As you all know by now, the batted ball distance leaderboards and individual player pages at BaseballHeatMaps.com is one of my new favorite toys. I’ve sliced and diced the data in many ways in various attempts to identify breakout and bust candidates. Since about two-thirds of the season is now in the books, the time seemed to be right to compare distances to last year.
Chris Davis owners are experiencing an unexpected surprise this season. Instead of a possible corner infield or injury replacement, they have one of the top players in the league for pennies on the dollar. On the other end of the spectrum is B.J. Upton who Ron Shandler picked to have “40/40 upside”, but is in the discussion has the worst everyday player in the majors. Going into the 2013 season, Davis and Upton were projected to have similar production because of a uncertain skill set and the similarities ended then.
It is hard enough to compile a list of breakout candidates in the preseason. Usually, these lists are pretty cookie-cutter and include a swath of young hitters supposed to be on the upswing. Gee, you’re predicting a young guy to get better and perhaps breakout? Throw enough darts at the youngins and some will turn out correct. But projecting a hitter to break out over the remaining 2 1/2 months of the season is even more difficult. How much should we weight the first 3 1/2 months versus the hitter’s career? Is the hitter hiding an injury if he has been a disappointment so far? Has age caught up to him? As usual, all we could do is analyze the various metrics we have at our disposal and make educated, higher percentage play guesses.
Yesterday, I took a look at the hitters whose BABIP marks most exceed their xBABIP marks. Aside from a looming batting average decline, a lower rate of hits on balls in play could take a bite out of a hitter’s RBI and run totals, which all combined could really take a toll on his rest of season fantasy value. Today I check in on the opposite side of the coin, those hitters whose BABIP marks are most below their xBABIP marks. These could potentially be your group of buy low candidates.
At the end of April, I used Jeff Zimmerman’s updated xBABIP spreadsheet to determine which players’ BABIPs exceeded their xBABIPs by the greatest amount. Of the five players listed in the initial look, all of them did indeed experience a decline in BABIP, nearly all being a significant falloff. Of course, four of the five were sporting .400+ BABIP marks, so of course even without a formula, you would assume regression was coming. We now sit a bit further along than the exact mid-season mark, so BABIPs are much more reasonable at this point. These are the 10 hitters whose BABIP marks exceed their xBABIP marks by the greatest degree.
In Mid-May, I cherry picked some of the surprising batted ball distance leaders and laggards. I focused mainly on the top and bottom range of distances while ignoring the middle. Now, I’m going to look throughout the leaderboard and identify players who have averaged a distance that validates their season or seems out of whack.
Expected ERA metrics like SIERA, FIP and xFIP are one of the best sabermetric developments we have benefited from in recent years. No longer do we have to eye the various luck rates and attempt to make mental calculations as to how lucky or unlucky a pitcher has been. Expected ERA metrics do the work for you and spit out a number on the same scale as ERA so you could quickly identify those pitchers benefiting from good fortune and those who have not.
Yesterday, I unveiled the best pitcher expected walk percentage equation yet. By simply looking at the percentage of total pitches that are thrown for a strike and the rate at which the strikes thrown are put into play, we can get a pretty good idea of what a pitcher’s walk percentage should be. I was literally in the middle of typing up today’s post putting the equation to work on 2013 data to get an idea of which pitchers should have a higher or lower BB% when another light bulb went off.
About a month ago, I shared the pitcher expected K% regression equation I came up with that does a great job of estimating what a pitcher’s strikeout percentage should be given the combination of his called, swinging and foul strikes. Since then, I have wanted to do the same for a pitcher’s walk percentage. Unfortunately, there have been many attempts in the past, but with much less success than for a pitcher’s expected strikeout percentage. I believe the highest R-squared attained has been in the low 0.50 range, which isn’t bad, but clearly suggests that there’s a lot more going on that isn’t being accounted for.
Yesterday, I identified which starting pitchers may be due for an increase in walk percentage given their pedestrian F-Strike% marks. Although not as good a proxy as SwStk% is for K%, F-Strike% is probably the best singular metric for a quick look at how good a pitcher’s control is. Using the same data set as yesterday, these are the pitchers who have posted walk percentages well above what their F-Strike% suggest.
