Kevin Nunn
Dr. Wooten
Econ 315
March 16, 2016
Player Evaluation Project Memo
I decided to do the "Money Ball" project due to a higher interest in baseball than in
teachers in Texas, no offense to Texas teachers. I first found the team stats for every team in
Major League Baseball last year, including win percentage, on base percentage, slugging
percentage, attendance, and ticket price. I used the OBP and SLG to find the production index
for each team. This formula was OBP*2 + SLG. That was multiplied by 100 so that it was easier
to interpret the numbers (=100*(2*C2+D2)on the excel sheet). I then multiplied the average
ticket price by the home attendance of each team to find each team's revenue. The Blue Jays and
Rangers stood out as teams who won a lot, but were not making a ton of money. The Red Sox,
on the other hand, lost more than they won, but had high revenue.
As for the scatter plots, the trend lines on these showed three things: first, the better a
team hits, the more they win (an additional point on the production index adds an average of
.0106 to their win percentage). Next, the better a team hits, the more money they make (an
additional point on the index would add an average of $5,027,432.86 to their revenue). Lastly,
the more a team wins, the more money it makes (an additional .001 on the team's win percentage
will add an average of $244,641.10). This means that good hitting not only wins games, but it
makes money too.
This brings the free agent players to table. The goal with the free agents was to measure
the amount of revenue each player brings in to a team against how much they signed their most
recent contracts for.
I started by picking 25 players from the list at random. The method behind that was to let
other people pick from the list for me. I allowed two girls that I was in Florida with, both
admittedly knowing nothing about baseball, to pick the 25 players for me. I felt like that was
about as random as it could get. The only themes to the list may have been former Philadelphia
Phillies and "guys with cool names," but the list seemed plenty random for my liking. I next
looked up the on base percentage, slugging percentage, and upcoming salary for each player. I
used the OBP and SLG exactly the same way as I did for the teams, finding the index for each
player. I then compared each player to a player at the "Mendoza Line." With an OBP of .250 and
SLG of .300, that player boasts a lowly index of 80. I used a formula that subtracted the
Mendoza line player index from the free agent's index (=E2-$E$27) to find the difference, and
then in the next column I divided that result by ten, because the average batter takes one-tenth of
his team's at-bats. I then multiplied that number by the amount that team revenue rises with a
one-point rise in the index ($5,027,432.86) and added the league minimum ($400,000) to find the
extra revenue that the player would bring in. This is listed in column H
(=(G2*5027432.86)+400000) and creates the revenue numbers. In column I, I used an if
statement comparing the players actual salary to their revenue increase to the team, simply listing
if they were underpaid or overpaid. Then, in column J, I noted by how much each player was
under or overpaid. I finished by plotting both the actual salaries and the predicted salaries, so that
I could also compare them on a graph.
From the information gathered, I concluded that a majority of players bring in more
revenue than the amount that they are paid. I would argue that if most are being underpaid, the
owners are doing a good job at negotiating with the agents of these players. To be fair to the
owners, if all they were doing with these players was breaking even, they would be wasting their
time even negotiating. These owners are business-people who are trying to make a profit on
every deal they make, and that includes signing players.
I also noticed that OBP and SLG may not tell the full story. I look at Eric Young Jr., for
instance, who is making a million dollars next year, but is listed as a decrease to a team's
revenue. Though he has a low slugging percentage, he is known to steal bases and field very
well. Saving runs can be just as important as scoring them, and he can do both.
I would also argue that overpaying guys like Yoenis Cespedes is not a bad idea either,
because adding players like him to a team on the cusp of a world series may end up being a great
investment. The Mets could make so much more money by winning a world series, so
overpaying the Cuban superstar may be a great long-term investment.
Lastly, I am amazed at how much one single index point can make a player. Baseball is a
game played over 162 games, and the index/ team revenue scatter plot shows that a few more
hits a week could make a player, and the team he plays on, a lot richer.
All-in-all, this was a good look inside the idea of "money ball," and this project is
probably only a small peak inside the window of a larger relationship between money and player
performance. Players make so much revenue for teams, and it is no wonder that they are paid so
good.