The Game’s Future: Why SM & Analytics Matter

On the Line

Why This All Matters


To say that baseball has come a long way in technological advancements within the last two decades would almost be an understatement. The interpretation of big data, social media promotion strategies, and sabermetric statistics have all contributed to creating higher quality play throughout the league for fans to enjoy. Fans are closer to their beloved players than ever with the help of social media platforms like Twitter and Instagram, inducting a younger audience into stadiums nationwide. Advanced analytics and statistical evaluations have enabled GMs, managers, and scouts to all make more informed decisions about player acquisition and evaluation. The game is more advanced now that it has ever been at any previous point in its history. The real question is, what’s next?

The Next Step


What was cutting edge and competitive advantage a decade ago is now commonplace among teams, and the next step for baseball analytics is to find the next wave of information for the next advantage. Answers on the next steps will varied by each person asked, likely a representation of the diverse backgrounds currently in baseball front offices, yet one thing will remain clear: There is a great deal that the baseball world still doesn’t know. Studies into things such as player nutrition, injury prevention and visual tracking data like Pitchf/x will continue to be explored. The most enticing of these is baseball’s exploration into medical analysis and injury prevention (what I believe is the next big step).  According to the 2014 SABR Analytics Conference, the new frontier of baseball data is not just about scouting players, but keeping players healthy and injury-free. The new area of research, just in in its infancy, is marrying baseball statistics with medical injury research.

Data-Driven?


Baseball will always be a sport that is starving for statistics. I think that the next major front in baseball-related data analysis will be in the video mechanics/analysis area. This practice has already been popularized at the high school, instructional, and collegiate levels and continues to grow with each passing year. Player tracking is also on the rise. The MLB is already doing a lot more tracking of player movements utilizing software like MLBAM and Trackman along with studies at MIT.

A League of Innovation


Finally, teams themselves will start becoming innovative organizations. The San Francisco Giants actually have the word “innovation” built into their mission statement. In 2004, the Giants were the first MLB team to offer Wi-Fi throughout their stadium. Today, the MLB averages 35% of fans engaging in online activity at games. The Giants’ stadium allows fans to easily upload content via the Giants app or social channels like Faceboook, Twitter, and Instagram, furthering along the fan engagement initiative. In 2009, the Giants introduced dynamic ticket pricing (DTP). This notion allows the price of game tickets to rise, or fall, depending on popularity and availability. Other teams now use DTP, and the idea has spread into other business climates for settings like restaurants, movie theaters, and the performing arts.

“The San Francisco Giants are dedicated to enriching our community through innovation and excellence on and off the field.”

It can only be assumed that other teams will follow suite on what the Giants are doing; creating a league wide push for continued innovation. Sounds a lot like today’s business climate, right? There is no better time to be a fan of the MLB than the present, and I personally can’t wait to see how high the ceiling really is for both social media and analytics in America’s beauitful game.

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Bryan White, MBA

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A Guide to Baseball Sabermetrics

sabermetrics

Baseball is truly the sport of the ‘stat junkie’. Many of these stats can be attributed to the system of sabermetrics,  which were first initiated into the sport in the 1980s, and grew exponentially in the 1990s. Sabermetrics really gained traction in the early 2000s, as many of baseball’s front-office decision makers became major advocates of some of these statistics as an alternate way of evaluating players. Sabermetrics derives from the acronym SABR, which stands for the Society for American Baseball Research. The phrase was coined by acclaimed baseball author and researcher Bill James. James and others created new statistics to measure player productivity other than the traditional batting averages and ERA.

In this blog installment, I will outline some of the more widely used stats that have come about as a result of sabermetrics, and how they are calculated. (All are defined from the American Baseball Sabermetrics Glossary)

BABIP: Batting average on balls in Play


The frequency of which a batter reaches a base after putting the ball in the field of play. For pitchers (a measure of the hitters they face), it’s a good measure of luck. So pitchers with high or low BABIPs are good bets to see their performances adjust to the mean.

Def Eff: Defensive Efficiency


The rate at which balls put into play are converted into outs by a team’s defense. Can be calculated with (1 – BABIP).

EqA: Equivalent Average


A stat used to measure hitters independent of ballpark and league effects. It’s a complex formula that takes into account hits, total bases, walks, hit by pitch, stolen bases, sacrifice hits, sacrifice flies, at-bats and caught stealing. It’s then normalized for league difficulty.

ERA+: Adjusted ERA


Earned run average (for pitchers) adjusted for the ballpark and the league average.

Fielding Runs Above Replacement


The difference between an average player and a replacement player is determined by the number of plays that position is called on to make.

IR: Inherited Runs


The number of runners inherited by a relief pitcher that scored while the reliever was in the game.

ISO: Isolated Power


A measure of a hitter’s raw power – extra bases per at-bat.

LIPS: Late-inning Pressure Situation


Any at-bat in the seventh inning or later, with the batter’s team trailing by three runs or less (or four runs if the bases were loaded).

Runs created


A term to measure how many runs a player creates. Its basic formula is hits plus walks times total bases, divided by at-bats plus walks.

OPS


One of the holy grails of sabermetrics. On-base plus slugging. Measures a batter’s ability to get on base and hit for power. It’s simply the on-base percentage plus the slugging percentage.

WAR or WARP: Wins Above Replacement Player


A statistic that combines win shares and VORP (Value Over Replacement Player). It represents the number of wins this player contributed, above what a replacement level hitter, fielder, and pitcher would have done.

WHIP: Walks & Hits Per Inning Pitched


The average number of walks and hits allowed by the pitcher per inning. (BB + H divided by IP).

All of these statistics are used to evaluate current players, prospective draft picks, and potential free agent targets over the course of the 162 game grind that is an MLB season. By utilizing these statistics, major league teams are able to break through the big data wall and find valuable ‘markers’ that indicate on-field talent. Through the utilization of sabermetrics, baseball teams are becoming smarter in both contract negotiations and lineup selections; providing for a better on-field product to be viewed by the fans.

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Bryan White, MBA

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No Longer a Guessing Game?

Kershaw

For the majority of its history, baseball has been widely embraced as a game of failure. In fact, many hitters are enshrined in the Baseball Hall of Fame having failed a staggering 68% of the time or more in professional plate appearances (this would translate to a .320 career batting average). This is what makes the game so beautiful in my opinion; finding the silver lining amidst a world of shortcoming. In no other sport would an individual be deemed a success with the percentages hitters face in baseball. A QB with a 32% completion percentage? No way. A point guard who shoots 32% from the field? Hello free agency. Failure is, and will forever be, a part of baseball….or will it?

Enter advanced analytics and Ray Hensberger, a baseball enthusiast and strategic innovator. Hensberger has been bold, and brilliant, enough to attempt to alter baseball’s failure rate. This entails pitchers not having the upper hand in baseball’s future. At a 2014 conference, Hensberger shared his innovative data crunching and the academic paper his team produced for the MIT Sloan Sports Analytics Conference. His team modeled MLB data to show with 74.5% accuracy what pitch a pitcher is most likely to select on a given count. Sounds ludicrous right? Actually, it’s quite the contrary.

Hensberger’s calculations are revolutionary; more accurate than any pitching analytics to date. How did they come this far? Hensberger and his team started with 900 pitchers on MLB rosters, and then excluded player who threw less than 1,000 pitches over a three season window.  This drew an experimental sample of about 400 arms for his model to evaluate. Variables like the matchup of batter vs. pitcher (Righty/Lefty), current at-bat (pitch type and zone history, ball-strike count), game situation (inning, number of outs, and number and location of men on base); as well as other features from observations of pitchers varying across a span of games, such as curveball release point, fastball velocity, general pitch selection, and slider movement.

The result was essentially a set of pitcher-specific models as well as a detailed report about what those pitchers would throw in in-game situations.

The ‘Field’ Test


Hensberger and his team ran this model over previously unseen games from the 2013 World Series featuring the Boston Red Sox and the St. Louis Cardinals. The result was a staggering 74.5% pitch prediction efficiency. Being a former college pitcher, this terrifies me. However, I see how it can benefit the game as well. Adding runs usually adds excitement and attendance. While this process is clearly a ways away from being implemented (and I wouldn’t be surprised if the MLB instituted policies against it to maintain the human element of the game), it is no doubt an astounding testament as to how valuable a concise understanding of big data is. There’s a lot of power to be harnessed, and we are finally tipping the iceberg.

Want more on this topic? Check out the link below featuring a lecture on pitch prediction from the 2012 Sloan Sports Analytics Conference. This ‘throwback’ shows how they have definitely been onto something for quite some time.

https://www.youtube.com/watch?v=MLwzQBNzoRw

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Bryan White, MBA

MLBAM – Batting 1.000 Against Big Data

In 2014, the MLB’s Advanced Media department introduced a new method of measuring every single play that occurs over the course of a game in the form of an advanced tracking system. The tracking system observes plays and sends coordinate data to computer systems at MLBAM (Major League Baseball Advanced Media), which uses algorithms developed by New York University computer science professors Claudio Silva and Carlos Dietrich to compute game metrics. The system had its debut last year at three MLB parks (Miller Park- Milwaukee Brewers, Target Field- Minnesota Twins, and Citi Field- New York Mets), and takes into account statistical factors like error mapping and confidence scoring.

The video below showcases some of the analytical data points made available by MLBAM tracking system. Watch as former Atlanta Braves outfielder Jason Heyward makes a game-ending, diving catch in the 9th:

The goal of this project is to revolutionize the way baseball is evaluated; by presenting tools that connect all actions that happen on the field and determining how they work together. This new datastream will enable the industry to understand the whole spectrum of a baseball game (batting, pitching, fielding and baserunning) and enable new metrics for evaluation by clubs, scouts, players and fans. It can be confidently said that this is the largest league-wide embrace of advanced analytics, regarding big data, to date.

Metrics Utilized

 (Taken from an MLBAM.com article on the tracking system)


Batting

For batting, real-time metrics will be exit velocity, launch angle, projected home run with distance, hang time, and fly-ball distance.

Base Running

Real-time base-running metrics are lead distance, acceleration, max speed, and home-run trot. “First step, route efficiency, stealing first step, and secondary lead distance will be available with a 12-second delay.” (MLBAM.com)

Fielding

Fielding metrics available live will be acceleration, max speed, and shift positioning; “12-second–delay metrics will include total distance on caught balls, first step, arm strength (catcher and fielders), exchange (catcher and fielders), pivot, and catcher ‘pop time’ (time to throw down to second base)”. (MLBAM.com)

Needless to say, it’s pretty cool stuff. With the 2015 season well underway, I’m excited to see what the inagural year-end reviews are on this innovative approach to sabermetrics.

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Bryan White, MBA

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MLB Analytics : Benefiting Businesses

MLB Data

This post marks the beginning of the second topic I will discuss in my blog postings; the use of advanced analytics in the MLB to gain insight into both gameplay and actual player value. Previously, I had discussed how the MLB’s model for social media could be applied (and would be beneficial) to almost all firms in today’s business climate. In a similar fashion, I will discuss how it would be prudent for businesses to adopt 3 particular mindsets/ analytical practices that the MLB is using to decipher big data. Many of the best practices now embraced by GMs and team managers throughout the sport can, and should, be used by executives in all lines of business.

1.) Value the Data


For the majority of its vast history, baseball managers have made decisions almost entirely off experience and ‘gut feeling’. This gut feeling was a justifiable explanation for just about any decision, ranging from the starting lineup to a pinch hitter or which relief pitcher to bring in from the bullpen. Now, with the understanding of big data through advanced analytics, nearly every in-game decision is driven by longstanding data records showing players’ statistics and tendencies. If a manager makes a critical in-game decision in baseball’s modern era, he’d better have data-supported reasoning.

The business takeaway of this is simple, be more data driven in decision making. Not saying that past experience isn’t important or applicable, but a willingness to take advantage of data you have at your disposal must be present.

2.) Be Open-Minded to New Metrics & Methods


Traditionally the hitting aspect of the game was solely evaluated on home runs, RBIs and batting average. For pitchers, wins and ERA dominated the scene. Nowadays, new metrics have been introduced that have changed how we have traditionally labeled a player as “effective”, and this has sparked quite the debate. It started slowly, with a handful of teams valuing on-base percentage (the prevalent statistic in the innovative book Moneyball) over the longstanding batting average. This has placed enormous value on players like Mike Trout, who thrive on the basepaths.

A similar approach needs to be embraced in the corporate realm. Traditional data does have its place, but the analysis of new forms of data such as text, social media, etc… are available that give an even more accurate picture of the climate of an industry.

3.) Prediction


Baseball teams have historically scouted, evaluated, and compensated players based on their production in previous seasons. The better a player played in the past, the greater his salary in the present and future, when contracts began to lengthen in years and ‘guaranteed money’ became a thing. By the end of a lengthy contract, teams were often overpaying for players whose best days on the diamond were well behind them (think Carlos Zambrano for all you Cubs fans out there). However, the increased use of analytics has enabled teams to become better at evaluating a players’ future performance (and when they might plateau), and structuring their contracts in accordance.

Likewise, businesses should combine traditional business intellect with predictive analytics. With so much big data now available to be interpreted, companies can make informed decisions not based on what has occurred, but what’s most likely to occur based on the trends data can illuminate.

Each of these embraces of modern analytics can be as beneficial to the corporate sector as they have been to America’s past time. All it takes is an inquisitive mindset, and a little deviation from the status quo, to gain lasting competitive advantages and industry insights.

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Bryan White, MBA

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Filling the Social Seats: Top 3 MLB Teams in Fan Following

MLB Social Media

Over the past few posts, I have talked about how much effort the MLB has exerted in attempt to generate traffic and build fan awareness. Today, in this ‘stats heavy’ installment of my blog, I want to pay homage to 3 organizations who have been exemplary of this goal. The teams below are the top three on MVPindex and are ranked according to their overall reach and fan engagement across Twitter, Facebook, Instagram, YouTube, and Google +. Let’s take a look at the standings and see who is leading the league on social media as the midpoint of the 2015 season draws closer.

1.) New York Yankees


To anyone who is a student of baseball lore and royalty, this comes as no major surprise. The Yankees are the number one baseball team on social media according to MVPindex. The Yankees’ Instagram, Twitter and Facebook have a combined audience of more than 10.2 million followers and fans. The Yankees also have the highest engagement rate in the MLB. Fans on Facebook like a post from the Yankees 17,019 times on average. They also share Yankees content an average of 1,758 times per post. These numbers don’t stray much on Twitter, with Yankees tweets averaging 190 retweets per tweet since the beginning of the season.

2.) Boston Red Sox


Again, no surprise right? The Yankees’ bitter rivals come in second in the baseball social media rankings on MVPindex. The Red Sox are the second most popular team on social media, with a total of 6.4 million fans across Facebook, Instagram and Twitter. This number puts them firmly in second place on Facebook and Twitter, but 4th overall on Instagram. The Red Sox post an average of 648 tweets/month on Twitter, or about 1 tweet per hour every 28 days. Fans respond to this ridiculously high volume of content, too. Fenway’s Finest have 5 of the top 50 tweets in baseball this month.

3.) Los Angeles Dodgers


This one may come as a slight surprise to some, largely due to the massive outpouring of support the cross-state rival San Francisco Giants received last fall as they fought their way to another World Series title. However, the Dodgers come in third both because they are tied for 4th in total followers, a cool 4.17 million, and for their resounding dominance on Instagram. The Dodgers are the most followed baseball team on the photo sharing platform.  They possess the entire top 10, and 49 of the top 100 posts to the platform this month. The average likes on a top 10 Instagram photo? 63,877. *Fun fact, that’s the equivalent to a sold out Dodger stadium engaging with each photo while more than 7,800 fans did the same at home.*

All three of the organizations mentioned above are known for another things besides a fanatical following: revenue. It could be easily assumed that the #1, 2, and 4 teams in the league in 2014 revenue were….you guessed it, the Yankees, Dodgers, and Red Sox (in that respective order). Only the world champion Giants (sitting #3) disrupted the collinearity between fan following and team-generated revenue. This is a picture-perfect example of how generating strong brand equity, combined with massive social traffic, generates into healthy bottom lines.

This concludes the half of my blog dedicated to the social media element of the MLB. In the next five installments, I will be focusing more on the advanced analytics side of the sports; covering topics like pitch-predictive analytics (say what? yes this has been in the works for a number of years) and sabermetrics used to evaluate current players and determine future stars.

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Bryan White, MBA

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The MLB and Snapchat

MLB Snapchat

Of the MLB’s numerous social media platforms, Twitter has served as the most extensive and best at building brand awareness (right now). The graph below (obtained via marketingland.com) shows the Twitter “pull” for each team compared to the industry average, and I found it interesting enough to include in this installment of my blog. A link is provided at the end of this post that redirects to the full report.


Information obtained via marketingland.com


However, my post today discusses a new partnership between the MLB and the Snapchat app; a partnership that has become extremely popular on the “My Story” portion of the app, and could be highly beneficial for both parties involved. For those of you unfamiliar with Snapchat, the app is essentially a social image sharing platform that allows individuals to send self-taken photos to one another with a minimal amount of text included. The MLB is utilizing this app to feature fan generated content. The content content will appear on Snapchat’s ‘Our Stories’, a feature that pieces together users videos and photos at a specific location. These photo/video collages will be displayed on either Wednesdays or Thursdays of each week. While there wasn’t a monetary exchange in this agreement, a high amount of upside is present.

For Major League Baseball this partnership is simple, it allows extensive access to an overwhelmingly young audience. Much like the MLB Fan Cave (as discussed in my previous post), the league is continually aiming to capture youthful attention. In a recent comScore report it was indicated that around 71% of U.S. Snapchat users are between the ages 17-34 (view here: http://bit.ly/1bRRKwg ) . When this is compared to the average viewing age of 54 (view here: http://bloom.bg/1ETzECO ), capturing the younger market is vital for the livelihood of the league. For Snapchat, it is the first sports market niche that the company has obtained. This gives Snapchat access to previously untapped revenue and advertising for the live-streaming industry. These MLB stories could become an ideal environment for feature ads in the foreseeable future, which would have quite the monetary benefit.

All-in-all, I see this deal struck by the MLB to be an advantageous one. Once again, they are capitalizing on a social trend and using it on a mass scale to generate brand awareness. This innovative approach should be a great way for the MLB to appeal to younger audiences, generate social media buzz on sites like Twitter, and form partnerships that will continue to further its digital agenda.


MLB Snapchat Screenshots
MLB Snapchat Screenshots

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Bryan White, MBA

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*Link to marketingland.com’s outstanding article on MLB Twitter influence:

http://marketingland.com/redsox-2nd-influential-mlb-team-twitter-105990