Getting to Know Fly Ball Pull Percentage (FB Pull%)

After two days discussing individual players with apparent upside and downside given their fly ball pull percentages (FB Pull%), it’s time to really get to know the metric. Let’s begin by looking at the leaguewide trend over the last 10 years.

League FB Pull% Trend
Season Pull%
2008 25.4%
2009 25.3%
2010 24.0%
2011 24.2%
2012 23.5%
2013 22.7%
2014 23.4%
2015 22.4%
2016 23.5%
2017 24.0%

In my Monday post, I only listed the last three seasons, which show an upward trend that matches with the fly ball revolution. However, when expanding this to the last 10 years, we see a far different picture emerge – no uptrend at all! In fact, it almost looks like an inverted bell curve if graphed, with the rate dipping in the middle years and then recovering. I’m not sure what’s behind these numbers, but we were actually higher in 2008 and 2009, declined and hit bottom in 2015, before jumping back up. So maybe there hasn’t really been any sort of shift in philosophy, as this looks more like randomness.

I then ran a year-to-year correlation on all hitters with at least 60 fly balls for two consecutive seasons from 2008 to 2017. My population set totaled 2,346 player seasons. The correlation was a meaningful 0.616, proving that pulling fly balls is indeed a repeatable skill.

But as with any skill, players change. Whether they hit the gym, the “light bulb goes off”, they tweak their swings, or any number of possible drivers of change, players experience gyrations in their FB Pull% all the time. So what happens after a player’s FB Pull% spikes or collapses from Year 1 to Year 2? I dove into the data to answer that very question. Here are the results:

Player FB Pull% Stickiness
Cohort Population % of Population Y1 Y2 Diff Y2 Y3 Diff
>= 10% 99 5.9% 18.9% 31.3% 12.4% 31.3% 25.0% -6.3%
> 0% & < 10% 722 43.2% 22.0% 25.9% 3.9% 25.9% 24.1% -1.8%
< 0% & > -10% 762 45.6% 25.8% 21.6% -4.2% 21.6% 23.5% 1.9%
<= -10% 89 5.3% 33.0% 20.5% -12.5% 20.5% 24.4% 3.9%

My population set was 1,672 hitters who knocked at least 60 fly balls for three consecutive seasons during the 2008 to 2017 period. I then lined them up so I had players in rows of three consecutive seasons, calculating the difference in FB Pull% from Year 1 to Year 2, and then again from Year 2 to Year 3.

I then divided them into cohorts based on the difference between Year 1 and Year 2. The first cohort was composed of hitters whose FB Pull% jumped by at least 10%. This doesn’t happen very frequently, as only 5.9% of my population fell into this group. Next was the groups surrounding no change in FB Pull%, those up to 10% growth and then those up to 10% decline. Last is the cohort filled with the hitters whose FB Pull% marks dropped by at least 10%.

Overall, the table paints an expected picture and reminds us that regression to the mean is king. Not the league average, mind you, but the player’s established mean.

The population averages in year one are interesting, as it confirms what we would have guessed — the guys lowest on the totem pole have the highest chances for upside, while the guys already atop the mountain have only one direction to travel.

Not surprisingly, the biggest gainers from one year to the next suffered the biggest declines the following season. However, these hitters still held onto half of their gains, still settling in just above the rest of the groups in year 3. On the other hand, the biggest decliners from year 1 to year 2 only rebounded about a third of the way back. So losses proved to be a bit more sticky than gains.

Perhaps most fascinating is that even given wildly different starting points in year 1, a total reversal of the FB Pull% leaders and laggards after year 2, everyone essentially meets back up right near the league average in year 3! It’s almost as if everyone was just bopping around for fun during the first two years and then got serious in year 3 and realized everyone actually has the same true talent FB Pull%.

So, as usual, don’t get too excited about big fly ball pull rate gains or precipitous declines. They all tend to revert right back toward the league average and at the very least, the player’s previously established norm. Instead, focus on buying players with better than average Brls/True FB and Avg FB Dist marks for your HR/FB rate shopping needs.



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Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.

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Brad Johnson
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The trough in the data coincides with around when pitchers suddenly shattered the previous norms for velocity. It takes time to adjust to everybody throwing 4 mph harder.