Graphs, Hitters, IceBat

Home Runs Follow Up

As I stated in my last post, predicting home runs from fly ball data could be another component of how we compare home run hitting abilities of players. As you can see from the graph above, I plotted the fitted values from the model split by each hitter. Most of the fitted values tend to be at the extremes, which coincide with logistic model properties. As for comparing hitters, it looks like Adam Dunn has the most predicted fly balls becoming home runs, while Jason Bay is on the other side of the fence.

In case you were wondering, here are each player’s mean HR prediction for fly balls: Adam Dunn (~50.4%), Manny Ramirez (~39.2%) and Jason Bay (~34.0%). No surprises there really. The order of these hitter’s HR predictions coincide with their career HR/FB rates and our general notion of their hitting style. Dunn swings for the fences or strikes out otherwise, while Manny and Bay display more use of the entire field (though that may be an optimistic statement about Manny’s capabilities now). While Hit Tracker does an excellent job telling us how far home runs really went, and what park/weather factors impacted the ball’s real projection, we don’t really have an idea of what those factors had on non-HR fly balls. Though I am speculating, maybe this topic of research will increase once the data from Field F/X (previewed in THT’s 2011 Annual) is published. Only time will tell…

Graphs, Hitters, IceBat

Home Runs: They May Deceive You

First of all, Happy New Years everyone! Hope you had a fun time doing whatever you do on these holidays. IceBat was a party pooper and decided to sleep all night in his freezer bed.

As you may remember, back in December I had to complete a couple of final projects. One idea that I didn’t use dealt with the concept that home runs are not always equal in displaying a player’s power or batting skills. We¬† equate overpowering shots to right-center field by Prince Fielder with balls that graze the more-than-generous right field wall of Yankee Stadium. What I mean is, there are more variables than just pure distance that go in to determining whether or not a fly ball becomes a home run. With this in mind, I can run a regression model to compute the probability that a flyball will turn in to a home run. I received a large data set (many thanks to Greg Rybarczyk at Hit Tracker) that spans the 2006-2008 seasons for three players (Adam Dunn, Manny Ramirez and Jason Bay). The data includes observational and calculated data (in the similar ways of Hit Tracker’s data – i.e. True Distance or Elevation Angle, etc.) on every long fly ball the players hit, totaling a tad over 700 observations. Included are variables such as what ballpark the ball was hit in, date & time, and the outcome of the play (single, double, home run, out, etc.)

As you can tell from the graph above, the outcome of the play isn’t so clear when only given the elevation angle and distance traveled summary of the ball. All the outcomes are generally scattered so that we cannot conclude any real correlation. I superimposed two boxes to easily show how similar balls can have different outcomes. In the case of the right-side box, a slightly different elevation angle could mean the difference between a home run and a fly ball.

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Hitters, IceBat, OffSeason, Salaries

MLB Free Agency Modeled-Out

Hey folks. Sorry to keep you all at bay these past couple of weeks. IceBat was…sick.

Another final report I wrote was based on MLB Free Agent contracts, and how or if we can model their outcomes based on prior years’ performance. The contract terms I used as response variables were contract length (in years) and average salary per season. I also focused on hitters and how metrics like Batting Average, On-Base Percentage, Home Runs, or even advanced ones like Wins Above Replacement (WAR) can help us see what the market is favoring and at what price. The reason to use different sources of metrics is to see what MLB Executives are listening to: traditional statistics or those advanced ones used by the Sabermetric community? Using models like this can also have some predictive powers.

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Graphs, Hitters, IceBat, Pitching

No Time!!

Crazy schedule for me these next few weeks. I’ll try to stay active, I promise. If not, IceBat will take over, but I’m guessing he won’t say much (he’s pretty shy and likes to chill in the corner of my room). Anyways, I thought I’d share a recent report I did for my times series class. It’s about the general shift of runs scored per game (by one team) over the years of MLB’s existence. If you have some time (and enjoy a few technical terms) I’ve uploaded a link below. Happy December holidays!

R.G in MLB History

Graphs, Hitters, Pitching

How Consistent are Baseball Players?

Batting averages, on-base percentages, and ERA are all standard metrics used to compare baseball players. But how do they help us to determine a player’s day-to-day production? I wanted to look at two player’s overall game performance over the course of 100+ starts. Let’s begin with Albert Pujols.

I think it would be an understatement to say Pujols has been the most consistent hitter in baseball over the past 5+ years. He’s a sure bet to be in the MVP talks from the beginning of each season. I decided to look at his Win Probability Added per game (a plus WPA means he added towards winning the game, while a negative WPA suggests a player was detrimental to the winning cause) over the 2010 season.

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Coolest Website

I discovered the most awesome baseball data website ever (besides Fangraphs), and I wanted to share with you.

Hit Tracker is a website that tracks every single home run hit in the majors. With every home run, it gives an analysis of the ball’s path, from hitter to fan. The main categories of stats to look for on a player’s profile of home runs are True Distance, Speed of the Ball off the bat, the angle of elevation and wind/temperature conditions in the ballpark. All these statistics will give you a rough estimate as to whether the player was lucky to have it land out of the park or not. Another added bonus to the site is that they categorize each home run into: “Lucky”, “Just Enough” or “No Doubts”. This allows you to gauge a hitter’s power beyond just the amount of home runs he has, or by a slugging percentage.

The variation in true distance is drastic, yet the same outcome occurs. Jimmy Rollins can get lucky by hitting a ball 380 feet out in Yankee Stadium that goes out, while Mark Reynolds can hit one 480 feet for a ‘no doubt’ trajectory, yet the return is the same. Seems a little unfair, no? Wouldn’t it be cool for a player to scout wind/temperature stats and know which field and at what vertical elevation to get it pass the fence? For football, kickers scout wind and trajectories of balls all the time, wouldn’t it be cool for hitters to do the same? I guess it’s different though, since the hitter doesn’t have full control of what the pitcher serves up.

Check it out sometime. They also have links to watch the home run clips.


Is Derek Jeter worth $161 million?


He has been known throughout his career as one of the most respectful, consistent baseball players of his era. Once known as the ‘big three’ shortstops with Alex Rodgriguez and Nomar Garciaparra, Jeter might be the only relevant player nowadays (with Garciaparra constantly injured and A-Rod with his name in the tabloids). It may be true he is a poster child for baseball, that his manner both on and off the field is the most desired by managers. Oh, and some consider him a meat head (i.e. Linda) or a cute guy with a nice butt (i.e. Hana). But the real question is, is Jeter worth the $161 million he has accumulated as salary for his services from the Yankees (and furthermore, is he worth the $189 million contract he received as a 26 year old)? Well, based on…:

Postseason Play

Jeter has appeared in 6 World Series, helping to win 4 of them and receiving MVP honors in the 2000 series. His career postseason numbers are very respectful as shown below:



































But just because his statistics are favorable and the fact he was known as ‘Mr. November’ doesn’t tell the whole story. When people talk about postseason play, they regard players who do well under pressure as clutch players, that without them the team wouldn’t have been as successful. Jeter is one of those guys. The problem with weighting postseason play is that great playing ability might still be underperforming for a player, as is the case for Jeter. A statistic used known as Wins Probability Added shows that Jeter is at a -0.33 for his career in the postseason (from 2002-2007), while his WPA for his career during the same period is at a 13.85 clip .

What does this mean? Despite his great numbers in standard baseball statistics, it is evident after signing his huge contract he didn’t have much to offer the Yankees in the postseason. WPA doesn’t go far back enough to see how his play helped the Yankees to win the 4 world series rings, but this does show how he can’t be considered as Mr. November every postseason he is in. Fans give too much weight to a small sample such as 4 years, where players play only about 15 games max if they get to the World Series.


You might see Jeter make amazing looking plays in the field, but the truth of the matter is those plays would look like routine plays for top tier shortstops like Miguel Tejada in his prime. Jeter has been criticized for his small range and his footwork. His Ultimate Zone Rating (a statistic that sums the runs value a fielder adds to a team compared to an average replacement) has been consistently below average. Thus there is much skpeticism as to whether or not he deserves the numerous Gold Gloves (an award for elite defense) he has been awarded. Only starting this season has Jeter shown major improvement in fielding the toughest position in baseball.

Yankee Revenue

The Yankees, on average, receive about $300 million in revenue each season. Obviously, this figure changes each year, especially with this year’s arrival of the new palace Yankee Stadium. Over the past 10 years, the Yankees have received $81 million in extra revenue due to their post season success. How much of this can be credited to Jeter? As a ten time All-Star, it’s obvious New Yorkers come to see Jeter play day in and day out. In order to get to the playoffs, a team must have a solid regular season record. As I stated earlier, Jeter has a pretty high WPA for his career during the regular season. Thus, he can be attributed to both the economical success of the Yankees during the regular and post seasons. Merchandise and advertisement as the proud Yankee captain can also be credited to his name. Thus all said in done, it seems evident Jeter makes the Yankees a ton of money. He’s been on top of the boards almost every year in offensive statistics, as well as maintaining a leadership position in the world’s most scrutinized city, where tabloids can ruin your name (see Alex Rodriguez).

Say if Jeter was replaced by an average shortstop because they didn’t want to give him the money he ‘deserved’. New Yorkers would go mad and hang the owners of the Yankees, the Steinbrenner family. But what if the Yankees had an up and coming shortstop who had stellar defense and could hit on par with average major leaguers? Jeter has gaping holes in his fielding abilities, which makes his bat less of a threat. In 2005, the Yankees were the worst defense to have made it into the playoffs. With a cumulative UZR of -130, their offense made up for it with a sum of +139 runs added. It’s no wonder they’re called ‘the Bombers’, but with no fortified steel defense.