Third Basemen - Shortstops - Outfielders 1 - Outfielders 2
Starting Pitchers 1 - Starting Pitchers 2
Fantasy baseball is one of my favorite parts of the MLB season. I'm an avid player and a pretty successful one for that matter. I've won my league 2 years in a row now and I have some strategies I use every year to help me out. If you read this blog consistently, you know that I'm really into sabermetrics and more in depth, predictive statistics. Every year I take a look at players and their performance in certain sabermetric categories and use that information to decide whom I want for my team. It may be just luck, but I've found success with some of these things, and this year I'm going to share some of my ideas with everyone.
For this opening post, I'm just going to explain the statistics I use. Here's the 6 stats I'm using in my analysis:
Batting Average (AVG): Everyone knows what batting average is. It's not a perfect statistic by any means, and a lot of luck and random chance go into it, which explains why different players will have huge fluctuations in their averages from year-to-year. It is simply the percent of times that a hitter gets a hit for all of their at bats (which excludes walks, hit by pitches, and sacrifices). Batting average is one of the most looked at and easily available statistics out there, so this is really just a stat to ease us into this process and give everyone a shallow, yet clear look at how successful hitters are.
Batting Average on Balls In Play (BABIP): For hitters, BABIP looks at all the balls the batter put in play that year, excluding home runs, and finds the percentage of the balls that went for a hit. This statistic tries to show us how lucky a hitter was with his contact. Baseball is a game of inches and a microsecond of difference during contact with the ball can change a hit into an out. Because of this, luck is a huge factor in batting averages. A hitter can make extremely weak contact and get a bloop hit, and then next time square a ball up and crush it, but right at a fielder and record an out. There's a ton of random chance that goes into the difference of a hit and an out, so BABIP tries to show you how lucky they were. An average BABIP is about .300, so if a hitter has a significantly higher number than that, it shows that they were "luckier" than the average major league hitter, and that if there luck regresses to average in the next sample size, there batting average will be higher (and vice versa).
On-Base Percentage (OBP): On-Base Percentage simply looks at all of a hitters plate appearances in a sample size, and finds the percentage of times that hitter reached base in the sample. This is a major indicator for how successful a young player can be in the major leagues. If a player has a significantly higher OBP than AVG, it shows that they are walking a lot, which shows good pitch recognition and patience in the hitter, which always results into good things. Also, as the hitter gets into deeper counts, they are more likely to get more favorable pitches to hit. If you're looking at a young, developing hitter, it is certainly helpful to look at his OBP numbers to determine how he will perform in future years.
Isolated Power (ISO): This one is a little bit more complicated. Isolated Power is a measure of a hitter's raw power, in terms of extra bases per at bat. The formula for it is ISO = (2B + (3B*2) + (HR*3)) / AB. As you can tell from that, singles hitters are not going to have a high ISO. The more doubles, triples, and home runs a batter hit, the higher his ISO will be. We can use this statistic to work with the general home run and RBI numbers to determine what kind of power we can expect from a player. Those home runs and RBIs are crucial in most fantasy baseball leagues, but they can fluctuate from year-to-year pretty easily, especially RBI. ISO gives us a truer feel for how effective a hitter is at driving the ball to gaps or over the fences.
Line-Drive Percentage (LD%): This one isn't as popular a statistic as the other ones mentioned, but it's a favorite of mine. This shows you the percentage of times a hitter hit a "line drive" per times he made contact. The term "line drive" is somewhat debatable, but this stat is mainly useful for showing us how often a hitter made solid contact. Good contact can turn into an out very easily, which goes down into the scorebook the same way a weak pop-up would, but hits and outs are not a factor in line-drive percentage, so we can use it to see how well the hitter sees and swings at pitches. If a hitter has a higher than average line-drive percentage but an average or below average batting average, we can say that that hitter was unlucky in that sample and we can fairly expect them to improve in batting average, among other more well-known categories.
Home Runs per Fly Balls Hit (HR/FB): HR/FB is the ratio of how many home runs a batter hits per fly ball he hits. This statistic isn't the greatest way to predict future power numbers for a hitter, but it is useful in the way I am going to use it. Players that play in home-run friendly ballparks are likely to have higher HR/FB percentages, so you have to account for that. This statistic will be used in this study to compare a players 2010 HR/FB ratio to his career average HR/FB ratio. So if a player had a significantly higher ratio in 2010 than he has the rest of his career, we can suspect that there was luck involved and we won't be surprised to see his power numbers fall the next year, and vice versa.
So those are the statistics. Now let's talk about how we're going to use them.
For every player involved, I am compiling his 2010 numbers in those 6 categories and comparing them to his career averages in those same categories. This way we can see who over-performed and who under-performed last season. Age is a huge factor when considering this, because a player in his mid-20's will be expected to improve, while a player in his late 20's to 30's will usually stay the same or get worse.
So we're going to try and use this comparison of numbers accompanied with the age of the players to intellectually predict whether the player will get better or get worse statistically in the 2011 season.
For a quick example, we'll take a look at "player x":
Player X, 32 years old:
2010 Season: .302 AVG, .335 BABIP, .400 OBP, .146 ISO, 20.1 LD%, 7.2% HR/FB
Career Average: .260 AVG, .280 BABIP, .353 OBP, .136 ISO, 18.5 LD%, 6.8% HR/FB
From these numbers we can draw the following conclusions:
- Player X had a very above-average 2010 season.
- Player X is at the age where he should be done improving, which makes us take his improvement much lighter.
- Player X had a very high BABIP in 2010, 55 points higher than his career average. That is not likely to happen again, so we can expect his batting average to drop significantly in 2011.
- Player X posted pretty average power numbers in 2010 (for himself). His ISO, LD%, and HR/FB were all a little bit higher than his career averages, but really not by much, which shows that most of his improvement in batting average came from singles.
- Player X will almost more likely than not have a worse 2011 season than he had in 2010. Player X is Carlos Ruiz. So there's a little sneak peak. Anyways, you can draw many different conclusions from different statistic samples. It's not a fool-proof method by any stretch, and I do not consider myself an expert in sabermetrics. I may not be the greatest at explaining all of my thought processes, but I will do my best and hopefully I can provide some interesting and helpful insight. The series will kick-off in the next day or two, we'll start by looking at and ranking my top 15 catchers. Be sure to check back for it.