(1) We need to first establish an agreed upon definition of streakyness, because player performance always varies in baseball. To me streakyness is “statistically significant variations in player performance which occur in a random pattern.” Using this definition at least two of your streakiest cases are not streaky at all because the variation in their performances is not random. Therefor their data, and similar cases, should not be used in your year-to-year correlation.

(2) “The one relationship that was statistically significant was a weak negative correlation between streakiness and plate appearances (r = -0.061, p = 0.016). It is tempting to think that this may suggest that better players (who play more) are less streaky, but this is unlikely.” – Actually this correlation is just a statistical representation of something we know to be true. As n increases we approach the true mean of N.

This is a fancy way of saying that as the # of AB’s increase all players averages in all stat categories will regress towards that players mean for each category. Since there is a maximum of 162 games more AB’s in essence means more AB/Game. So if a player gets more AB/Game then their performance, when viewed on a game by game or series by series manner will appear more consistent. To look at it from a logical standpoint, If every player got 20 AB’s each game, then there would be a greater chance that they would post extremely consistent box scores.

When you look at it this way it makes incredible sense to see this correlation from a statistical standpoint. This correlation is not meaningless, but is totally anticipated and would be shocking if it were not statistically significant, or at least closely approaching statistical significance, every season.

(DAVID WRIGHT 2009) “Neither of the two David Wright seasons we looked at earlier makes the list, but his concussion-marred 2009 season was the streakiest in the league that year.” – In this case you variable is not streakyness. You are determining a players streakyness by analyzing performance statistics. These statistics, and your streakyness coefficient, are being confounded by a third variable.

Wright was injured. Injury affects performance. Your streakyness coefficient identified his performance variations as significant and therefore tagged his performance as streaky. Unfortunately your coefficient is neglecting the fact that these variations were not random. They were effected by a third variable which impacts performance. If you determined Wright’s streakyness coefficients for the smaller periods in between each of the injuries you are likely to get a very different value.

When you minimize the effect of the confounding variable (injury) Wright’s 2009 streakyness coefficients are each likely to approach those in other seasons. You don’t expect that performance statistics (especially counting statistics) would correlate well when comparing an injury marred season to a healthy season, so why would you expect that a streakyness statistic (if it exists) would correlate well from an injury marred season to a healthy one.

(BRENNAN BOESCH 2010) Brennan Boesch’s 2010 season wasn’t streaky in the least bit, but you list him as the streakiest player of 2010. Boesch was the model of consistency. He was consistently brilliant in his first 30-40 games or so, and consistently horrible the rest of the season. This change was due to a change in the way pitchers approached him, there is nothing streaky about it.

If you isolate his performance from before pitchers adjusted to him and seperated that data from his performance after made those adjustments you’d have two very different sets of statistics, and both would have similar streakyness coefficients. Boesch is not a streaky player because pitchers adjusted and he failed to adapt to that, he is just a younger player who’s talent was negated by pitchers who had figured him out.

Here your measure is again being affected by a confounded viable, although I have trouble giving that variable a name. It is not that common that a player is shut down as badly as Boesch was, but there was very little streak to it. Consistent brilliance and consistent darkness. Kind of like the extraordinarily predictable, and not streaky at all, rising and setting of the sun. So again your coefficient is identifying statistically significant differences, but failing to recognize that these differences are not occurring in a seemingly random pattern.

(4) So injury and other events in a players season are confounding your variables and really reducing the validity of this study. Without controlling for confounding variables there is no way to use statistics to represent a players streakyness. I use statistics exclusively in making fantasy determinations because I can’t watch a lot of games and because my eyes too often lie to me, only seeing what I want to see. Numbers can also be use to lie, and while your intention was to uncover the truth, you need to combine statistics and common sense in order to do that.

I am thoroughly surprised that you didn’t see these problems when Boesch and an injury riddled season popped up on your most streaky list. The only way to make any determination on the existence of streakyness requires using a very unscientific method. You need to pinpoint where the so called streaks which your correlation has identified are occurring in the season. Then you need to and attempt to correlate them with isolated events which you deem to have an impact on the players performance in the statistical category being evaluated.If you can correlate the players streak to a confounded variable you must control for that variable of exclude the data.

There is no way to do this objectively, and as such there is no way IMO that you can statistically evaluate streakyness. If you have any ideas for objectively eliminating (or significantly reducing) the confounding variables without eliminating too many valid cases then please let me know as I would be happy to brain storm with you.

The way I see it there is no truly valid point in time to say, this is the game pitchers figured Boesch out. Additionally how do you correct for playing a series in 100 degree arlington. This is likely to affect performance, but should the data from this series be excluded or controlled for. You could make a case, for it, but then you’d have to start controlling for playing in Minnesota in April. I just don’t see any way that you could statistically analyze streakyness, but I also see no reason to use this article and the data presented to discredit that some players are streaky. I know you say that this article is not meant to be absolute proof that streakyness doesn’t exist, and agree with you. I may even go one step further to say that this article provides very little proof at all, however the ideas are sound and it is an excellent premise for more statistical analysis.

]]>Interesting piece. Honestly, I don’t think it would really work with WPA for a few reasons. The first is just, most obviously, you’d have sample size issues, since your wOBA on the left side would be based on 4 or 5 plate appearances. Also, one of the main concerns with parametric methods is the need to rely on a particular set of assumptions about the distribution. I’d frankly be shocked if the error in a model like that were normally distributed. Not to say parametric methods are bad–they’re very useful. But it’s a weaker argument when assumptions don’t apply.

It’s also worth noting that I’ve tried some variation on this in the past (it was a while ago, so I don’t remember exactly, but I think I did a logit model on walks, based on like 20 previous PA’s, and didn’t find anything there. If you have the time and the interest, I would still encourage you to go ahead with it, or at least to keep tinkering. You never know what you might find.

]]>I saw it when it was put up about a while back, not sure if it’s been updated…

]]>I looked at a somewhat similar topic one day: the consistency of players. If you wanted to take a look here’s the link: http://theicebat.wordpress.com/2010/11/13/how-consistent-are-baseball-players/

Instead of taking differences between a player’s seven day length and his seasonal mean for a metric, I used a time series model approach. I think this would be interesting using wOBA and trying to predict a player’s next day wOBA based on his past seven days. What’s nice about time series would be the lag (of seven days in your case) determined parametrically. What do you think?

]]>I haven’t, but that’s a good idea. Unfortunately, I don’t have any data on DL time, nor do I know where to get it. I could definitely see that being an explanation for underlying streakiness. If you have any idea where I might get something like that, I can try to run it.

]]>First of all, thank you for the interesting study.

I seem to be a bit late to the discussion, but if you’re still around … have you looked into any correlation between streakiness and DL-time? My first thought upon seeing the skew of your last histogram was that it could be due in part to playing through/after injuries; this could also help account for the weak negative correlation with PA.

Thanks

]]>Thanks again.

-Seth

]]>I should also add that I’m glad you brought up Damon’s relationship problems. I think that kind of thing would be a big part of what drives the streakiness we observe. Such off-field events occur at times that are, from a baseball standpoint, completely random, and may well be part of why there’s no identifiable individual streakiness–since we can’t control for off-field noise.

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