Sabermetrics from the Perspective of a Professional

On February 21st, I interviewed LA Angels outfielder Daniel Robertson about the role of sabermetrics in baseball, at multiple different levels. He gave me a perspective I hadn't seen in the numerous articles and books that I read about sabermetrics and how it's used in lower baseball levels. To him, sabermetrics were primarily useful for seeing how good a player is at a certain time, which is true in the sense that there aren't really any reliable statistics that are capable of projecting accurately how a player will improve primarily due to the unpredictability of prospects in baseball. Daniel Robertson had this to say about statistics in college:

"I think using them for maybe college seniors or college juniors, as opposed to freshman or sophomores, you can get some kind of a player or get a rough idea about a player. Going into pro ball, I think it’s already used in the minor leagues, not for every player, but for the top prospects, so you can see how their success in the minors correlates to the big leagues, but I think for early college students and high school students it’s too soon. It would be very unpredictable because you’re going to get kids that grow, kids that put on weight or lose weight. Also think it’s too much for a 19-year-old kid to worry about... But later, as a junior or a senior, you can get a rough estimate and a good base point."

This has a lot of merit in that in the first few years of most players in college, they're still growing and putting their own stamp on their game, which can change a lot about a player and makes sabermetrics very ineffective when used with certain prospects at that young an age. Sabermetrics, however, are very effective at determining how good a player is at that given time, which can be very useful as a tool for players to see how their game is improving (or declining), even if they aren't judged by the numbers themselves. It will be interesting in the coming weeks to see how sabermetrics can be used in practice with college players. 

My full interview can be seen on the pages tab of my blog.

Thank you to everyone who reads this and feel free to comment! 

Understanding the Complexities of Sabermetrics

"I don't like them fellas who drive in two runs and let in three."

- Casey Stengel, Hall of Fame manager 

Statistics have been closely woven into the fabric of baseball since its start in the late 19th century. Yearly battles for the home run crown have been nearly as intense as the coinciding division races ever since Roger Maris famously beat teammate Mickey Mantle in a race to break Babe Ruth's home run record. Fans have been gripped to their TVs and radios whenever a star player comes to bat or a star pitcher takes the mound as they fight to hit the most home runs or strike out the most batters that season. But as the sport has evolved over the years, statistics have been forced to follow, as more and more teams look for statistics that can replicate the "common sense" observations that people in baseball like Casey Stengel made. Every team wants to avoid the player who pulls you in with his high home run total but strikes out so much that he's actually a detriment to the team. The goal of sabermetrics is to avoid these players whose impressive counting stats tend to overshadow their overwhelming shortcomings in other areas while also highlighting under-the-radar players whose contributions are more subtle but are imperative to the success of the team.

Everyone knows that if you score more runs than the other team you win. There are multiple models to predict a team's winning percentage based on comparing runs scored and runs allowed. The most basic is this linear model: 

WP = .500 + β(RS − RA)

where WP is winning percentage, RS is average runs scored per game and RA is average runs allowed. β in this model has been determined over years of research to be approximately 0.1. A more complicated model is the famous one based on Bill James' Pythagorean Projection:

where γ is an exponent that after years of analysis has been determined to be 1.82 to 1.83. Both of these models are capable of fairly accurately predicting performance of teams in Major league Baseball. That said, a problem may arise in college baseball. 

Imagine a team always allows 4 runs. Another team, meanwhile, has one star pitcher that always pitches the whole game and only allows 1 run that game. The other 4 pitchers, meanwhile, always allow 4.75 runs per game. Both teams average allowing 4 runs per game, but in head-to-head competition the first team will win 4 out of 5 games. The inconsistency of the college game due to the incredible skill difference at times may make many sabermetrics impossible to implement at that level. My goal is to look further into the game with personal analysis of the University of Arizona baseball team's performance in order to see what sabermetrics can be implemented.

Sources:

Dayaratna, Kevin D. and Miller, Steven J., First-Order Approximations of the Pythagorean Formula, By the Numbers, 22 (2012), No.

1, pp. 15-19. 

McDonald, John F., Extensions of the Linear Runs-To-Wins Model, By the Numbers, 24 (2014), No. 2, pp. 7-11

Miller, Steven J., A Derivation of James’ Pythagorean Projection, By the Numbers, 16 (2006), No. 1, pp. 17-21.  

Introduction and Background

Hello! My name is James Parisi and I'm a senior at BASIS Tucson North High School in Tucson, Arizona. During this current school year, seniors at BASIS were given the opportunity to be involved in and do our own senior research project, what we call an SRP, in place of our third trimester of senior year. For our SRP, we pursue an internship with a specific research topic in mind in hopes that the internship can provide the tools necessary to answer, at least to some degree, our research questions as well as provide some much-needed experience in the workplace.

In my SRP I will be performing various tasks involving the organization and analysis of statistics for the University of Arizona baseball team. I will be primarily supervised by their national title-winning coach Andy Lopez, although I will likely spend most of my time with his current statistics team.

My project looks to determine how or why sabermetrics, a general term for all types of more advanced baseball statistics, can be used in college baseball. While they are already used extensively, it is not as apparent if they make any substantial difference for teams using them as it is in Major League Baseball, where it is well documented with books like Moneyball that sabermetrics are effective. My goal will be to see if sabermetrics are effective in the same way as they are in the MLB, and if not, what aspects of college baseball makes it less suitable for these same advanced stats.

I'm very thankful for this opportunity and I'm excited to see what comes of this project and my research in the coming months.