Yes, we all know that batting average, as a value stat, is a terrible indicator of a player’s worth, but I think it’s still one of the first things that we glance at when we look at a player’s Baseball Reference or Fangraphs page.  One thing that makes BA fun is its day-to-day fluctuations and how that influences the season’s-end BA.  No season’s-end .312 hitter hit .312 every single day.  It’s a random distribution of 0/3 days and 4/4 days and 2/5 days that creates a .312 hitter. That’s what I wanted to take a look at.

For today’s graph, I took Pujols’ individual day BA’s from his Game Log over at Sports Illustrated and his season-to-date BA from his Batting Game Log at Baseball Reference, and constructed a simple line graph in Excel.
Warning: This picture is biggggggg.  It’ll stretch the screen a little bit, and I apologize, but there’s a lot of data

Click to enlarge

What does this tell us?  Well… not a whole lot.  The scale of the Y-axis is something I fought myself for awhile on; While I wanted to capture the entire spread of the individual day BA (So, from 0.00 to 1.00), that minimizes the visual impact on how much the season-to-date BA fluctuates.  For example, during a two week stretch in late July, Pujols’ batting average fell from .310 to .295, which is pretty significant.  The graph shows it only as a slight, innocuous drop.

Just looking at the graph, and knowing Albert as a hitter, I’m guessing that Albert’s BA stabilizes more quickly than other hitters.  Anecdotally, Colby Rasmus seems like a player who has rather large fluctuations in BA in relatively short periods of time, so I’ll be sure to look at him in a future post and compare him to Albert.  For now, though, enjoy this graph!

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