As a young boy and through my early 20s, I was as devoted a sports fan as you could find. I could break down rosters and spit out baseball, football, hockey, and basketball statistics as easily as reciting the multiplication X table. As years ticked off and adult responsibilities confronted me, I began to distance myself; sport by sport, I transformed from fanatic to distant fan.
Since I retired, I have begun to immerse myself in sports again, and the sports landscape has changed in many ways. One change has been the current symbiotic relationship between sports leagues and gambling. Sports leagues have gone from saying gambling had the cooties to a “public affair” with the gambling industry. The sports journalism model has transformed from developing and publishing stories to creating content for digital media’s insatiable demands. One of these changes is that reporters have a penchant for trying to make any accomplishment historic. For example, Player A was the first player to score “X” number of points, “Y” number of rebounds, and “Z” number of assists, at a given age. This information and a fare card will get you on the NYC subway.
One of the significant changes that got my attention was the emergence of advanced statistics, popularly known as Sabermetrics, which by most accounts came into being around 1980. The introduction to advanced statistics, or Sabermetrics, was in large part the film Moneyball which tells the story of how the Oakland Athletics and General Manager Billy Beane constructed a baseball team roster based on acquiring ballplayers that were undervalued based on sabermetrics valuations. Billy Beane receives the lion’s share of credit for using advanced statistics early. He was their general manager for 19 years.
Oakland was a team known to be notoriously cheap when spending money on salaries. This new approach to fielding a team was as much a function of the close-fisted behavior as it was the development of a new paradigm for constructing teams. Beane was lauded for looking at player characteristics that were often undervalued when assessing the potential of a player and their impact on winning. During his tenure as General Manager, the A’s lineup outperformed teams with much bigger payrolls for several years.
For a seven-year stretch, the Oakland A’s earned one of the best records in Major League Baseball with one of the lowest payrolls among 30 teams. During that time, they averaged 93 wins per year from 1999 through 2006. The success of his team changed the landscape of baseball. It focused on statistics like On Base Percentage (OBP), Slugging Percentage plus OBP plus Slugging Percentage. Paradoxically less attention was paid to physical characteristics, essentially the” Five Tool Player.” The player that could:
- Hit for power
- Hit for average
- Fielding ability
- Throwing ability
- Running speed
This new approach also valued drafting college baseball players more than high school prospects. It’s noteworthy that the National Basketball Association takes the opposite view of college versus younger athletes, hence the “One and Done Rule. I should point out that Major League Baseball has had an organized minor league structure for decades, while the NBA has almost none to speak of (the G League).
The success of the Oakland A’s and Billy Beane during those seven years had a seismic effect on other teams. It instituted a significant movement towards using these new metrics to make player and roster decisions. This transition led to additional measurements being created and adopted to make not only roster decisions but also gained momentum to evaluate a player’s values regarding contracts and awards. I swear I’m not yelling to “tell kids to get off my lawn.” Still, there are some things that I find very suspect or questionable about this cult-like allegiance to this new way of looking at the baseball world—a baseball world defined by exit velocities, launch angles, and Wins Above Replacement (WAR).
The Oakland A’s achieved laudable success during those seven years. There is no argument. The issue is that people who look at that success don’t acknowledge or factor in some things. One is that the Oakland A’s had three pitchers acquired before Beane (Hudson, Molding, and Zito) that accounted for an average of 66 wins from 2001 to 2004 (Daryl Horowitz, Bleacher Report, 9/ 22/ 2011). People should also consider that over 12 years of Billy Beane’s reign; the Oakland A’s averaged a pedestrian win-loss record of 78 wins and 83 losses. Over his 19-year tenure, the Oakland A’s averaged 85 wins and 78 losses.
I’m not anti-analysis. I’m anti-misuse of analysis. There is a troubling social and cultural trend of groups of people so profoundly vested in reconstructing the world we live in that the only way they can do that is by attacking and invalidating all previous value systems used. Evolution and refinement are not good enough. “We created New Coke, and you better accept it because we say so,” and we know how that turned out. There is an arrogance and air of certainty exhibited by many advocates that, quite frankly, hasn’t been earned. Many new statistics will help develop strategy, identify tendencies, and put your players in the best positions to succeed, such as defensive shifts. Using it to evaluate a player’s performance may just be another data point for consideration.
The turning point for me regarding advanced metrics was in 2012. There was a debate about whether Miguel Cabrera of the Detroit Tigers or Mike Trout of the Los Angeles Angels should win the American League MVP that year. A knock-down drag-out ensued between the Stat Heads and the Old Heads over which criteria should be used to determine who was the Most Valuable Player. The “Old Heads” continued to see traditional statistics like Batting Average, Home Runs, and Runs Batted In as criteria for the award. At the same time, “Stat Heads” argued that the only way to determine value was by using the new metrics.
It was a pissing contest that included accusations, name-calling, and certainty. The debates and arguments were like those occurring daily between warring generations on social media.
One critical new statistic was On Base Percentage (OBP). OBP is the sum of Base Hits, Walks, and Hit By Pitches (HBP). This sum is then divided by the number of At Bats. Stat heads see this statistic as an improvement over Batting Average. They claim it makes batting averages irrelevant. That’s nonsense. It’s additional information that focuses on a player’s ability to get on base. It is useful, but there is nothing that supports their claim. It is additional information, but it’s not a replacement for Batting Average. One of its flaws is that it gives walks and hits by pitches the same value as base hits. The last time I looked, the only times a Walk or Hit By Pitch can result in an RBI is if the bases are loaded. To coin a phrase made popular by Herm Edwards, former coach of the New York Jets,” We bat to hit the ball.” The Stat Heads argue Batting Averages are irrelevant. I believe that the OBP statistic has value, but it is more of an embellishment of the more meaningful Batting Average statistic.
I’d like to address the second new statistic: On Base Percentage plus Slugging, or OPS+. This statistic is a marriage of the new and old school statistics. I will be honest with you; in my opinion, this statistic isn’t very useful. The two individual figures tell you more about a batter than the sum of their parts. This calculation never establishes a relationship between the two variables. It’s just the sum of two numbers. It would be like analyzing a person’s general heart health by concluding by adding their blood pressure to their resting heart rate.
Wouldn’t it be more meaningful to apply a weight to each way a ballplayer gets on base and then divide that by the number of successful At-Bats? It would give you a measure of how valuable each successful At-Bat was. For example, Player A averaged 1.5 bases as opposed to Player B’s 1.1
Of all the new statistics used to make a case for Mike Trout being American League MVP, Wins Above Replacement, otherwise known as WAR, was front and center.
Major League Baseball states on MLB.Com:
WAR measures a player’s value in all facets of the game by deciphering how many more wins he’s worth than a replacement-level player at his same position (e.g., a Minor League replacement or a readily available fill-in free agent).
For example, if a shortstop and a first baseman offer the same overall production (on offense, defense, and the basepaths), the shortstop will have a better WAR because his position sees a lower level of production from replacement-level players.
WAR quantifies each player’s value in terms of a specific number of wins. And because WAR factors in a positional adjustment, it is well suited for comparing players who man different defensive positions.
There are several different versions of WAR, but I will focus on one, which is the Baseball Reference version which is called bWAR. Baseball Reference contends that you can determine who is the Most Valuable Player of a league not by comparing their actual accomplishments during a season but instead uses this formula:
Players Runs over Replacement = Player_runs – ReplPlayer_runs = (Player_runs – AvgPlayer_runs) + (AvgPlayer_runs – ReplPlayer_runs)
Now all this formula is doing is breaking up runs over replacement into two categories: how many more runs is a player responsible for than an average player (known as Runs Above Average), and how many more runs is an average player responsible for than a replacement level player (which does not depend on the player in question).
WAR is an analytical fiction that wraps itself in a cloak of relevance created by an aura of statistical validity. The effort to develop a statistically valid way of evaluating the pertinent characteristics of a ballplayer’s contributions to their teams’ success or lack thereof is laudable. Still, there are legitimate questions to be raised about these efforts.
The first significant challenge is numerous variables contribute to winning, and 1. You must identify those variables that contribute most to winning, 2. Define the relationships between those variables, 3. Determine which variables need to be controlled, and 4. Verify your assumptions.
Including minor league replacement players in the calculation raises questions about the methodology. When Major League teams must replace a ballplayer, they very rarely reach down to the Minor Leagues to replace that ballplayer. They usually replace that ballplayer with someone on their Major league roster or orchestrate a trade with a team for a Major Leaguer. Baseball Reference’s bWAR assumes that a team consisting of Replacement Players will result in a 48-114 win-loss record. This doesn’t happen often. Over 146 seasons and 377,340 games, as of 2017, there have only been 22 teams that have won 48 games or less during a season.
Like all other team sports, success in baseball depends on the interplay, coordination, and synchronicity between teammates. It is especially true when it comes to fielding. How do you determine what was more important in successfully turning a game-ending double play? Was it the range exhibited by the shortstop or the ability of the second baseman to pivot, avoid the slide and get off a strong enough throw to get a speedy runner out? How important is it that a runner thinks twice before going from first to third on a single when your right fielder has a cannon for an arm? How does playing behind a pitcher that throws an effective sinker reflect on an infielder’s WAR? If these types of things are important to you, how do you account for them and their impact on that player’s performance, and if not, how do you control these variables in your model.
In my decades of experience, first as an athlete and then as a fan, the notion of being the most valuable athlete in a sport required notable team success or achieving something viewed universally as historic to compete for that title. As a rule of thumb, a player’s team that does not have above-average success affects their chances of winning the Most Valuable Player award. Usually, that player would have to perform at historic levels to receive serious consideration. During my review of material for this article, I could not uncover any indication that WAR supporters consider these factors. It cracks me up when I hear commentators state that Mike Trout has performed at historic levels based on WAR. I always thought that qualifying as historic required a passage of significant time. WAR didn’t even become a thing until approximately 2012. For the record, Miguel Cabrera and the Detroit Tigers made won their division (team success), and Cabrera was the first person to win a Triple Crown in 42 years (historic accomplishment).
More than any other sport, baseball has been memorialized by numbers recognized by almost every American sports fan; numbers like 714, 755, 61, 56, and .406. For most fans, we know what they mean and which players accomplished them. The greatness of these players or the magnitude of the moment was not established by a Rube Goldberg machine that hasn’t yet established validity or earned the respect it so arrogantly demands. Their value was established through a historical context and lens and by direct comparisons between them, contemporaries, and or players that preceded them. Improved analysis of the game has undoubtedly made significant contributions to strategy, techniques, and tactics but the determination of the MVP of a sport has no choice but to be subjective because of the myriad of factors to be considered. I commend the Baseball Writers Association of America (BBWAA) for recognizing that in 2012.
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