B4B

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?