B4B

..that might be true if you’re in love with somebody 10 years your senior; but in fantasy baseball, age is everything.

You want a team full of players on the upward swing of the age curve:

The average player doesn’t “breakout” at age 27, they increase their skill level from age 20-26 (when the greatest chance for a breakout will occur), enjoy a season or three at their “peak”, and then decline thereafter. (Studies: 1 | 2 )

There’s hundreds of metrics that give us clues about any given players worth. As value detectives we want to pay attention to the right clues. We want defining evidence, not superfluous excess. So what data matters? What clues tell the real story? In this article I’m going to answer those questions by identifying the opposite.

1. Spring Training Cliches

We’ve all heard the hyperbolic statements given by coaches and players in the offseason. Some of us might’ve even bought into their optimism a time or two. In 2005 Adam Dunn said he would steal 40 bases, and Vernon Wells swore he prepped his body for a 30/30 season. Or what about 2006 when Zito claimed he rediscovered his 90-92MPH fastball? The list goes on…

Ultimately, almost none of these claims materialize into anything other than false hope.

My Theory:
By the time a player gets to the majors, he’s been through many off-season regimens (high school, college, minors, etc). Incentive is at its highest for young MLB prospects to “build muscle”, “lose weight”, “get eye surgery”, “find velocity” etc. If a player has shown the ability to increase his physical body–that results in higher production–it can be identified in his previous season statistics. There’s a prodigious amount of available and accessible data; trust it. (the right data of course)

2. Run Totals

Runs tell us almost nothing and are highly susceptible to off-season roster reshuffling. Instead of looking at a players previous run total, get a good understanding of where players are positioned in their line up, and how many runs that lineup can potentially produce. When targeting the runs category, find players who meet the following criteria:

• High BA/OBP
• Bats at the top of a run producing offense.
• Has speed as defined by the ability to bunt for hits, get infield hits, and steal bases.

3. RBI Totals

Apply many of the explanations given in #2. When targeting the RBI category, find players who can:

• Hit for power and average as identified by: a high Contact Rate, high FB% & high Isolated Power
• Bat behind players who have the same skill set as described in #2
• Play in a lineup that’s great at avoiding outs

* For team run production projections, visit Baseball Musings. Pinto, has an on going series using Marcel 2008 projections and the line up analysis tool. It would be a good idea to rank the top 30 and draft/pay accordingly.

4. ERA & 5. WHIP

In a previous entry I detailed why ERA and WHIP do a poor job at summarizing a pitchers skill level. You want to pay for pitchers who have been successful at:

• Striking out their opposition (high K/PA)
• Inducing ground balls (high GB%)
• Controlling the strike zone (low BB/PA), and
• Getting batters out efficiently (low P/PA)

If a pitcher had success in these areas last season–but his ERA/WHIP were poor–than he was probably on the wrong side of luck, and should be a nice value selection in your upcoming draft.

6. Save Totals

A players save totals from year to year are mostly just random variance. Elite, good, and average teams compete for the same amount of save opportunities annually. When targeting the saves category find pitchers who:

• Excel in the areas mentioned in #4/5, and
• are the best pitchers in their respective teams bullpen (eliminating competition issues).

7. Win Totals

Target effective pitchers with high IP totals on teams that win a lot of games. And even than, it’s still a matter of luck. Why is this even a standard category anymore?

So there you have it, a macro view of information and statistics you should ignore in place of more player defining alternatives.  How much do you agree or disagree with this list?

This table represents Average Team Save Opportunities from 2005-2007:

The top 10 teams (less the A’s & Angels) have been mostly “.500 ball clubs” over the 3 years represented. There’s good teams at the bottom, middle and top of the table, and excellent teams (Yankees & Sox) couldn’t be more in the middle.

Next time you’re undecided between Closer A, and Closer B, choose based on the Closers’ ability to miss bats not the quality of the team.

The amount of enthusiasm that goes into bashing any major online sports media that dare call themselves "Experts" has always baffled me.

It’s as automatic as death and taxes: If an "Expert" Cheat Sheet, Mock Draft, or Big Board gets linked to from a message board, expect the posters to wring out the work like a moldy wash cloth.

I stand in isolation on this one: but I’ve always found value in their rankings, cheat sheets, and mock drafts. How? I just assess the rankings from the bottom up.

How to Evaluate Pitching Statistics:

Most fantasy baseball veterans already use these pitching statistics and apply them “second hand”. But even the most seasoned veteran might find the tables and ranges I’ve included in this article useful.

One thing I’ve noticed when reading through many fantasy baseball articles, is the lack of in depth information about the statistics mentioned. I created this resource to compliment the great information that’s already widely available on the web. Next time, one of your favorite writers mention these metrics, you’ll know what they mean and how to apply them.

These statistics are skill based outcomes. If you start paying more attention to the following numbers, you’ll improve your pitcher evaluation methodology immediately.

1. Strikeouts per 9 innings (K/9)

Out of all pitching stats this one is most stable from year to year. In other words, if a pitcher strikes out 1 batter per inning one season, there’s a good chance it will happen the next: this is great because unlike ERA you have a better understanding of what you’re paying for. A pitcher with a high K/9 is less dependant on luck and defense, and the number directly relates to how deceptive his pitches are. Higher K/9 usually = more effective pitcher.

2. Walks per 9 innings (BB/9)

A low BB/9 signifies outstanding control and usually results in keeping opposing batters off the bases. It is important to keep an eye out for pitchers who are elite in this category to boost your teams WHIP: this simple statistic is often overlooked for K/BB ratio but it carries tremendous value standing on its own.

3. Ground Ball Percentage (GB%)

The theory is simple: pitchers who yield higher ground ball rates reduce the chance of giving up HRs. These types of pitchers are also less susceptible to HR prone ballparks. Like BB/9 and K/9, a pitchers ground ball rate is usually stable from year to year. GB pitchers with low walk rates and high strikeout rates are extremely rare commodities. The best pitchers in the game are simultaneously successful in all 3 of these categories. Felix Hernandez has tremendous upside for this very reason.

4. Fly Ball Percentage (FB%)

Simply reverse the explanation in #3. In theory you don’t want to own a Fly Ball Pitcher in a ballpark that gives up a lot of home runs. (In later entries we’ll talk more about those ballparks and more importantly how to build a ballpark defensible pitching staff).

5. Singles allowed per 9 innings (1B/9)

Singles allowed per 9 innings is a skill based outcome according to a study done by the fine Sabermetricians over at MVN. Their study concludes that pitchers have very little control over fly balls that turn into HR’s (which directly goes against many other fantasy baseball sites teachings who use DIPS to evaluate pitchers). I would highly advise you to read the study here.

After probing for more information the author (Pizza Cutter) had these additional comments:

Indeed, it looks as though 1B/PA is a little more skill based than we thought it might be, although it probably has something to do with the fact that strikeouts and walks per PA are very very skill-based and stable.

1B/9 doesn’t come without question marks, but could prove to be very applicable if proven as a skill based outcome. This would be monstrous for fantasy leagues that use the WHIP category. If you have the inclination Retrosheet offers the data in PBP format and it is downloadable for free.

Closing Comments:
All of these statistics are essential when evaluating a pitchers skill level and if utilized properly can help you find “diamonds in the rough” who are overlooked because of bad luck.

The sites in the table below offer statistics 1-4 on a per player and per league basis in a nice organized interface for free.