martinez jumped on a first-pitch splitter. He doubled to left field.

Two months later, Martinez hit home runs in his first two at-bats off Tanaka. His first home run was off a slider, two-seamer sequence. The next was on a first-pitch curveball. There’s something to saying a hitter just sees a certain pitcher really well.

Martinez sees Tanaka really well; another good reason for the Red Sox to sign him.

Lance Lynn to the New York Yankees

There hasn’t been a ton of steam behind any Lynn rumors, but after Yu Darvish landed with the Chicago Cubs, there have been reports that he could be a lower cost Plan B for the Yankees.

As a Yankee, he would be judged on how well he pitched against the Red Sox. Here is how Lynn has done against two of Boston’s lineup mainstays.

Lynn vs. Hanley Ramirez: 2 for 10 (.200)/.273 OBP/.300 SLG/1 2B/1 BB/4 K

There’s the scene in the Dennis Quaid movie “The Rookie” after he makes his major league debut and a reporter asks him what pitches he threw. Quaid responds with, “fastball…fastball…fastball.” That’s Lynn every single start. In 2017, nearly 82 percent of the pitches he threw to right-handed hitters were four-seam fastballs. That’s a higher percentage than R.A. Dickey threw his knuckleball. Add in his cut fastball, and that’s 97 percent of the pitches he throws.

Ramirez hit just .249 off fastballs last season, which would bode well for Lynn in pinstripes.

Lynn vs. Mookie Betts: 3 for 5 (.600)/.500 OBP/1.200 SLG/1 HR/3 RBI

Betts hit .291 off fastballs last season. The home run Betts hit off Lynn on May 16 of last year was off a four-seam fastball.

There isn’t a lot of mystery with Lynn. He throws his four-seamer to his glove side and his two-seamer to his arm side. Wash, rinse, repeat. If he’s hitting his spots, it’s effective. When he misses to good fastball hitters, like Betts, he pays for it.

Jake Arrieta to the Washington Nationals

The Washington Nationals’ World Series window is closing fast. Bryce Harper is a free agent after this coming season. There are more rumors about him leaving than about him staying. Ryan Zimmerman, a mainstay in the organization, is on the back nine of his career. In fact, probably on hole 15 or 16.

Behind Max Scherzer and Stephen Strasburg, the rotation gets a little shaky. So why not go all in on this season and sign Arrieta?

If they do sign the 2015 Cy Young winner, it won’t be to fend off any other NL East teams. No one else in the division looks to be in the same class as the Nationals. It will ultimately be to make it out of the National League and into the Fall Classic, which means going through the class of the NL, the Los Angeles Dodgers.

Arrieta of course has thrown a no-hitter against the Dodgers, in 2015. However, the Los Angeles lineup has been almost completely turned over, as Joc Pederson and Yasmani Grandal are the only players slotted to start on Opening Day that were also in the lineup on August 30, 2015 against Arrieta.

If Arrieta would sign with Washington, it would be the young guys – Corey Seager and Cody Bellinger – he would need to get out.

Arrieta vs. Corey Seager: 3 for 5 (.600)/.667 OBP/.600 SLG/1 BB/1 K

All three of Seager’s hits off Arrieta are singles, so he hasn’t done huge damage, but Arrieta still hasn’t been able to figure the young shortstop out.

Arrieta’s success ultimately comes down to control. If he can put his two-seamer where he wants, he dominates. Against Seager, he has continually missed inside the zone, and he has paid the price.

Arrieta uses his two-seam differently than most right-handers. Instead of mostly using it arm-side, he likes to run it in on lefties and bring it back across the inside of the plate. Seager got to one of those inside two-seamers on May 26 of last year when it leaked back out over the plate.

Arrieta vs. Cody Bellinger: 0 for 2/1 K

Sample size is small here, but it shows how dominant Arrieta is when he has his control of all his pitches. In that May 26 game, Arrieta got Bellinger swinging at a curveball in the dirt for strike three and was completely in control.

The same was the case when Arrieta got Bellinger swinging in the NLCS, when he kept his two-seamer arm-side.

Of course, Bellinger got Arrieta in the third inning of that game when Arrieta tried to bury a slider on Bellinger’s back foot and left it out over the middle of the plate, and Bellinger put it in the right field bleachers.

Alex Cobb to the Minnesota Twins

The Twins desperately need pitching. Behind Ervin Santana and Jose Berrios, the starting rotation is extremely thin, and Santana is out at least 10 weeks with a finger issue.

After surprisingly making the postseason last year, there’s more expectation in 2018. To do that, the Twins will need to go through the Cleveland Indians, who they were 7-12 against last season.

Cobb would see a lot of two lefties that are always at the top of Cleveland’s order.

Cobb vs. Jason Kipnis: 2 for 10 (.200)/.333 OBP/.300 SLG/1 2B/2 BB/1 K

Cobb has liked using his curveball against Kipnis in his career, which is in line with how he typically attacks hitters. In 2017, Cobb used his curveball 31 percent of the time to righties and 38 percent of the time to lefties.

The crafty right-hander has mixed his pitches against Kipnis as well, something he hasn’t necessarily done against Jose Ramirez.

Cobb vs. Jose Ramirez: 2 for 5 (.400)/.333 OBP/.600 SLG/1 2B

Cobb has shown a tendency in the past to be splitter-heavy against Ramirez, a pitch he threw only 20 percent of the time to left-handers last year.

In the at-bat Ramirez doubled off Cobb in September 2014, four of the six pitches he threw were splitters, including the last one.

Cobb was a lot more reliant on his splitter back then, throwing it 40 percent of the time to lefties in 2014. After coming back from Tommy John surgery that consumed the 2015 and most of the 2016 campaign, he has been less reliant on the pitch.

Jonathan Lucroy to the Washington Nationals

Continuing on the Nationals’ World Series window train, the most obvious hole on the roster is behind the plate.

Last season, Matt Wieters played in 123 games for Washington, hit .225 with a .632 OPS. His WAR was -0.5.

Although Lucroy is coming off a down year, he would be an upgrade.

As with the Arrieta projection, we will look to how Lucroy has performed against the Dodgers.

Lucroy vs. Clayton Kershaw: 1 for 20 (.050)/1 2B/4 K

To be fair, not many hitters have success against this Kershaw guy. But Lucroy’s career average against him is well below Kershaw’s .206 career batting average against.

It’s been the slider that has tripped Lucroy up against Kershaw. The Dodgers ace is one of the best in the game at burying a slider on a right-hander.

Typically for a hitter, it’s advantageous the more you can see a pitcher. In Lucroy’s case, he may not plead for more plate appearances against Kershaw.

Lucroy vs. Alex Wood: 2 for 5 (.400)/2 BB

Although Lucroy spent time in Colorado last year and most of his career in the NL, he doesn’t have a large sample size against any Dodger other than Kershaw.

He has had success against Wood, though, reaching base four times in seven at-bats.

Wood is a no-nonsense pitcher as far as repertoire (fastball, curveball, changeup), but he employs a herky-jerky motion that is deceptive. The two times Lucroy has gotten to Wood have been when he missed a spot out over the middle of the plate.

Yu Darvish Has Much to Prove vs. Lefty-Loaded NL Central


The MLB’s cold-stove offseason upgraded to at least lukewarm Feb. 12 when the Chicago Cubs and top free agent target Yu Darvish closed on a potentially $150 million deal. That could wind up being a bargain for the game’s preeminent strikeout pitcher over the last five seasons when considering the usual cost of quality starting pitching.

The Cubs now can rest easy with their rotation set heading into spring training, as Darvish’s signing officially signals the end of Jake Arrieta’s tenure on the North Side. The basic numbers support the Cubs’ investment that provides them with a solid top end of Darvish, Jon Lester and Jose Quintana in some undetermined order.

But STATS TVL data – tracking pitch type (T), velocity (V) and location (L) – is anything but basic, and there should be some cause for concern with the majority of Darvish’s 2018 starts set to come against NL Central lineups loaded with left-handed hitting options due to the unbalanced schedule. The projected numbers combined with Darvish’s history against lefties might cause some Cubs fans to tone down the optimism a little.

Chicago led the NL with a .285 batting average on balls in play (BABIP) against lefties in 2017, and Lester led all MLB starters striking out 34.1 percent of the left-handers he faced. Darvish, whose 11.04 strikeouts per nine innings is the highest rate of any pitcher since he made his MLB debut in 2012, finished 19th punching out 26.9 percent of lefties he faced between his time with Texas and the Los Angeles Dodgers.

That’s a solid rate and not far below the 27.6 percent of right-handers Darvish fanned. But when left-handers put bat on ball? Not as good. Darvish ranked 140th in MLB with a .328 BABIP against lefties compared to a .241 BABIP vs. right-handers that was the sixth lowest in baseball. Despite left-handers having trouble against his slider, Darvish threw his four-seam fastball at a higher usage rate than any of his other pitches.

The results? Well, have a look at how lefties fared against Darvish in the second half:

Of the seven players with at least nine career hits off Darvish, six either bat left-handed or are switch hitters. The outlier is Mike Trout, whose four homers are tied for the most against Darvish with left-handers Brett Gardner and Brandon Moss.

According to Roster Resource, three of the Cubs’ four NL Central foes project to have at least half their non-pitcher spots in the lineup occupied by left-handed hitters. The Reds project to have the most with six, and Darvish might want to refrain from throwing his four-seam fastball against majority of them. TVL data projects Joey Votto to hit .464 against Darvish’s four-seamer with Billy Hamilton (.443) and Scooter Gennett (.427) not far behind.

The Brewers project to start five lefty hitters, and among current NL players with the most hits off Darvish, Stephen Vogt (seven) and Eric Sogard (five, tied for second) are at the top of the list. Milwaukee added another solid lefty bat in Christian Yelich, who came over from Miami in an offseason trade. Yelich is 2 for 5 with a homer and two walks in his career against Darvish, and most of those at-bats are recent.

Yelich has a .471 projected average against Darvish’s four-seamer, and he took that pitch deep in their first-inning matchup July 26 of last season. Using STATS Video Solution, that specific at-bat is easy to find with just a few clicks.

You’ll note in the graphic above that lefties hit only .161 against Darvish’s slider in the second half last season. Darvish didn’t take any chances with the four-seamer after Yelich’s homer and retired him on a slider in the third.

That slider should be used often against Matt Carpenter, according to TVL data. Carpenter, who is one of three Cardinals lefties in their projected lineup, has never faced Darvish, but is projected to hit just .116 against his slider. Everything else? .401 against the four-seamer, .395 against the curve and .330 when facing a two-seamer.

Josh Bell and Gregory Polanco are the only Pirates lefties guaranteed to be on the major league roster, with Polanco going 0 for 2 in his only previous matchups with Darvish. Bell projects to hit Darvish’s four-seamer at a .418 average, but despite what’s on paper, it doesn’t mean the Cubs made a mistake in bringing Darvish to the NL Central.

There’s plenty of good in Darvish’s arsenal and he’ll make a difference in one of the majors’ best rotations. He’ll just need to be especially careful with pitch selection in the lefty-loaded division.

The Last Line of Defence, the First Line of Attack


Ederson and Alisson are said to be transforming goalkeeping, often without even handling the ball. Here we assess the distribution of the Brazilian duo with STATS Playing Styles, Ball Movement Points and Tier 6+ data, which might lead us to question which of the two is doing more to ambitiously deliver the ball to their teammates’ feet. It also might lead us to consider a lesser-known name or two in their own leagues.


Ederson says he could play midfield if Manchester City lose any more players to injury. Alisson was recently referred to as the Messi of goalkeepers.

Both are known for, among other traits, their distributive quality, and much of that appreciation is heaped upon the Manchester City man after Pep Guardiola paid a substantial fee to pry him from Portugal. But how can we measure this beyond the eye test of Ederson lofting a precise ball over an opposing player in a high position to Leroy Sané’s boots? How do we do so meaningfully in a way that goes beyond successful passes? How can we reward ambition? And if we can do that, how do we do it objectively and in ways clubs like Roma can use to properly valuate Alisson as demand rises? And, conversely, how can clubs rumoured to be interested, such as Liverpool, use it to properly go about player recruitment?

In STATS Playing Styles, we have our ways. Is Ederson really reinventing the position? Or is he drawing more attention than a compatriot who does his job similarly but without quite as much flair?

We’ll start by saying, yes, Ederson is an exceptional distributor, and we’ll show you the proof in a moment. World-class keepers such as David De Gea and Jan Oblak aren’t going to match the specific passing accuracy and ambition you’re about to see that validates what Guardiola saw in the former Benfica man.

But he might not be one of a kind on the level he’s often talked about. In fact, he might not even be Brazil’s best distributing keeper. Few will bother contending Ederson is of Alisson’s quality in the traditional sense between the sticks, and we’re going to see here that Alisson might also hold certain advantages with his feet.

We’ll start with the basics, where Ederson shows some slight advantages.

(Graphics by Stephan van Niekerk)

You see here that Ederson completes a higher percentage of his overall and forward passes, but as we go up the pitch, that’ll change. That said, both of these guys are exceptional with their feet. For a little context, no other starting Premier League keeper completes more than 66.9 percent of forward passes (Tottenham’s Hugo Lloris). No other Serie A keeper tops 73.6 (Inter’s Samir Handanovic).

Checking in on other world-class keepers, Bayern Munich’s Manuel Neuer has been injured this season but came in at 70.5 percent last season. Atlético Madrid’s Jan Oblak is only completing 31 percent of forward passes this season, down from 44.4 in 2016/17, but he tends not to play short balls, which transitions us into a new level of specificity with our two main subjects.

At least some of Ederson’s success, it turns out, probably has something to do with the distance he attempts.

We see here that, despite Ederson attempting more passes into the final third, Alisson distinguishes himself notably more up the pitch. He attempts more long passes (beyond 34 metres) and is considerably more successful with them. For context, Oblak has attempted 340 long passes but only connected on 34.1 percent with 49 toward the final third at 14.3 percent. In the Premier League, De Gea frequently sends the ball long (435 attempts) but more often than not misses (40.5 percent success), and there’s a similar trend with him going toward the final third (98 attempts, 20.4 percent). AC Milan’s Gianluigi Donnarumma, the emerging benchmark for the next generation of Italian goalkeeping, attempts fewer long passes (281) and completes 51.2 percent while connecting on 27.3 percent of 22 attempts to the final third.

So the Brazilians in question tend to be judicious with distribution, and they convert a higher rate of long passes – often far higher. We’ll see below how this results in a higher net oBMP, a key category in STATS Ball Movement Points.

BMP considers ball movement made by an individual player from a start zone to an end zone and assigns value based on past results from massive amounts of league data. These scores accumulate during a match or across a season to indicate the value of a player’s ball distribution. BMP considers every involvement a player has to credit or discredit decisions with the ball and reward creativity. It’s what football minds could always see but never calculate. It goes beyond expected assists by looking at the full chain of passes, weighing the probability of that pass leading to a shot later in the play. Passing points generate expected shot points, so if a player generates one BMP, he has generated passes to lead to or defend one shot. It expresses the level of threat or wastefulness that can be attributed to a player. It’s broken down into categories of offensive and defensive as well as positive and negative (oBMP+, oBMP-, dBMP+, dBMP-) with net values telling the more conclusive story.

We’ll get back to BMP momentarily. First, one last chart to get us there.

Let’s start at the end with team points earned, where each is contributing at a higher rate than is expected. But if we look into straight goalkeeping for a moment, Alisson distinguishes himself as the clear-cut No. 1 for Brazil heading into that important tournament they’ve got in Russia this summer. Both keepers have been in goal for all of their club’s league goals and faced all of their club’s league shots this season. Ederson has allowed 20 goals with Man City’s expected goals against coming in at 19.9, so with him in goal, they’re right at what’s expected of a league average. Alisson has Roma conceding far fewer than expected, which corroborates the traditional keeper value he’s perceived to hold over Ederson.

As for the subtle things both keepers do very well, the counter attack is interesting to consider when properly contextualised. Only Jordan Pickford (1,374.8 metres) leads Ederson in the Premier League in counter distance passed. He leads by a significant margin, which initially seems interesting from Pickford’s perspective because STATS Playing Styles shows us Everton counter considerably less (minus-9 percent of the league average) than City (plus-23 percent). But Pickford goes long with the ball 70.9 percent of the time, and not very effectively. He’s attempted 619 long passes at a 35.4 percent success rate.

Alisson leads Serie A keepers in counter distance passed while only one other keeper has reached the 600-metre mark, which probably speaks to the differences between the varieties of football typically played in Italy and England. But this shared ability of Ederson and Alisson to get a counter started means they’re frequently sparking transition play, which in Playing Styles is differentiated from direct play with various distinctions.

Another interesting keeper to consider here is Kasper Schmeichel, who leads us back into BMP. At first glance, his 46.9 percent forward-pass success seems horrible. But his long balls (642 attempts) account for 71.7 percent of his passes, and he’s been better at it (42.1 percent success) than Pickford and De Gea. That contributes to the Premier League’s third-best oBMP for a keeper (0.39). It’s still not at the level of Alisson and Ederson, who are among the most dangerous keepers in the world in terms of creative threat.

Schmeichel also leads the top-five European leagues with a 1.29 dBMP, and by no small margin, meaning he’s disrupting attacks more than any keeper.

With Ederson second in the Premier League in oBMP, who’s first?

Burnley’s Nick Pope (0.53).

It’ll make sense after addressing the numbers all the way though. He completes only 42.9 percent of his passes, but he goes long 82.1 percent of the time (633 attempts). He completes those long balls at a rate of just 34 percent, but he’s sent 329 toward the final third at a rate of 33.1. In other words, his success rate entering that area of attack is on Ederson’s level with roughly nine times the attempts. He’s connecting ambitious passes in dangerous areas on the pitch. That’s resulted in four first assists to shots – three more than Ederson and Alisson combined – and two second assists to shots.

He leads Europe’s top five leagues in oBMP as one of three keepers with marks ahead of Alisson. Compared to Alisson and Ederson, he may take a different route to getting involved in the attack, but he has a considerably different 10 outfield players in front of him, so it’s often the only way for him to contribute as a forward-thinking keeper.

Any club considering Pope will also want to know how he fares with short and medium passes. He’s completed 116 of 123 inside that range, and he plays for a team that’s consistently under more pressure in those defensive areas than clubs such as Manchester City and Roma. That’s 94.3 percent, which puts him ahead of Pickford (93.8), a well-respected keeper Everton paid plenty for over the summer. It makes you wonder what kind of distributor Pope would be if he had the luxury Ederson has of being judicious with his long balls.

Pope’s also been strong between the sticks in the traditional sense. Burnley have conceded 24 goals, which is a Premier League-leading -24.2 of expected. Everton? -4.2. In terms of expected goals, Burnley with Pope in goal are, this season anyway, above even Manchester United’s level this season with De Gea (-20.0).

This is all supported by player points, where Pope (3.9) ranks just ahead of Ederson and only behind De Gea (4.1) in the Premier League.

So from a recruitment perspective, a club like Liverpool may well be set on the emerging greatness of Alisson as an all-around keeper. But so might a number of other clubs, especially if he has a strong showing on the world’s stage come June. The great thing about deep data when assessing a deal is it allows a club to walk away or in a different direction rather than getting carried away.

Ederson’s been strong, both with that subjective eye test he frequently passes and the objective data we’ve considered here. Alisson might be even better. But those clubs this summer looking to follow the emerging trend and attack from the back might save a few pounds by digging even deeper.