If you are a fan of Major League Baseball, you have probably heard of the term Moneyball, a term for the approach taken from now legendary Oakland A’s General Manager Billie Beane. Beane’s concept was to use his small salary cap to shrewdly pay for players that put up the right stats but did not command large salaries for it in order to maximize the only stat that truly matters for a ball club: wins. He started a data analytics revolution in baseball, and now every team hires top notch statisticians in order to maximize the amount of wins they get from their players for every dollar they spend on them. But still today, some teams just remain better at it than others, and the data shows a very strong correlation between how efficiently teams spend their money and how many games they win in a season. Look at the right data you will be able to see just how well your favorite team is spending its money, and whether or not your favorite player is hurting his team by getting over-paid.
First, we must understand how exactly this data works. Using statistics obtained from USA Today and Fangraphs, I charted two key stats for all players in the MLB that those stats were available for over the course of the 2018 season: Average Annual Salary and Wins Above Replacement. For those unfamiliar, Wins Above Replacement, or WAR, is a model that calculates a combination of different statistics that have an effect on the game of baseball to determine how many more wins that a given player will generate over the course of a season than a replacement level player. So in theory, your average player that a team can sign off the waiver wire should have a WAR of zero. On the other hand, Mike Trout, who is thought by many to be the best player in baseball, has a WAR of 9.8. This means that signing Mike Trout should generate about ten more wins in a season than signing a random guy of the waiver wire. This might not sound like much, but it is enough to drop almost every first place team out of the top spot in their division (with the exception of the Cleveland Indians, who played inwhat was by far the worst division. No other team in the AL Central even broke .500). Comparing WAR and Average annual Salary for 2018 creates a graph that looks like this.
The line through the middle of the graph is called the line of best fit. It essentially shows where a players salary should be based on their WAR by comparing every data point on the chart and generating a line that is the shortest distance away from every single point on the graph. A player with a point higher in comparison to the line is more valuable to a team per dollar they receive. The value that measures this distance is called a residual (r), which is found by simply subtracting the value predicted by the line of best fit from the actual observed value. According to the comparison of these two variables, Mookie Betts is the most valuable player in baseball for the money, with a residual value of of 8.9, almost a full point value higher than his closest competition, Jacob deGrom (8.1) and a point and a half higher than the previously mentioned best player in baseball Mike Trout (7.3).
On the other side of the coin, Chris Davis is the least valuable player per dollar, by a similarly large margin, standing a full point below his closest competition.
Of course, these numbers mean nothing if they do not indicate anything of a team’s performance. Fortunately, when all the residuals for a team are added together, they show a very strong correlation between a team’s residual total and their win total, with the r squared value landing at .809. What this means is that about 81% of the variance in a team’ s wins can be predicted by their residual total.
It is fairly clear just from looking at the data that as the total residuals go down, so do the wins tend to go down. Even in baseball, where there is no hard salary cap and teams are able to outspend others based on their market size, efficient spending on productive players can have a real, positive effect on winning. It should come as no surprise that Billy Beane’s Athletics are still near the top of this list all these years later.
For those interested, you can view the full data set HERE.