Are the Seattle Mariners Rebuilding?

Image from Mariners Blog.

Traditionally, teams that win 89 games in the previous season don’t typically become sellers in the following offseason. Those types of teams are a few players away from solidifying a roster that has a chance to make a deep run into the playoffs. The Seattle Mariners may disagree with that notion, and it isn’t because of their roster. They play in one of the most competitive divisions in baseball. The Astro’s (103-59) and the Athletics (97-65) each went to the playoffs last season, and neither team shows any sign of being on the decline.

According to the CGMs Statistical Power Rankings, the Astro’s were the most talented team and the Athletics were the seventh best team in baseball last year. The Mariners were respectably ranked 13th, but competition in the American League was fierce and as talented as the Mariners roster was going into the offseason, they were still on the outside looking in going into the 2019 season. The Mariners were in need of a top of the rotation starting pitcher (to replace a declining Felix Hernandez), a middle reliever to bridge to Edwin Diaz (now a Met) and another high-powered outfield bat to help bridge the gap between themselves and the rest of the AL West. As a middle-market team, the Mariners had little change of luring a top free agent, and instead would need to orchestrate a plus-version of Moneyball to compete with a dominant Astro’s team, and an Athletics team run by a man that can only be described as a sorcerer in Billy Beane.

The Mariners had three options:

  1. Become buyers in the offseason and spend money (which they may not have, they paid $157,000,000 for their roster) on free agents, or trade for bonafide MLBers by mortgaging what little talent they had left in their already depleted farm system.
  2. They could sell off some of their more expensive chips, and attempt to maintain their roster’s integrity by acquiring younger players while trying to outlast the dominance of the Astros at the expense of the immediate.
  3. Blow it up, sell off their pieces, reload their farm system, and wait three to five years for Houston to hit the cliff.

From what we can tell, the Mariners are exercising option two. In the last week, they made what will likely be the biggest trade in the offseason, sending the MLB’s best closer in RHP Edwin Diaz, who posted 57 saves, a .79 WHIP and a 1.96 ERA to the New York Mets along with 13-year veteran 2B Robinson Cano. Cano, coming off an 80-game suspension for violating the league’s substance abuse policy, hit .303 last year and was serviceable at second base, but is still owed half of the 10-year $240M contract he signed with the Mariners. In return, the Mariners received a compliment of young and veteran players from the Mets. The deal was headlined by fifth overall pick OF Jarred Kelenic, who projects as a top 50 MLB prospect without ever having stepped in a professional batters box, and is joined by another top 100 prospect in RHP Justin Dunn who is still a year or two away from the bigs, but is a candidate for a training camp invite. The Mariners also received three MLB-ready players in OF Jay Bruce, RHP Anthony Swarzak, and RHP Gerson Bautista. Bruce had a rough 2018 campaign, but has a lot of pop in his bat, a plus arm, and is one of the best clubhouse personalities in baseball. Bautista projects as a plus reliever and has good “stuff” that should keep him around in the league for a long time. Swarzak is a veteran middle reliever who should immediately fill the 7th inning role for the M’s despite a down 2018 season.

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The trade accomplished a few important things for the Ms:

  1. It replenished their weak farm system with three young prospects that project as starting to all-star caliber players.
  2. It dumped the contract of 2B Robinson Cano who was still owed $120M.
  3. It brought in serviceable talent at multiple positions.

The Mariners continued making moves a few days later by trading their leadoff hitter and two-time all-star, SS Jean Segura along with RHP Juan Nicasio and LHP James Pazos to the Phillies. Segura, the 28-year old breakout star who the Mariners acquired in a trade with D-backs hit .302 in his two years with the Mariners. In return, the Mariners received 1B Carlos Santana-who posted a career-low batting average (.229) and OPS (.766)-and Phillies former top prospect, SS J.P. Crawford. Although Crawford has maintained himself as a top 50 prospect since 2013, his MLB service time has been forgettable.  He’s hit .214 in 225 plate appearances and struck out 59 times over that span. What he did manage to do well was flash what may be the best glove in baseball along with posting a respectable .333 OBP.  I have doubts that he will become a better player than Segura has become, but he’s young and the Mariners could be the change of scenery and the fresh start he needs.

The salary dump of Cano and Segura should help the Mariners sign a few players to fill some gaps over the next few years, but what these trade really do for the franchise is cement the next five years at a few positions. Dunn, Kelenic, Crawford, and Bautista should be in a Mariners uniform for a long time to come. The Mariners may not be better than they were Yesterday, but playing in the AL West all but guarantees that for the next few years, their ceiling is the wild card. Mariner fans should be pleased with the trades, despite giving up Segura and Diaz. If J.P. Crawford and Jared Kelenic can live up to their potential, it’s going to be a fun team to watch down the road.

Analysis: Assessing the Effectiveness of the CGMs MLB Statistical Stratification (Power Rankings)

Introduction:

The MLB season is in the books, and it’s time to look back and evaluate how the CGMs MLB Statistical Stratification System performed.  Roughly 60 Games into the MLB regular season I attempted to create a new stratification method to replace conventional power rankings. You can read that article here to learn about the system’s methodology. Baseball is a game where analytics can tell a story, but there fails to be a comprehensive and universally accepted formula for stratifying team performance. Most rankings are determined by the eyeball or litmus test in which random value is applied to one of many different categories to determine performance. I set out to standardized value sets so that from one set of stratification to the next, teams are being evaluated on a consistent scale. To select these variables I looked to baseball statisticians such as Bill James to determine what factors play the most significant role in team performance. By weighing those categories (OPS, BA, WHIP, ERA, and Fielding Percentage), I generated a formula which produced a composite score that closely correlates with a team’s record. For this analysis, I am going to repeat my previous approach to see how the teams stacked up at the end of the year and verify whether or not my approach has validity. Additionally, I will make assertions about ball clubs by breaking down the data set and identify weaknesses in the methodology.

Previous Findings:

In my June rankings, I found that the formula had a close correlation with the league standings.  At that point in the season, I was able to determine that the Tampa Bay Rays were underperforming by comparing their composite score to their record, at the time of the initial analysis, the Tampa Bay Rays wear a slightly better than .500 team (28-27), But they were ranked as my sixth best team. As the season unfolded, it turned out that my metric was accurate, as the Rays finished the season With 90 wins in a tough AL East Division that produced two 100-win teams and the World Series champions.

Similarly, I found that The Los Angeles Dodgers were underperforming according to my metric. At the time of the initial analysis, they were 4 games under .500 (26-30) but ranked in the middle of the pack which means they should have had at least two to three more wins at that point in the season. The Dodgers finished the regular season with 92 wins and the highest run differential in the National League (+194).

New Data Set: 

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Click here to view the original data set.

End of the Season Stratification:

  1. Houston – 4. (103-59)
  2. Boston –  4.2 (108-54)
  3. Cleveland – 4.9 (91-71)
  4. LA Dodgers – 5.95 (92-71)
  5. Tampa Bay – 7.25 (90-72)
  6. New York Yankees – 7.8 (100-62)
  7. Oakland – 7.95 (97-65)
  8. Washington – 8.89 (82-80)
  9. Atlanta – 9.4 (90-72)
  10. Milwaukee – 9.55  (96-67)
  11. Chicago Cubs – 9.55 (95-68)
  12. Colorado – 10.3 (91-72)
  13. Seattle – 13.65  (89-73)
  14. Arizona – 14.3 (82-80)
  15. Pittsburgh – 15.35 (82-79)
  16. St. Louis – 16.7 (88-74)
  17. LA Angels –  17.5 (80-82)
  18. Cincinnati – 17.75 (67-95)
  19. Minnesota – 18.9  (78-84)
  20. NY Mets – 19.5 (77-85)
  21. Toronto – 20.2 (73-89)
  22. San Francisco – 20.85 (73-89)
  23. Philadelphia – 21 (80-82)
  24. Detroit – 22.7 (64-98)
  25. Texas Rangers – 23.5 (67-95)
  26. Kansas City – 23.55 (58-104)
  27. San Diego – 24.05 (66-96)
  28. Chicago Sox – 24.7  (62-100)
  29. Miami – 24.8 (63-98)
  30. Baltimore – 27.1 (47-115)

Overperformers:

  • Philadelphia – Hard to imagine saying a sub .500 team overperformed, but according to our metrics they should’ve lost a few more games.
  • St. Louis – 88 wins may have been the upper end of their feasible spectrum, especially in one of the most competitive divisions in baseball.

Underperformers:

  • Washington – This shouldn’t come as a surprise, Washington could barely hold a lead all year. The only team with a bullpen that gave away more leads was the Mets.
  • Pittsburgh – If the Cardinals overperformed then the Pirates underperformed. They stratified as a better ballclub than their division rivals, yet managed to lose five more games. Managing might be the unmeasurable factor in their failure to top the Cards.

Findings, Data Set Problems, and Trends:

  • Teams with a composite score <10 tended to have more than 90 wins, Washington being the only exception.
  • Fielding percentage is being evaluated too highly in the data set. The deviation from the best defensive team to the worst was only .008% (Astros .989 and White Sox .981), yet it is being graded at 10% of the weighted composite score. In future data sets, I will need to weigh it less severely and allocate the free percentage toward other metrics.
  • Findings may at times be spurious due to some metrics being part of others (OPS and BA).
  • Composite scores are built off rankings from other statistical categories, the deviation between teams may be exaggerated based off actual performance in each category, but the ranking of teams remains accurate because they are all being evaluated on the same scale.

A Better Way?:

This is by no means the best way to stratify teams, as I highlighted in the previous article, regression analysis can tell us more about which of these metrics is both statistically significant and impactful (coefficient value). Previous baseball analyses have largely done this work for us, identifying the value of these metrics which has become a staple in player analysis but rarely used to evaluate overall team performance. To improve upon the CGMs Stratification System, a regression identifying the impact of each variable on team performance would help in better weighing the metrics to hone in on a more accurate composite score.  Until then, this methodology is among the few to evaluate team performance outside of team record.

Analysis: MLB Power Rankings, A New Statistical Stratification of Analyzing and Projecting Team Performance

Introduction:

I am of the mindset that a weekly power ranking fails to accurately reflect the MLB’s rankings. It’s useful to demonstrate weekly trends but fails to effectively stratify a team against its peers in any meaningful way.  Teams get hot, or play poor competition and go on streaks similar to what the Rays did earlier in the year when they began closing ground on the division-leading Red Sox. Week-to-week teams can rise or fall significantly, and while it makes an interesting read, the weekly rankings don’t mean much. A periodic ranking over quarters of the season paints a more complete picture of what’s happening, and with this in mind, I set out to stratify the league through the first ~60 games.  

I am stratifying teams based off of a new system that I’ve created that focuses on statistical analysis rather than simply a team’s win-loss record. This counters the popular argument that ‘you are your record,’ but this is a Power Rankings, not a simple breakdown of standings and a team’s performance/trend over their last 10 games. The purpose of this approach to stratification is to determine how talented a team truly is. Some teams may be playing well, but due to strength of schedule, or catching a streaking team, they may be underperforming their actual ability. Similarly, a bad team may be overperforming. To say that a team’s record has a spurious relationship with their talent is foolish, but I don’t think it tells the entire story. I predict that the weighted variables that I will be presenting in these rankings will correlate strongly with a team’s record. 

I am heavily stealing the propositions made by Eric Walker and Bill James, the fathers of sabermetrics to contribute to my narrative and give context to my weighted categories. I choose five variables to assist in creating my power rankings. Surprisingly, or not, runs scored, runs against, and wins are not included in the calculation. Instead, I chose variables that when combined tell a story about the team. Earned Run Average (ERA), Batting Average (BA), Fielding Percentage (FP), On-base Plus Slugging (OPS), and Walks and Hits Per Inning Pitched (WHIP). When combined, these statistics offer a holistic measurement of team performance.

Methodology:

Now, anyone that is familiar with statistics understands right away that some of my independent variables influence others, which may be the first flaw in my argument. But, each category plays its own role in the narrative, and thus must be included because they still measure different events in the game.  Without running a regression analysis, it’s impossible to determine which of these variables has the most impact (coefficient) on our dependent variable (which is performance – wins/losses) and which are actually statistically significant. Fortunately, others that have come before me have done most of the legwork. Enter Eric Walker, Bill James, and others (a good article by Adam Houser argues similar points and can be found here: https://www.iwu.edu/economics/PPE13/houser.pdf).

OPS may be the most important offensive stat in modern baseball. As Walker and James put it (and House agrees), the best way to generate offense is through the ability of a hitter to not get out. Getting on base is the BEST way to do this. The image of Billy Beane and Sandy Alderson in Oakland yelling at old scouts and telling their minor league managers to ensure their players are drawing walks is visceral to this point. The heralded stat of batting average is important because getting a hit leads to having a runner on base, but it only contributes so much as to add extra bases to a players ability to already get on base (measured in the OPS/OBP) for the purposes of this article. OPS contributes another thing if you’re getting on base. Walker also stated that the next most important statistic is slugging percentage, which is also measured by OPS. To advance and drive in runners, putting the ball in play is absolutely necessary.  The batter doesn’t need to be safe at first to generate runs or advance runners into scoring position.  These are not novel concepts to anyone who has read his works, or Moneyball, but they are important to the new formula, and as such OPS becomes the most important measurable offensive statistic for the purposes of this new rankings metric. It will thus be weighted more heavily than the other offensive statistic (BA), which is self-explanatory.

Fielding percentage is often overlooked. Defensive runs saved has become a popular metric to measure individual players, particularly when valuing fourth outfielders, but it is not significant enough to warrant a lion’s share of this metric. Most teams deviate by less than .01%, and while ensuring that teams are converting the putouts that they should be making, it is weighted less than other categories even though it is the lone metric that measures a team’s fielding efficiency.

WHIP was found by Houser to be the most effective measure of pitching/defense. I similarly find it the most important metric to measure a pitcher, but ERA offers a more holistic team rating. You might be asking, why have WHIP if you have ERA? Both measure the talent of a pitcher against a batter, but an ERA also measures the team defense and coaching strategies, whereas WHIP primarily measures the battle between a pitcher and the batter. This may be up for debate and could be argued as my second flaw in the measure, but I’ll try to explain my reasoning. An earned run could be scored by a team forfeiting a run with a runner on second and third with one out, and taking the out at first on a ground ball as opposed to challenging the run at home. This is a defensive strategy decision, and not necessarily a product of the pitcher vs. batter. Similarly, a manager’s decision to bring in a certain pitcher to face a batter (ie lefty specialist, deciding to pull a pitcher prematurely, or leaving one in for too long).  The resulting hits/walks/runs would then be on the manager, a metric which we, unfortunately, do not measure in a coherent data set. In the absence of this, we lump defensive decisions, strategy, and managerial choices together with the pitcher vs. batter duel as part of the narrative for ERA, whereas fielding percentage measures the team’s ability to not commit errors that lead to unearned runs.

WHIP, our last metric, previously discussed as the most important defensive statistic by Houser,  measures the average number of Walks and Hits Per Inning Pitched that a team’s pitchers give up. This is critically important because it is essentially the defensive counter metric to OPS. It is the ability of a defense and pitcher to prevent runners from getting on base.  As such, it will be weighted heavily in this equation. Again, having both ERA and WHIP may be counter-intuitive, but teams are being stratified against one another equally, but teams are being stratified equally and the two metrics as we have discussed measure different events. So it shouldn’t prevent the end result from being accurate. 

Weighted Percents:

Unfortunately, without a regression offering the statistical significance and coefficient, I need to use my narrative to determine the weights. After a few conversations with people telling them my idea (some of which liked the idea as a tool for projecting a teams success, others subscribe to the win-loss being the end all be all) I think I’ve come to a balance. I wanted to have offensive and defensive metrics weighted equally for the sake of the overall rankings, so below you will find my weighted breakdown of each category.

  • ERA: 20%
  • WHIP: 20%
  • FP: 10%
  • BA: 15%
  • OPS: 35%

Data Set: Accurate as of 6/1/2018

Google Docs view for those that cannot see the data set: https://docs.google.com/spreadsheets/d/1KKwtf9tOEnfpzuGlglTg_GbCALtcsYgCzWhQ5KX1MVU/edit?usp=sharing

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Results – Power Rankings:

Using the weighted averages and the data set above, I created a composite score that averages the rankings of each team. The team with the lowest score rounded to the nearest hundredth will be ranked highest. Example: Houston’s ranked a 4.3 based on my metric, and as such are ranked first. To the right of their ranking, I put their current record as a way to determine how well the metric is correlating with their record, and whether or not teams are overperforming or underperforming (based on the metric). Below are the results.

  1. Houston – 4.3 (36-22)
  2. Boston –  4.35 (39-18)
  3. Chicago Cubs – 6.8 (30-23)
  4. New York Yankees – 8.15 (36-17)
  5. Washington – 8.2 (32-24)
  6. Cleveland – 8.25 (30-25)
  7. Tampa Bay – 8.5 (28-27)
  8. Atlanta – 8.95 (34-23)
  9. Seattle – 9.15 (34-22)
  10. LA Angels – 10.45  (30-27)
  11. Pittsburgh – 10.65 (29-27)
  12. Milwaukee –  11.45 (36-21)
  13. Detroit – 13.9 (27-30)
  14. Philadelphia – 15.35 (31-23)
  15. Arizona – 15.55 (28-27)
  16. St. Louis – 15.9 (30-24)
  17. LA Dodgers – 15.95 (26-30)
  18. Colorado – 16.05 (30-26)
  19. Oakland – 16.4 (29-28)
  20. San Francisco – 16.95 (26-30)
  21. NY Mets – 18.15 (27-27)
  22. Minnesota – 20.5 (22-30)
  23. Kansas City – 20.5 (20-36)
  24. San Diego – 22 (25-33)
  25. Chicago Sox – 22.1 (16-37)
  26. Cincinnati – 22.55 (20-37)
  27. Toronto – 22.6 (25-32)
  28. Miami – 24.65 (20-36)
  29. Texas Rangers – 24.85 (24-35)
  30. Baltimore – 26.05 (17-40)

Conclusions:

  • The Rankings System seems to correlate well with team performance.
  • Including both ERA and WHIP was justified based on variations seen in Cleveland, Colorado, Tampa Bay, Atlanta, NY Yankees, St. Louis. These illustrate the value of having both metrics, but as expected, most WHIP statistics strongly correlate with ERA.
  • Tampa Bay Rays are underperforming according to the Rankings System.
  • LA Dodgers are underperforming according to the Rankings System.

The Way Forward:

  • The next step for this system is to run regressions to more accurately weigh each category and determine if any other statistics should replace or be included in this stratification methodology. 
  • Take this stratification methodology and build on it to project teams likely overall records and success in the future. 

Shoutout to the Mercer University Weighted Average Grade Calculator.

Commentary: Five Bold Predictions One Week into the MLB Season

1. An Angel will win the MVP award and he won’t be named Mike Trout. This prediction may not seem as bold as it did when I had originally made it before the regular season, but I’m doubling down. Shoani Othani will win the MVP and AL Rookie of the Year Award. He’s off to a blistering start and has been key to the Angels’ early success. He throws high 90’s and touches 100, hits to all fields, has one of the highest exit velocities in all of baseball, and the best part is, he’s fun to watch. He has to come down to earth at some point in time, but I can’t stand when people disregard the early part of the season. Every game and every at-bat counts. You can’t take those Angels wins away from them, and chances are, you can’t stop watching Shoani Othani. With any new player, I get concerned about what happens when scouting reports get fully developed. People will find a way to get him out, batters will figure out what he’s throwing, but until then, he will continue to rake and sit guys down.

2. Both Aaron Judge and Giancarlo Stanton will hit more than 50 home runs. This might not come as a huge shock either, but let’s not act like hitting 50 homers is an easy task, each of these guys did it last year, but that doesn’t mean they will have similar production this year. Already Stanton is having serious problems at the plate. He’s on a record-setting pace for strikeouts during the early stretch of the season. He’s already struck out 23 times, one less than Roger Marris did during the entire 1968 season when he 310 ABs. Comparisons throughout generations are difficult, as the game has changed dramatically and launch angle and fly balls are more valued from hitters than they were in the past, but Stanton looks different than he did last year. There have been analysts that have broken down his swing mechanics and the results are telling. He’s shortened his stride at the plate and opens his hips differently than he did in previous years. This causes two problems, first, he won’t be able to hit the high and tight fastball as effectively as he has in previous years, and second, he will not be able to hit effectively to all fields, making him a more one-dimensional hitter. That being said, I fully expect a professional hitter playing for a team with the resources like the Yankees to get himself sorted out. Stanton is coming from Miami to New York, and the ballpark dynamics couldn’t be more different. I expect a few more of Stanton’s deep flyouts in Miami turn into pole hugging home runs over the short fences at Yankee Stadium.

3. The New York Mets will head to the postseason after winning only 70 games last season. The Mets had a historical injury prone 2017 season which saw 20 players have 28 different DL stints.  Those injuries came to key pieces of the Mets roster. OF Yoenis Cespedes spent 80 days on the DL, SP Noah Syndergaard spent 145 days on the DL with an arm injury, SP Matt Harvey spent 79 days, SP Steven Matz spent 113 days,CP Jeurys Familia spent 106 days and OF Michael Conforto spent 48 days on the DL with a freak shoulder injury that occurred when he swung his bat. This season, the Mets are entering the year health, and with the additions of veteran 3B Todd Frazier replacing injured David Wright, 1B Adrian Gonzalez as the bridge to top prospect 1B Dominic Smith, and the return of slugger OF Jay Bruce, the team is poised to take advantage of a weak NL East. The Nationals are their biggest schedule hurdle, but the Mets traditionally have played well against the NL East favorites, already sweeping them once this year, and could feasibly make a run at a Wild Card spot. I still expect the Nationals to win the division, even with their early struggles, but if the Mets stay healthy and their bullpen continues to be among the best in the MLB, then they have every opportunity to make a postseason run. They have three long arms in their bullpen that can bridge the middle innings and both Seth Lugo and Robert Gsselman have been lights out so far.

4. Chris Sale will win the AL Cy Young and Max Scherzer will win the NL Cy Young Award. At this point Scherzer is like Meryl Streep at the Oscars, everyone knows he’s in going to be one of the top two candidates, and it comes as no surprise at this point. Every fifth day you get to watch something special. Scherzer threw over 200 innings (in the National League) and had a 2.51 ERA last season. This year, the 33-year old is off to a hot start, he has a .90 ERA and has struck out 27 batters through 20 innings pitched. Sale, similarly tallied over 200 innings last season and held a respectable 2.90 ERA. This year, he has a 1.06 ERA through 17 innings and has struck out 23 batters. These two are the most dominant arms in the sport and continue to be durable and consistent. So long as they remain healthy, there’s nothing to stop their Cy Young bid.

5. The Texas Rangers will be must-see TV, at least every fifth day. Bartolo Colon and Adrian Beltre in the same dugout. They have more fun playing the game of baseball than anyone else. If you happen to catch the Rangers play some interleague baseball there’s a chance you can see a unicorn. https://www.mlb.com/video/must-c-colons-first-home-run/c-671207583

Commentary: Facebook to Broadcast 25 MLB Games: Mets-Phillies Game Today at 1PM

At 1pm the Mets-Phillies game will air exclusively on Facebook. This will be the first MLB game exclusively broadcasted on a social media platform. The move stems from an MLB initiative to expand viewership and attract a younger audience. The MLB has long struggled to captivate younger fans and has lagged behind the NFL and NBA viewership in recent years. The long average game time (3:05), long season (162 games), and slow pace of play have been attributed to the games viewership stagnation. To Major League Baseball’s credit, while the NFL has seen a drastic decline in its viewership over the past couple years, the MLB has not seen their decline at the same rate. Locally broadcasted games have remained relatively steady and some markets have shown growth, but the MLB continues to struggle with younger audiences, and their nationally televised broadcasts continue to pull comparatively weak ratings. Further contributing to the MLB’s problem, in a new trend, stadium attendance was down last season, but the MLB is actively trying to fix their problem, by introducing rules to improve the pace of play and shorten the game.

Facebook is one of the most heavily trafficked websites, and the social media platform caters to an audience that the MLB is trying to access. By exposing a younger viewership to baseball they may have the opportunity to slowly expand their fanbase and ensure the future of the sport at the same time. Over the next two months, Facebook has the broadcasting rights to nine MLB games, all mid-week afternoon games, and will broadcast a total of 25 games throughout the entire season. The Facebook broadcasts will be produced by the MLB Network and the games are accessible on any web-enabled streaming device from Facebook’s Watch MLB Live page. One feature of the broadcast is that it will be largely interactive, with the MLB Network team leveraging the social media platform to bring their audience into the game. Their goal is to engage with as much of the social media conversation as practicable without hindering the call of the game. Today’s broadcast will act as a barometer for the MLB and may prompt future investment in exploring social media as a broadcasting service to expand their audience.

Fans new to baseball will experience a good NL East matchup during today’s Mets-Phillies game which will showcase the aces of each pitching staff, Noah Syndergaard, and Aaron Nola. Both had strong outings in their season openers, Syndergaard struck out 10 batters, and Nola threw five shutout innings in only 68 pitches before being controversially pulled in a game that the Phillies eventually lost. Wins in the NL East have been hard to come by, and both teams are in a sprint to avoid an early chase of the division-leading Washington Nationals.

To watch today’s broadcast visit: https://www.facebook.com/MLBLiveGames/ 

Photo Credit: Newsweek