2016 MLB Leaderboard: Pop up %

If you’ve read just about any of my posts about pitching, you probably saw me cite IFFB% (infield fly ball percentage) at least once. This, of course, is a measure of the number of balls hit in the air that never left the infield. However, this is an imperfect stat on its own. We can make it better by doing one mathematical operation and turning it into PU%. No, that’s not a measure of a player’s odor.

Dad jokes aside, PU% is pop-up percentage and is better than IFFB% because unlike IFFB%, PU% is expressed as a percent of all balls in play while IFFB% is only as a percent of fly balls. For example, a pitcher might have a very high IFFB%, but is a ground ball pitcher and doesn’t allow many fly balls. Thus, his high IFFB% doesn’t translate to many outs and we are most concerned with how a pitcher generates outs. You see, pop ups are almost guaranteed outs and can be considered almost equal with strikeouts.

So, why do I often use IFFB%? Because Fangraphs (my main data source) readily supplies IFFB% data but does not provide PU%. It requires that additional step of analysis. The math involved is pretty simple. PU% = IFFB% x FB%. It’s as simple as that. Regardless, it can be hard to find leaderboards with this stat in them so you can identify the league leaders and perhaps uncover some pitchers that make up for mediocre strikeout rates with elite PU%.

Today I’m going to show the PU% leaders for 2016. Hopefully this will be useful in preparation for 2017. The table will be limited to only the top 100 and only starters with more than 40 innings pitched, to keep it simple.

After that table, I put together a table where K% and PU% are added together. This shows a starter’s “easy out” percentage, which are outs that the pitcher doesn’t really have to rely on his defense for. The pitcher is basically in complete control of these outs. Thus, it is a measure of pitcher dominance over hitters and is likely to be very sustainable.

The top pitchers in this metric should be reliable fantasy options from start to start and less dependent on BABIP and luck. Further, this stat rewards starters that might be missed by other measures of value like xFIP, FIP, K%-BB%, etc. This is especially true for fly-ball pitchers that work high in the zone with their four-seam, like Drew Smyly or Marco Estrada. I hope you find both of these leaderboards to be useful when preparing for 2017.

Without further ado, here are the two tables I promised:

I will let you all ruminate on these leaderboards for a little while. In my next post, I will dig more into these tables and see if I can identify some sleepers. Tschus!