Good piece. It is hard to quantify the effect of veteran presence, and it may often be over-rated; but it does seem to have real value - especially in individual cases. Case in point: when the Nationals re-signed Adam LaRoche this week, Davey Johnson praised the move, using the term "clubhouse presence." Most of the analysis of the move has projected LaRoche's offensive and defensive production in comparison to that of Michael Morse - and most analysis gives an edge to Morse on that score. But one of the key reasons the Nats wanted LaRoche back was because he was the player that 19 year old Bryce Harper hangs out with on road trips. According to published reports, the two became very close. While other team mates would go out to a bar, LaRoche and Harper would go somewhere else and talk baseball. Johnson remembers, all-too-well, how the careers of Dwight Gooden and Daryl Strawberry, two comparable talents, were ruined by off-the-field influences, and I am sure that he was eager to keep LaRoche around as a stabilizing "big-brother" influence for Harper. That value may be very real, but it is psychological and developmental -- hard to objectively verify and very difficult to tease out of the statistics.

Nice piece. I would think that the right veteran (mix of personality, credibility, playing knowledge, and people skills that are best for a certain young player) can probably help a younger player that might need help. I'm sure making it to the major leagues can be an adjustment for some players, as can learning a new role there. Charismatic teammates can probably influence some young players, for good or ill. Some younger players probably already have the right mindset and likely need little help, others are likely mostly immune to peer influence. But there are probably some players that a good veteran, through setting a good example, being friends, or advice, might help.

It also might vary by team. If the team has several bad influences, it might be very important to have some good ones, too. If the team lacks bad influences, it might be less important.

Since this outcome is going to depend on the young player, the veteran, and the overall team environment, my guess is that statistics offer little help with this, as there are too many variable we lack information on and are hard to quantify.

Related to this, I've often wondered if the age distribution of a roster has any detectable influence on team performance- e.g., would a team with a relatively even distribution of a handful of rookies, a handful of young post-rookies, and some early and late veterans perform better than a team made up predominantly of veterans or young regulars?

I can't imagine an easy way to statistically test this, what with the thousands of other variables involved, but one could start by dividing teams into age-distribution categories and comparing records.

Good article. This question, like many questions that people try to tackle in sabermetrics, is probably fundamentally not addressable with OBP (or WAR, or VORP or whatever).

Veterans, club house chemistry, and so on and so forth, these and umpteen other factors, they affect different players in different ways. Perhaps a veteran leader shows a kid a couple of tricks to deal with the daily grind, extending his longevity. Perhaps being in a series of good clubhouses encourages a player to keep his love of baseball and hence good habits... Perhaps... Perhaps... Perhaps...

I love digging into the fog of this game we all love, but as a wise sabermetrician advised, it must be respected. As highly trained, professional, and mechanically perfect baseball players may be, they're still people, with emotions, preferences, tastes, quirks, and flaws.

Methodological question-

When you controlled for the first season's OBP, did you regress the change in OBP (as a dependent variable) against the first year (explanatory variable)? This will give a biased result. The correct way is to use the sum (or average) of the OBP for both years as the explanatory variable.

You probably know better, but I see this error all the time.

I gather you are evaluating the proposition that the improvements to the young players happen in the season the veteran is kept around. What about in later seasons? If the veteran spending a season with the younger player is a "treatment" for immaturity, might not the effects come out later?

Some supposed "veteran" influences, turn out to do the reverse - see the Omar Vizquel incidents in Toronto last season. The oldest man in baseball ended up throwing management under the bus for a lack of direction on the team, failed to point out Yunel Escobar's faux-pas when he should have known better, etc.

On the flip side, look at the recent stuff emerging about 21 year old hockey star, Tyler Seguin - http://sports.yahoo.com/blogs/nhl-puck-daddy/tyler-seguin-spends-lockout-living-abject-squalor-leaves-210150618--nhl.html

You're only capturing about 50% of Mark Kotsay's last 4 years on the bigs with your sample. Any methodology should start with him and work backwards to define the set

I totally agree with your last paragraph. People are so eager to claim that outliers will not repeat that they miss the very real possibility that a certain human being might behave abnormally in certain situations due to a very real and repeatable cause. Josh Hamilton cannot possibly have persistent day/night splits bc most players do not. Jered Weaver is apparently no more likely than the average pitcher to outperform at home next year. Justin Upton's ridiculous career home/road splits: sheer noise. Luke Hochevar: chronically unlucky, mostly because Hellickson stole all his luck. The list goes on and on.

One reason I am confident that people have gone off the deep end in applying generalizations to tail cases is that I have made a living betting on baseball for the past 10 years, and it's been in no small part due to taking the other side of these inaccurate predictions of mean reversion (provided the sample is large enough to refute it).

I hope someone here or elsewhere can devote an entire series to examining serious career outliers (in FIP vs. ERA, handedness splits, etc.) apart from the rest of the population and determine if such players should be lumped in with the rest of the player universe, or rather if we should be applying different predictions of regression.

Russell,

Excellent work, as per usual. I meant to ping you when I saw the chat comment, and I couldn't agree more about the large-N model.

I remember learning something in stats class about applying the results of large-N studies towards similarly robust samples - if 500 players tend to regress toward a mean in one season, then one might expect a similar sample to follow the same trend. But the model breaks down on a case-by-case basis.

Besides, individualized analysis opens up so many fun research questions, many of which don't enter the analytic framework of a large-N model.

Really interesting point, Russell — I was intrigued by your quick comment about large-N research in the chat, and this explanation adds some clarity to it.

But do you see people moving away from these studies toward more case-by-case research? It's hard for me to imagine how to even structure such studies. Longitudinal data focused on individual performance, perhaps?